Deception Detection in the Courtroom

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PHI 306 - Applied Ethics

January 6, 2019

Word Count: 14,301

Arizona State University

Deception Detection in the Courtroom

Introduction

The neuroimaging technologies have brought major changes to the society we live in

today. In the past, detecting lies, and diagnosing certain diseases was such a taunting task but

today, it has become easier due to emergence of neuroscience technologies. The neuroscience

technologies encompass the sue of brain imaging methods that allows scientists to read the brain

of a person and understand which parts of it took part in accomplishing certain tasks. These

technologies have led to the emergence of the use of deception detectors has become the order of

the day. Companies have seen the need to determine whether their employees are being the

truthful or are being deceptive. With the praise that these technologies have received from the

media and the neuroscience field, many believe that its use will soon be in the courtroom in most

parts of the world. This is likely to bring a lot of change to the justice system because depending

on its accuracy, people will be hard to deceive the court and it is ore likely that more people will

receive justice in the process. Therefore, with the impacts of these technologies and especially

the use of lie detection technologies, it is important that the use of deception detectors is used in

the courtrooms. It will improve the credibility of the justice system m as well as accuracy of

information.

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The increased use neuroimaging in hal legal issues is just one example hpich shows how

society is shifting towards a data-driven way of deciding what is true. The development of

forensic neuroscience makes people think that the court is a place where even the very process of

cognition can be studied and verified. Şen et al. point out that the fusion of different data sources

- for instance, the analysis of facial expressions and the voice along with the brain activity - helps

to detect deception in a more accurate manner as it allows to access the complex and multi-

layered emotional and cognitive recations of the person (Şen et al. 309). This technological

intermixing not only leads to a higher level of technical precision but also questions different

epistemologies of truth in the law sphere. When brain data is shown as evidence, judges and

juries are faced with the task of interpreting the scientific information themselves instead of just

taking the human testimony at face value. This change, though it can lessen the number of

biases, still entails the danger of putting too much trust in neuroscientific authority when it

comes to legal reasoning. As Fornaciari and Poesio explain, the use of the automatic deception

detection system brings in the algorithmic mediation aspect to the moral judgment thus forcing

the courts to maintain a proper equilibrium between the technological objectivity and the human

interpretive discretion (Fornaciari and Poesio 315).

Standards of credible evidence must be rethought in order to evolve the methods of

analysis of neural deceit. Salmanowitz explains that under extremely tight circumstances,

neuroimaging can display some cognitive processing even when the individual is unaware of it

hence offering the insight which cannot be easily reached by simply observing the behavior

(Salmanowitz 142). However, this opportunity also triggers the conceptual clash of privacy,

autonomy, and transparency of evidence. Such methods being allowed by the court, it is doubtful

whether neural data ought to be considered as a voluntary testified statement or as a coerced one.

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It is not only the criminal law where the ethical implications are widely felt; the manner in which

the society imposes the boundaries of human self is also altered. Scarpazza and Sartori stress that

the existing deception studies cannot be distinguished between fake lies and neural responses

triggered by stress and, consequently, the means taken are extremely weak in terms of

interpretative aspect (Scarpazza and Sartori 478). This is what makes the appellation of a critical

attitude towards them important prior to submitting the neuroscientific truth in front of the court

of law.

Concurrently, deception neuroimaging has been heralded as a means of democratizing the

evaluation of the truth by lessening the influence of rhetoric, money, and social status on legal

outcomes. Sen et al. report that data-driven analysis of real-life trials indicates that the

combination of linguistic, physiological, and neural markers can be used to confirm the results to

a great extent even in a wide range of populations (Şen et al. 312). Such an inclusive accuracy

might be able to reverse systemic injustices of testimonial credibility for instance, in delimited

victims of the societal system. Yet, Fornaciari and Poesio warn that the training of cultural and

linguistic data-specific algorithmic models can result in the production of biases that pretend to

be objects of neutrality (Fornaciari and Poesio 332). These facts make us aware that technology

is not neutral; it incorporates social and ethical values depending on the way it has been

designed. Salmanowitz argues that only through the involvement of different transparent

disciplines can the use of neuroimaging in court be considered a responsible adaptation without,

however, losing the basic rights or increasing the injustices (146).

Traditional Lie Detection Methods

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The emergence of neuroimaging technologies has led to an increased need for the use of

lie detection methods in the courtrooms today. Today, there are various lie detection systems

that exist which include the polygraph among other methods that are used to determine whether

someone is telling the truth or if such person is deceptive. According to Wolpe et al., (2) these

lie detection technologies such as the polygraph tests are set in the determination of the

physiological correlations of human behavior through a set of parameters being measured which

include psychological and other parameters that are normally overlooked but are crucial in the

determination of deception. Therefore, given these explanations lie detection systems in a very

reliable way are able to provide a foundation through them determinants of behavior if someone

is telling the truth or is lying. Over the years, polygraphs have been overtaken by these

technologies that allow scientists to detect if someone is lying or not. However, Wolpe (30

asserts that even though new technologies have come today that are used to detect lies, the old-

fashioned lie detector polygraph is still considered are the most reliable system in getting the

truth out of a person. Other methods are also under considerations that use the AI systems which

look for more than just the physiological responses in a subject which could revolutionize the lie

detection technologies.

Polygraphs and other lie detection systems provide reliable information about a subject

by checking a number of things. Essentially, this system looks for the physiological changes in a

subject which include the change in blood pressure, the pulse rates, the expansion of the chest

and the skin’s electrical conductance during the question. Any abnormal changes in these

parameters during the questioning process indicates deception from the person which could help

in the process determining a person is telling the truth or he or she is not. The data that the

polygraph records during this process are useful in that it explains the autonomous nervous

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system’s activities which not only gives a reflection of the deception arousal of such questions

but also the anxiety that such questions arouse from the subject. Even though various critics have

come to refute the accuracy of such methods, there is enough data that shows that the deception

in a human being triggers a lot of neural responses among other physiological responses that

could help in determining whether a person is being deceptive or not.

Physiological measures have typically been relied on for lie detection, which has now led

to the psychological aspects of the problem being researched. According to Vrij, Fisher, and

Blank, deception consumes more brain power because lying, among other things, requires one to

invent fake information and at the same time keep track of inconsistencies (Vrij, Fisher, and

Blank 5). The fact that this puts a heavy load on the brain indicates that cognitive symptoms of

deception may even be the ones that most clearly differentiate it from the truth. However, this

has not stopped the polygraph from being still very much in demand as it seems to be providing

objective and numerical results. Meijer et al. warn that people may take such data too literally

because they fail to see the complexity of human cognition and hence they place too much trust

in the accuracy of the diagnosis (Meijer et al. 595). The focus on quantification that is kept alive

by the court systems at the same time may be obstructing the influence of subjective and

contextual factors. Investigation of Vicianova into the past discloses that the earliest lie detection

ceremonies also counted more on the relief that comes from the symbolic than on the empirical

(Vicianova 525). Consequently, traditional methods measure truth just as much through trust.

The link between belief and measurement which identifies deception detection not only as a

scientific but also as a sociocultural phenomenon is the very science of measurement that is put

forward here. Researchers, knowing this, have to reconsider their attitude towards polygraph

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results and realize that these are not only a technological achievement but also human

expectations.

As technology advanced, the polygraph has nowadays become one of the symbols of how

modern science has tried to quantify honesty. According to Meijer et al., a polygraph derives its

energy not on its consistency but on being the representation of technology and objectivity

(Meijer et al. 596). The legitimacy of decisions made by institutions is strengthened on the basis

of this power as a symbol which creates the illusion of being neutral and in control. On the one

hand, Polygraph results as Vrij, Fisher, and Blank point out, are highly influenced by the mental

condition of the individual one tests and his condition privy to the operation of the test (Vrij,

Fisher, and Blank 12). The above findings demonstrate that the subjective nature of test

interpretation has invaded the allegedly objective systems. In addition to that, Hauch et al. claim

that even the computers that treat the verbal and physiological cues are biased since the lie can

be different in each individual (Hauch et al. 312). The same shortcomings point to the classical

methods of lie detection being as subjective as it is objective. The fact that it exists continually is

a testimony to the human need to have the opportunity to verify moral uncertainty with material

evidence. The existence of the polygraph, then, is an indication of faith in measurement as

opposed to faith in undisputed truth. Knowing this paradox enables scholars to understand why

these technologies have not vanished, despite being called into question in more than one

instance when it comes to their validity.

History of detecting lies reveals the fact that the meaning of truth and evidence evolved

during the history of humanity. Vicianova notes that some of the earliest approaches to the

European continent e.g. trial by ordeal, pulse examination and many others were founded on

cultural preconceived notions of divine justice (Vicianova 523). Moral reasoning and not

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scientific reasoning underpinned these approaches. The twenty-first-century polygraphs

attempted to secularize this type of searching basing the truthfulness on physiological signs, in

their turn. But as Meijer et al. have claimed, the physiological arousal is not only affected by

anxieties and setting but it is also equally affected by real deception, i.e., heart rate, blood

pressure and skin conductance (Meijer et al. 594). This renders it hard to boast the objectivity. In

reference to Vrij, Fisher, and Blank, lie detecting ought therefore to be construed as a case

between thinking, situation and feeling (Vrij, Fisher, and Blank 8). Historical history of faith to

physiology does not present how ideology would be supplanted by science, but rather it

describes how the regimes of belief concerning what is comfortable truth are being altered. With

this development, the scholars can observe how the courtroom has become one of the new arenas

of this ancient search after credibility. The result is that there will be a continuing debate

between the ethical hope and the empirical validation.

The analysis of reaction-time has become a contemporary development of the old

polygraph reasoning. Suchotzki et al. indicate that people took more time to lie than to tell the

truth since lying required the not only blocking of veridical memories, but also making other,

false options (Suchotzki et al. 430). This hesitation is a sign of cognitive interference and nothing

more than moral weakness. These findings are explained by Vrij, Fisher, and Blank as the fact

that deception involves the use of higher-order executive processes, specifically, working

memory and inhibition control operations (Vrij, Fisher, and Blank 7). Meijer et al., however,

caution that the idea of the response time as a single indicator of dishonesty leads to the

possibility of confusing the law of being hesitant and being guilty (Meijer et al. 599). Response

times can also be prolonged by cultural communication styles, fear or fatigue. Therefore,

although the paradigms of reaction-time can be used to modernize the process of finding

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cognitive signatures, it maintains the core conflict that has existed between the polygraph:

associating physiological or behavioral abnormality with dishonesty. Additional interpretation is

provided by the introduction of temporality, which also creates behavioral complexity. The

bottom line though is that the timing of deception cannot be separated of the variability of

humanness.

The recent advances in computational linguistics have redefined the field of studies on

deception by introducing machine learning as a way of analyzing textual and verbal information.

Hauch et al. discovered that the alarm system of detecting deception utilized language cues such

as pronoun frequency, detail richness, and tone of emotion (Hauch et al. 309). These

characteristics are close approximations of psychological distancing techniques that lie tacklers

are unaware of. According to Conroy, Rubin, and Chen, algorithms that distinguish fake news

also apply similar linguistic markers to indicate the presence of insincerity in writing (Conroy,

Rubin, and Chen 2). These methods change the focus on the inner states to the field of

communicative behavior, increasing the lie detection. However, according to Meijer et al., these

models face the danger of confusing the difference in style with lies, especially when judging

between different cultures and languages (Meijer et al. 597). The computational practices are

therefore a reflection of the dilemma of the polygraph in that human variability subjectivity is

converted into quantifiable yet ethically questionable data. These tools are becoming more

sophisticated which poses new ethics and legal concerns regarding surveillance and expression.

With the changing technological trend, linguistic analysis can redefine deception as a digital

rhetoric.

Another frontier is affective computing in which physiological readings are combined

with emotional modelling. The IEEE Transactions on Affective Computing research makes it

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clear that facial expression, tone, and heart-rate data have higher predictive value in the process

of analyzing deception when combined (Affective Computing 308). These multimodal methods

aim to elicit emotional micro cues which indicate cognitive dissonance. Nonetheless, Lai, Chen,

and Chiang claim that interpretive uncertainty can not be eradicated by the fuzzy logic models,

as human affect is relative (Lai, Chen, and Chiang 176). Their results emphasize the fact that

probabilistic reasoning can, and not necessarily, refine truth assessment. The same is observed by

Meijer et al. who report that increasing modalities increases the ethical dilemmas especially in

the area of privacy and consent (Meijer et al. 598). Affective computing therefore carries on the

legacy of polygraph to the digital era: a promise of objectivity that has been marred by a long-

standing ambiguity concerning what exactly emotion reveals. It further hints that emotional

openness could be the new battleground on justice. The truth is subject to change, and is a

moving psychological scale as barriers between thought and feeling disappear.

In cognitive-based lie detection, the focus lies on mental load rather than on emotional

arousal and deception is redefined as a problem of information processing. According to Vrij,

Fisher and Blank, these interrogations aiming at heightening cognitive load like reversing the

sequence of the story can exaggerate differences between liars and truth-tellers (Vrij, Fisher, and

Blank 14). These manipulations show the interaction between effort and coherence to the test.

Suchotzki et al. also support the fact that increased cognitive strain is associated with

quantifiable delays on the deceptive responses (Suchotzki et al. 435). However, according to

Meijer et al. the mental load of the act of deceit also does not distribute evenly, but rather varies

depending on motivation, guilt, and practice (Meijer et al. 593). Such variability makes it

difficult to establish some fixed point of detection. The methods of cognition therefore proceed

with the development of traditional instruments on the conceptual level but are still restricted by

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the same interpretation problem the instability of deception to the situation of psychology.

Nevertheless, their dependence on quantifiable thought processes can provide useful information

concerning human reasoning. They prove that the very process of lying tells one about cognition

as much as it does about right and wrong.

Current fuzzy-reasoning models are an effort to harmonize biological ambiguity with

probabilistic logic. According to the authors Lai, Chen, and Chiang, their systems combine as

many as six uncertain indicators like voice stress and galvanic response to arrive at one single

deception probability score (Lai, Chen, and Chiang 173). Their method realizes that lying cannot

be simplified to a binary categorization. This probabilistic turn is a reflection of the conceptual

advancement from the ancient lie detection to the present one which concentrates on contextual

credibility instead of absolute truth. However, Meijer et al. in a very different tone find that the

act of giving numerical confidence to moral judgments may lead to the risk of their ethical

oversimplification (Meijer et al. 599). Data may be seemingly accurate while at the same time

hiding subjective weighting decisions made by the designers. According to Vrij, Fisher, and

Blank, human interpretation cannot be replaced by reasoning models since the latter lack cultural

and emotional sensitivity (Vrij, Fisher, and Blank 16). In this way, fuzzy systems stand for an

intricate but morally challenging transition to the polygraphic logic that has been resolved. They

tell of a deepening awareness that uncertainty has to be measured itself. This acknowledgment

might, in fact, lead to the redefinition of the standards of evidence in the justice system.

Cross-cultural psychology uncovers the fact that cues of deception differ broadly in

different societies disproving the presumptions of the uniformity of polygraph testing. Hauch et

al. observe that verbal markers of lying such as reduced number of self-references or emotional

words vary across different cultural communication standards (Hauch et al. 314). This

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observation contravenes the universal nature of linguistic-based models. Conroy, Rubin, and

Chen also identify that non-Western languages that train algorithms based on the Western data

sets improperly recognize the truth in non-Western languages as lies. Conroy, Rubin, and Chen

(3) state that these mistakes serve to illustrate the existing cultural bias in automated detectors.

According to Meijer et al., in order to make ethical deployment operational, it is necessary to

adjust to linguistic diversity and socioemotional finesse (Meijer et al. 596). Although these

observations show that the aim of traditional methods to lie detection obscures a deep relativism,

the seeking of universality is really just a narrative of culture rather than a physiological

occurrence. Allowing such differences can help to adopt more equitable international uses of

forensic technology. Credibility will depend on whether one is ethically tuned not on technical

progress.

Although there are methodological changes, the traditional lie detection still contains a

philosophical paradox: it considers the honesty not as a specific relational phenomenon, but as a

quantifiable material. Meijer et al. argue that not even physiological measures or cognitive

measurements can see deception in a direct way, but it can only see it indirectly (Meijer et al.

593). This epistemic disjunction remains in the most advanced technologies. Vrij, Fisher, and

Blank put us to mind that deception is inherent in context in that even the truth itself is created

by a speaker and a hearer (Vrij, Fisher, and Blank 18). Equally, the history of Vicanova

demonstrates that the process of detection of lies has always been a show of a belief in systems,

rather than an empirical evidence (Vicanova 528). The acknowledgement of this continuity

redefines the major question of the field: not how to get science and lies to be perfectly synched,

but how societies make belief based on science. The difficulty of contemporary scholars lies in

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strikes between technical specificity and ethics. It is at this point that truth can only be meant to

be measurable and meaningful.

Modern Brain Imaging Techniques

The other ways that have come into practice in recent years are the use of brain imaging

technologies. Brain imaging technologies have been recognized as some of the reliable ways of

reading deception from a person because it allows the expert to read the brain and access

information. One of such technologies is the functional magnetic imaging fMRI which allows the

scientists to measure the changes in the blood oxygenation when the brain performs a given task.

Through this process mapping the behavior of a subject becomes possible (Tovio 193).

Phrenology is another brain imaging technology that could allow scientists to read the human

brain. Through the imaging it is possible to read the activities of the brain and acquire

information that a subject may not communicate to ensure that he or she is not deceptive. These

technologies have been used over the years to access information from people to ensure that they

are telling the truth.

Functional magnetic resonance imaging (fMRI) has changed the way neuroscience

understands the concepts of truth, intention, and deception by showing in a very direct manner

the changes in blood oxygenation in the different areas of the brain involved. According to

Orrison et al., fMRI reveals dynamic activation patterns associated with cognitive processes such

as memory retrieval and moral reasoning, thus it becomes a key instrument in deception research

(Orrison et al. 112). This kind of visual precision brings the previously intangible psychological

constructs down to measurable spatial data. However, such data should be interpreted with

caution because neural activation does not necessarily mean specific mental states. Castillo

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points out that the history of imaging has been an evolution from simply showing the anatomy to

functionally interpreting it, thus the distinction between observation and inference becoming less

clear. According to Castillo (S114), by converting the idea into an image, researchers are at the

risk of pushing the correlation to causation further than it actually is. Still, the power to watch

neurons talking to each other as it happens is both a gift of great insight for the courts and a

source of great excitement for researchers. The concomitant capacity to illumine and to confound

is what makes neuroimaging such an intriguing, albeit problematic, epistemic tool.

Diffusion-weighted imaging (DWI) deepens brain analysis by revealing the

microstructural movement of water molecules within tissue. Baliyan et al. refer to DWI as a

method that can localize tiny changes in white matter that incipient changes are not visible in

standard MRI scans (Baliyan et al. 787). Due to its high sensitivity, it is an essential tool to study

those brain areas which are impugned in lying, namely, the prefrontal cortex and anterior

cingulate. Nevertheless, these patterns should not be hastily construed as deception markers.

Wintermark et al. argue that DWI help locate the structural damage in traumatic brain injury

while its use for identification of cognitive states is still a matter of research (Wintermark et al.

e4). Innovation as well as risk is involved in making the transition from pathology to

psychology. Wintermark et al. (e4) express the view that in using diffusion data to deduce

honesty, researchers are pushing a clinical tool in the area of morality. The change here is an

example of how progress in imaging tech can often bring in ethical questions regarding the limits

of the interpretative process.

Photoacoustic imaging, a hybrid approach that leverages the best of both optical and

ultrasonic signals, has been a game-changer in the way we can now look at brain functions. Yao

and Wang highlight that the technique allows seeing at various levels from tiny to large,

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including both hemodynamic and metabolic changes, with very high accuracy (Yao and Wang

011003). Being a multidimensional technique, it has the potential to be the only one capable of

picturing the extremely fast neural changes that happen during cognitive effort. Nevertheless, the

use of photoacoustics in lying research brings in both, technical and philosophical, issues.

Mabray, Barajas, and Cha point out that even in well-controlled clinical environments, the

process of understanding variations in signals is very demanding in terms of precise calibration

and requires a great deal of support from the context to make sense of it (Mabray, Barajas, and

Cha 10). A mistaken interpretation of these signals might result in a false identification of the

source of the intention or in the wrong detection of the awareness. In spite of such constraints,

the introduction of photoacoustic imaging is a metaphor of the neuroimaging community's

resolve to achieve profound and unified views of consciousness. The progression of the

technique indicates that the coming period of the lying detection might not be single imaging

modalities but rather hybrid frameworks that connect biological and cognitive data.

The ethical issues surrounding the use of neuroimaging in deception-related studies are

essentially dependent on the increasing diagnostic power of such a method. Rizvi, Batchala, and

Mukherjee point out that the imaging utilized to identify brain death should comply with very

rigorous clinical and ethical criteria due to its legal consequences (Rizvi, Batchala, and

Mukherjee 312). Their argument is a warning example for lying detection, where results might

have a similar influence on judicial outcomes. It is somewhat unsettling to think about using

methods created for decisions between life and death in medicine for deciding truthfulness in

cases consent and interpretation becoming questionable. According to Wintermark et al.,

imaging evidence, though very convincing, is beyond the knowledge of non-experts who are

interpreting it (Wintermark et al. e3). Their statement underlines the possibility of the wrong use

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of such evidence in law fields. The takeaway from medical diagnostic imaging is that despite

high technological precision, it does not automatically ensure ethical or epistemological clarity.

In the case of lie detection, it is a sign that the responsibility has to change correspondingly with

the capability and thus, keeping the innovative method as a tool for justice rather than its enemy.

Current neurofeedback methods significantly change brain imaging from mere passive

observation to an active form of control by the individual. According to Thibault, Lifshitz, and

Raz, neurofeedback allows people to see and change their brain functioning in real time, thus

making a tool out of perception for self-control (Thibault, Lifshitz, and Raz 250). The power to

self-regulate confuses the differences between observation and intervention. In case

neurofeedback is utilized in deception studies, it may cause liars to have the ability to

intentionally alter their brain activity, thereby challenging the idea of involuntary truth signals.

Orrison et al. argue that even though functional imaging is a very powerful tool, it can still be

affected by a person's voluntary modulation and habituation (Orrison et al. 115). The mere

possibility of cognitive countermeasures being employed makes neural evidence even more

trustworthy. Still, this specific problem exemplifies the human mind's power to get around it.

According to Thibault, Lifshitz, and Raz (250), neurofeedback, by making the brain both the

object and the means of control, uncovers the intricate relationship between the mind,

technology, and moral agency in modern neuroscience.

At first, advanced radiological methods that were developed for oncology are influencing

deception detection research by their focus on precision and structural mapping. Ajithkumar et

al. explain that the innovations in imaging for radiotherapy, as in the case of intensity-modulated

approaches, lessen the cognitive side effects because the neural regions are targeted with higher

accuracy. Ajithkumar et al. (e94) emphasize that the same idea, a lie detection method with

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minimum collateral interpretation, hence isolating the relevant brain activity for increased

validity, is borrowed from the radiotherapy field. In addition to that, Belsuzarri, Sangenis, and

Araujo state that intraoperative imaging decision-making becomes more effective when real-time

feedback is integrated into the surgical plan (Belsuzarri, Sangenis, and Araujo 72). This

integration is a reflection of the deception research goal to synchronize data acquisition with the

immediate analysis. On the other hand, Mabray, Barajas, and Cha point out that even with the

most accurate imaging, the brain’s interpretive flexibility is beyond the reach of the imaging (9).

Mabray, Barajas, and Cha (9) say, if we combine these revelations, it indicates that the power of

imaging is not just in the representation, but in the ongoing, context-aware, sensitive adjustment,

which is a defining feature of morally correct forensic application.

Looking back, the history of imaging reveals that it has always been a blend of both

revelation and projection. Castillo describes the progression of brain imaging from very basic

anatomical sketches to the complex and multidimensional radiologic interpretation, which in turn

has changed the way scientists visualize mental phenomena. In his view, the change is not far

from the human journey of externalizing one’s thought, of making the invisible visible (Castillo

S112). According to Belsuzarri, Sangenis, and Araujo, the use of intraoperative imaging is a

perfect example of this change, where it is not only observation but also manipulation that is

involved (Belsuzarri, Sangenis, and Araujo 74). This union of ideas, however, carries a risk of

knowledge: by becoming more interactive, imaging might on the quiet side influence the

phenomena it intends to observe. Rizvi, Batchala, and Mukherjee talk about similar issues that

arise in the determination of brain death, where the interpretation acts as a bridge between

biological facts and ethical judgement (Rizvi, Batchala, and Mukherjee 314). These instances

serve as a warning to scholars that every imaging method comes with embedded philosophical

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ideas about the mind and matter. Contemporary deception detection continues this inheritance,

its images being not only the reflections but also the negotiated representations of belief.

New hybrid systems on the horizon comprehend the unified analytical frameworks from

the integration of fMRI, DWI, and EEG data, which are capable of mapping deception across

both temporal and spatial scales. Mabray, Barajas, and Cha explain the way in which multimodal

fusion deepens the diagnostic trust by correlating the structural and functional aspects (Mabray,

Barajas, and Cha 11). In the case of lying, such a combination could identify differences between

knowingly making up a story and an automatic reaction from the subconscious. Yao and Wang

point out that cross-modality imaging temporal resolution can be enhanced without any impact

on spatial fidelity (Yao and Wang 011003). This improvement gives researchers the opportunity

to follow brain activity as it happens, thereby becoming close to the temporal flow of the very

thought. Still, Wintermark et al. caution that the merging of technologies may result in increased

errors of interpretation if the analytical models used are not transparent (Wintermark et al. e2).

Hence, the shift to hybridization is not only a sign of epistemic aspiration but also of

methodological humility, i.e., the acknowledgment that truth is intricate, consisting of different

layers, and changes with time.

The growing use of modern brain imaging in the legal and psychological fields is one of

the major changes that are happening in the way knowledge is acquired visually - the belief that

seeing is knowing. Functional imaging's persuasive power, according to Orrison et al., is due to

its apparent transparency by providing to the jury "pictures of the mind" which look

incontrovertible (Orrison et al. 119). However, Castillo claims that radiological images are

complex in nature as they depend on the color mapping, thresholds, and narrative framing

(Castillo S115). These aesthetic aspects make it difficult for the image to have the full power of a

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legal proof. Thibault, Lifshitz, and Raz point out that the fascination with brain visualization may

result in the idea of "neurorealism," in which visual data are taken as an objective reality.

Thibault, Lifshitz, and Raz 252) state that the argument of the authors applies to the field of

research in deception, which may inadvertently give more weight to the factor of visibility than

that of validity. In recognizing the rhetorical power of imaging, one is not only more critical but

also open to different disciplines. Real understanding is not the image, but the interaction of the

science, ethics, and interpretation involved.

Ethical and Reliability Concerns

The ethical struggle between validity and reliability is at the core of the assessment of

modern deception detection technologies. The issue of debate is most notably pointed in legal

and neuroscientific research, where the precision of data is often at odds with the moral

interpretation of it. Fendler suggests that if one aims at reliability, measurement that is

consistent, one usually ends up compromising validity, that is the authenticity of the actual thing

being measured (Fendler 215). This problem has an immediate relation to brain imaging,

meaning that the neural patterns that are stable may be taken as the ones that show truthfulness.

Courts, when they give more value to consistent results than to interpretive nuances, are actually

acting at the risk of making tools that yield ethically deceptive but predictable results, appear as

if they are legitimate. The paper by Markovina et al. states that research across different

countries in terms of human behavior can be used to show that reliability does not necessarily

lead to conceptual equivalence (Markovina et al. 28). The authors suggest in their publication

that the cues to deception might differ from one culture to another, thus falsifying the universal

correctness of the detection method. In order to understand this point thoroughly one should also

see how reliability is turned into a moral demand rather than just a technical standard. Ryan

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argues that in artificial intelligence systems an excessive trust in the algorithmic consistency can

create a situation where trust is misplaced, hence turning reliability into a moral substitute for

truth (Ryan 2752). By realizing this trade-off, one discloses that ethical scientific practice calls

for not only stable data but also interpretive humility, meaning the acknowledgment that on some

occasions without context one can be as likely to be serving justice as to be failing it.

Human interpretation is one more factor that complicates deception science.

Neuroscientific technologies rely on the skill assessment, and this has led to an inevitable

interpretive dimension that is added to the purportedly objective systems. According to Alsaawi,

qualitative research relies on the capacity of the researcher to traverse ambiguity, bias, and

subjectivity in the interpretation of the data (Alsaawi 76). In the same manner, scientists who

examine the brains scan have to decode the neural patterns based on subjective structures that are

determined by training and expectation. Mohammadi et al. discovered that in organized medical

ethics questionnaires, personal beliefs and institutional norms had a serious influence on the

scores of reliability (Mohammadi et al. 3). Their results are similar to the issues in forensic

neuroimaging, where the judgment of assessors can be partially biased. This acknowledgment of

subjectivity aids in explaining the need of reliability to incorporate reflexive awareness as a

methodological protection. It is important to note that according to Fendler, ethical reliability

should be sensitive to contextual diversification as opposed to imposing strict norms of

homogeneity (Fendler 223). It implies that ethical integrity is neither in the removal of

subjectivity, but its recognition in open relation to it. Deception detection technologies must thus

be accommodating to interpretive pluralism whereby various disciplinary points of view can co-

exist. This is an effective method of assuring ethical fidelity since human variability is positioned

to appear as enlightenment as opposed to incorrectness.

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Each new level of ethical responsibility can be added to the assessment of reliability due

to artificial intelligence and automation. Machine learning systems are highly likely to hide the

ethical implication that accompanies their construction. Ryan points out that the society is

becoming overly perplexed as technical accuracy and moral trustworthiness are becoming

interchangeable and he describes this process as epistemic automation (Ryan 2751). The problem

with this tendency arises in situations where algorithms are used on the context of human

judgment, e.g., truth verification. Raji et al. demonstrate that even the facial recognition system,

which is set to audit bias, still recreates the discriminative trends of the training data (Raji et al.

4). These results reveal that social inequity can be supported by computational reliability. It is

important to realize that algorithmic ethics are yet to be determined by human judgment, though

before doing this, one needs to address this problem. The same author mentions that to prevent

implicit bias in research conducted by humans, it is necessary to implement methodological

transparency (Alsaawi 78). This commonality of human and algorithmic interpretation increases

the ethical stakes: there is not only the technical mistake but, also, the moral one. This will

ensure that in deception detection reliability is not judged by the effect on the statistical

reproducibility only but also by the social accountability. Ethical reliability, in turn, turns out to

be a multidimensional construct, comprising of technical rigor and reflexive awareness.

It must also be ethically responsible such that sensitivity to the context of data collection

is taken. The emergence of technologies that are focused on surveillance has escalated the

discussion of consent and control issues. Chung, Demiris, and Thompson believe that

technologies employed to track vulnerable groups of people, including smart home systems that

help protect older adults, pose severe questions about consent and autonomy (Chung, Demiris,

and Thompson 159). In their work, they show that ethical design should not be technologically

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oriented but rather people oriented in terms of human dignity. Likewise, detection of deception

within the legal context is liable to violate cognitive privacy as members of the society are

profiled without their full knowledge of it. Fendler asserts that procedure-based ethical

legitimacy is based on respecting the complexity of human beings and not lowering it to

measurable indicators (Fendler 224). Mohammadi et al. prove that when trust is developed

among the participants, the accuracy of ethical judgments in medical research increases

(Mohammadi et al. 4). The recognition of such a parallel is critical in the acknowledgment of the

system of fairness which is as much of relationship as measurement. To use analogy,

neuroimaging evidence can be more morally reliable through having the respect of agency and

informed consent. It is because ethical practice not only relies on the measurement of data, but

also on the experiences of the subjects who are measured.

The use of ethical technologies in a commercialized manner makes the definition and

delivery of reliability more difficult. There is an inclination of the public to believe new systems

of science basing on narrative frameworks not based on empirical support. Cerri, Testa, and

Rizzi demonstrate that perceived integrity in ethical products is usually an important factor in

influencing consumer trust compared to verifiable certification of the products (Cerri, Testa, and

Rizzi 348). This observation can be applied to how neuroscience and AI are viewed by the

general public with regard to legal matters where confidence replaces understanding. Ryan

cautions that this kind of misdirected faith may reduce the ethical control to labelling as opposed

to responsibility (Ryan 2755). To evaluate this risk, it is necessary to consider the fact that the

marketing language reacts to moral vision. When dealing with deception detection, scientific

reliability rhetoric can help cover-up marketing policies exaggerating technological infallibility.

Markovina et al. claim that to prevent inaccurate generalizations, the use of cross-national

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reliability requires cultural and linguistic adaptation of the research (Markovina et al. 29). In the

same manner, a claim of reliability should be covered with transparency on limitations. Ethics,

then, should not only be confined to the methodology, but also be extended to communication- in

which reliability may be deployed to the benefit of knowledge and not to commercial appeal.

Reciprocity of emotion, ethics and reliability discloses some concealed nature of human

judgment that cannot be reflected in quantitative models. The effect of emotional cognition on

the evaluation of truth in scientific and moral standpoint is working. Cerri, Testa, and Rizzi

demonstrate that moral concern is harsh in individuals, capable of arguing with logical appeal

and placing more stress on emotional appeal, instead of on the consistency of facts (Cerri, Testa,

and Rizzi 346). This can be also applied in courtroom practice where the implication of this is

that the most reliable data may not necessarily prove to be convincing when clashing with the

moral instincts of jurors. According to Alsaawi, the qualitative interpretation in and of itself is

dialogical in nature-that is, it is associated with empathy and a contextual sensitivity in contrast

to repetition (Alsaawi 80). This argument implements the fact that reliability needs to comply as

well as to suppress human variability. On the phase of the end of ethical models one should be

incisive of lacking a firm of connection between the emotion cognition and epistemic humility.

Another argument put forward by Fendler is that the inclusivity and reflective dialogue, as a

compromise between accuracy in science and pluralism in moral dialogue, ought to be measured

in terms of ethical reliability (Fendler 225). Following this methodology in the event of

deception detection, reliability will still be an ethical practice that is constantly being worked

upon rather than a technical measure.

The issue of cultural relativism also has to be overcome in reliability tests in deception

technologies. Research on social sciences always comes up with revelations that the cues of

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morality and behaviors are highly contextual. Markovina et al. underline that the reliability of the

survey across nine European countries necessitated the manipulation of the survey frames to

accommodate the local meanings of behavioral norms (Markovina et al. 31). On the same note,

cues and neural reactions involved in lying might vary markedly, depending on the cultural

settings. According to Mohammadi et al. the culturally adaptive approach enhances ethical

validity by matching testing processes with the moral expectations of the participants

(Mohammadi et al. 6). Such flexibility requires humility in scientific institutions which purport

to be universal. According to Ryan, global AI ethics has a tendency to spread Western concept

on fairness, which strengthens the epistemic inequity in the name of reliability (Ryan 2758). The

use of cross-cultural calibration should hence be an ethical requirement in deception detection

technologies. Dependency disinterred of diversity is no more than a shallow form of universality-

-technologically consistent and ethically incomplete. Incorporation of cultural pluralism is a way

of putting back moral balance because the truth too is relative.

It is ethical reliability that needs to be reconceptualized of what it means to trust

technology. In this regard, trust is a social contract as well as a moral choice. Raji et al. posit that

ethical audits need not be disjointed as per their continuous operation, as they lose effectiveness

with time as the systems change as quickly as oversight processes (Raji et al. 6). Their review

indicates the significance of accountability circles between technical updates and ethical review.

Ryan also supports the concept of trustworthy AI and points out that to achieve reliability, the

systems should be transparent, interpretable, and human-centered (Ryan 2753). Assuming the

validity of this concept to deception detection before us, we must recognize that, trust is enabling

and blinding at the same time. According to Fendler, measurement requires ethical evaluation

that is recursive i.e. continually challenging the premises on which the measurement is organized

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(Fendler 227). This reflexivity changes the existing reliability as a fixed feature to a continuous

moral obligation. When put to neuroimaging, this kind of approach makes truth-seeking

technologies develop in a responsible manner. Ethical science cannot possibly prosper in the

future without systems that are ready to question their authority.

The deception detection technology concerns

There are various concerns about the use of neuroimaging and lie detection technologies

since psychologists and psychiatrists are not on the same page regarding its use. One of the

concerns that have become evident is the issue of reliability. According to Wolpe, et al., (4) the

ma concern in regard to the use of deception detection systems such as polygraphs and fMRI I

the judicial and the civil settings has been on its accuracy in measuring the level of deception and

determining whether a statement is true or false. The other thing that Wolpe, et al., (4) asserts as

an issue of concern is the reliability of the technology’s questioning paradigm and the issue of

relevance that the technologies have in the feeds they are being used. Many people argue that the

use of polygraphs is not accurate in determining whether someone is deceptive or not. This is

based on the reason that sometimes, there are inconclusive results from such systems which

leaves a lot of doubt in the minds of those who needed answers. The questions used in the

process and the inconclusiveness leads to the reliability issues since it cannot be easy to rely on

something that gives inconclusive results. The other concern is the other feds such as the

criminal laws have varies adjustments that could influence the process of making decisions.

Sometimes criminals are given a chance to explain the reason for their misconduct that could

change the decisions and it is believed that such adjustments could make the use of polygraphs

less useful.

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The nature of the bone-toss controversy about the deception detecting machines tells of

the continuous clash between the scientific desire and the ethical limitation. Scarpazza and

Sartori stress the fact that even after decades of studies, there is not a conceptually consistent

accuracy or interpretation of lie detection and there exists a variety of approaches to the question

(Scarpazza and Sartori 478). This fact of disagreement invalidates the use of such tools in the

court where there is a high moral stakes. Fornaciari and Poesio also contend that in a court

setting automated deception systems can be wrong in interpreting the linguistic nuances of irony,

ambiguity, or culture (Fornaciari and Poesio 324). These difficulties demonstrate that reliability

is not to be introduced beyond the interpretive complexity of language and human emotion.

Although Sen et al. discovered that even the multimodal systems of voice, facial and

physiological data generated significant differences when subjected to recorded trial of real-life

trials (Sen et al. 310). These test results depict courtroom reality that interferes with laboratory

accuracy. The ambiguity they express gives some reason to believe that the truth in the state of

law discourse is not algebraically determinable--it is required to be a matter of human judgment

and conscience.

There is a constant clash between accuracy and fairness as one of the main ethical

problems in deception detection. Salmanowitz reminds that introduction of pain neuroimaging in

courtrooms elicited the same skepticism because its apparent exactness was being deceptive to

the juries to overestimate visual evidence (Salmanowitz 144). The same consideration can be

made with respect to fMRI-based lie detection, in which the brightness of brain anatomy might

introduce the illusion of infallibility. According to Vrij, Fisher, and Blank, such overconfidence

disregards the cognitive variation of deception, which is influenced by the emotional load,

motivation and stress of the situation (Vrij, Fisher, and Blank 10). Failing to conceive these

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elements as conclustive indicators of lying sabotages justice and not promotes it. Vicianova can

track this issue to the historical foundations of lie detection, when believing in technology would

be a better substitute to skepticism (Vicanova 525). The moral of the story is that the legal

system should not be cause-blinded by reliability to possible epistemic boundaries. Even the

seemingly objective technologies are interpreted and not acknowledging this continues to be the

source of the moral fault of the previous pseudoscientific methods.

Deception detection innovations have recently led to multimodal systems that integrate

behavioral, physiological, and computational data; however, the ethical issues of these systems

have not been resolved yet. According to Şen et al., machine learning models that were trained

on courtroom data and showed high accuracy levels also increased biases in the datasets (Şen et

al. 314). In other words, those systems which achieve good results still inherit the ethical blind

spots of their creators. Lai, Chen, and Chiang provide fuzzy reasoning systems to cope with

uncertainty in data comprehension, thus giving the truth as a probability rather than as a binary

decision (Lai, Chen, and Chiang 175). This answer introduces a nuance but it also raises

questions about the role of probabilistic truth in a justice system that is inherently categorical of

guilt or innocence. Fornaciari and Poesio argue that linguistic ambiguity is frequently beyond

computational classification, hence it is a factor that makes the morality of algorithmic decision-

making more complex (Fornaciari and Poesio 329). The conjunction of computation and ethics

in deception research is, therefore, a paradox: as systems get more accurate, they also call for

more interpretive humility on the part of their human users.

Neuroimaging-based lie detection, once legally incorporated, essentially conflicts with

the very notions of credibility and autonomy that have been there for a long time. Scarpazza and

Sartori argue that such a technology might lead to the situation where human testimonies are

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considered just neural data, and thus, the machine becomes the new moral agent, taking over the

individual (Scarpazza and Sartori 479). This change causes a shake-up of the legal principles that

have been relying on the dominance of the mind over the body, e.g., the law has been giving

more weight to intentions and thoughts rather than biological facts. Salmanowitz suggests that

while neuroimaging may be an avenue to understanding of mental states, it has to be ethically

legitimized through the conditions of voluntary participation and informed consent (Salmanowitz

147). In situations where people are forced upon, like in a court of law or in a jail cell, you can

hardly ever see these conditions being met. On top of that, Vrij, Fisher, and Blank emphasize that

the detection of deceit effectiveness depends on the validity of the underlying cognitive

assumptions, such as the connection between lying and mental effort, which they argue is an

over-simplification of the psychological reality and further that the authors also give other

explanations, for instance, anxiety or fear, instead of the mental effort (Vrij, Fisher, and Blank

7). The limitations of the latter to name a few indicate that even so-called technological

objectivity cannot replace the moral responsibility that technology needs to be handled with. The

problem they have is not about getting better image quality, rather it is about safeguarding the

ethical pillars of justice against the persuasive power of science.

Various studies increasingly question whether deception detection can ever be neutral

from an epistemological point of view. Suchotzki et al. argue that reaction time measures, which

were once considered to be reliable indicators of lying, depend a lot on the situational context

and individual cognitive load and, therefore, vary significantly (Suchotzki et al. 433). Their

findings challenge the idea that deception is characterized by universal temporal patterns of the

reaction times. Vicianova points out that even early lie detection instruments such as pulse

monitors were based on overgeneralized physiological correlations (Vicianova 528). This

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historical continuity reveals that the quest for mechanical truth has always been troubled by the

same conceptual flaw - the reduction of moral complexity to measurable behavior. Fornaciari

and Poesio claim that automated linguistic models may make the same mistake of structurally

assuming that the syntax of the language inherently contains the deception when it is socially

performed (Fornaciari and Poesio 334). The point is that no matter how advanced the technology

is, truth is still an interpretive construct. Being aware of this shields the court from the justice

system conflating objectivity with fairness.

Deception detection technologies that go beyond methodical and ethical boundaries also

raise bigger philosophical questions about human freedom and accountability. According to

Scarpazza and Sartori, the neural evidence of deception may unintentionally weaken the person's

control of their actions by suggesting that behavior is biologically determined (Scarpazza and

Sartori 480). When lying is something that can be objectively seen, giving moral responsibility to

a machine becomes a process, this change has deep legal consequences. Salmanowitz also says

that similar discussions are happening about neuroimaging for pain assessment, where the

objective data most of the time take the upper hand over the subjective experience (Salmanowitz

146). In the case of deception, this struggle between the two sides risks the disappearance of the

insight and the narrative as reliable ways of telling the truth. Vrij, Fisher, and Blank point out

that empathizing with the deceiver's cognitive state is as important as being technically accurate

when one wants to grasp the concept of deception (Vrij, Fisher, and Blank 14). Their view

supports the idea that the machine cannot be given the task of moral interpretation. Ethical

principles require the participation of the human mind, not the brain, to be understood in its

social, emotional, and moral side.

Argument for Courtroom Application

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One of the primary reasons for the use of neuroimaging and deception detection tools in

courts is that it may lead to more accurate and fair judicial decisions. In their study, Aletras et al.

point out that machine learning models that work with legal texts are able to predict judicial

outcomes of the European Court of Human Rights with a high degree of consistency (Aletras et

al. e93). The ability of such a system to make predictions serves as a means of AI collaboration

with human judges, as AI identifies the most relevant precedents and makes cognitive biases less

likely. On the other hand, Ryan argues that transparency and trustworthiness are the main factors

that determine whether one can put trust in AI, and he emphasizes that ethical use of technology

necessitates that humans supervise every stage of the decision (Ryan 2755). This perspective is

in agreement with the rule of law according to which the court is entitled to employ technology

as a tool to assist, rather than entirely handing over the reasoning to, the judiciary. According to

Spano, the process-based review court involvement, where courts incorporate procedural

guarantees in the examination of digitally processed data, ensures that the rule of law is retained

even when there is a change in technology (Spano 476). The deployment of deception detection

in such a setting could thus not only help in evidentiary standards but also in ensuring that judges

retain their independence. Therefore, the use of technology, together with control and regulation,

can actually be a means of upholding the ethical basis of judicial decisions rather than a tool that

diminishes it.

The use of neuroimaging and AI-based lie detection in court procedures is expected to

lower the rate of wrongful convictions by providing measurable data to supplement subjective

testimony. Pech and Platon argue that judicial independence should be accompanied by

accountability and, therefore, courts need to respond to innovation by not only accepting it but

also ensuring fairness (Pech and Platon 1831). In this respect, lie detection devices can be

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considered as verification units that help the human decision-making process. Ryan states that

the moral reliability of AI depends on the way its algorithms are trained, assessed, and changed

to be in line with transparent decision-making principles (Ryan 2758). Such a model of

accountability is similar to the chain of custody for evidence, in that it allows for the neural data

to be accessible to verification and ethically based. Aletras et al. position that in addition,

algorithmic regularity may enhance legal predictability especially in complicated human rights

scenarios where the evidence is open to interpretation (Aletras et al. e94). However, the

difficulty is to take steps to combine them with the judiciary without losing its moral discretion.

When used properly, the use of deception identification can contribute to the betterment of the

justice system by providing a moral judgment based on scientific evidence instead of just relying

on the intuition.

The ethically sound neuroimaging in court scenarios justifies its introduction as a means

of democratizing the very process of evaluating the truth. Alsaawi, among other points, states

that qualitative research, similar to human testimony, is a subjective one, as it is influenced by

language, culture, and interpretation (Alsaawi 76). On the other hand, lying detection devices

intend to eliminate such subjectivity by offering physiological or linguistic data which are

beyond the personal bias of the observer. But Raji et al. caution that algorithmic systems carry a

risk to reproduce unconscious social inequalities that are embedded in their structure (Raji et al.

4). Understanding this drawback necessitates the creation of inclusive, ethically vetted systems

that take into account the diverse population. Cerri, Testa, and Rizzi argue that if people feel that

they are treated fairly and that the process is transparent, then they are more likely to recognize

technological mediation as legitimate (Cerri, Testa, and Rizzi 347). The point of legal systems is,

therefore, a radical one: ethical transparency turns technological reliability into social trust. The

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use of detection techniques combined with participatory ethics can, thus, be instrumental in

advancing procedural justice and strengthening public trust.

One major way in which neuroimaging technologies could change the rigor of

evidentiary practices is by providing objective insights into mental states that are relevant to

criminal intent. Gerards mentions that the European Court of Human Rights considers a

"necessity test" when deciding the proportionality between state interference and human rights

(Gerards 470). Applying similar scrutiny to neuroimaging means that its usage is justified only

when it materially contributes to justice. Ryan's "trustworthy AI" model also supports this view

by requiring that systems be explainable, accountable and in line with human-centered ethics

(Ryan 2753). Such an agreement stops technological evidence from having more weight than the

moral judgment. Aletras et al. note that predictive legal tools, can, in fact, be a source of judicial

interpretation, rather than the main one, by being thought of as analytical assistants (Aletras et al.

e95). Within this framework, the detection of lies is just another layer of evidence that is being

strengthened rather than substituted. The brain-based evidence could be, therefore, the courts’

next step in a corroborative mechanism, which, by human reasoning, gets them to a better

conclusion, thus, striking a balance between scientific insight and moral deliberation.

The use of deception detection technologies in court also complies with the European

principle of subsidiarity, which highlights the joint responsibility of institutions and individuals.

According to Spano, subsidiarity supports legality by giving lower courts the power to interpret

the law on their own within a regulated framework of control (Spano 481). In the case of tech

evidence, this concept indicates that judges on the ground still have the freedom to decide while

they can be assisted by the uniform AI-supported evaluations. Ryan argues that the creation of

ethical AI can only be achieved if the AI is adaptable to the context, i.e., such a system should

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function differently depending on the social and legal surroundings (Ryan 2756). The same

flexibility is there in subsidiarity which is a very dynamic way of governance. Raji et al. warn

that lack of regulation in technology standardization may result in a blanket set of ethical

standards which gradually replace the different institutional types (Raji et al. 7). Hence, the use

of deception detection technology must be introduced in stages through a subsidirary model

whereby the ethics of the issue and the local values cohabitate. Such a move safeguards not only

the technological side of things but also the judicial pluralism in different legal systems.

The use of deception detection systems alongside procedural justice can be instrumental

in the reduction of human bias and the removal of emotional influence from trials. Alsaawi

contends that qualitative data are rich; however, they are more vulnerable to subjective

misinterpretation by investigators or judges (Alsaawi 78). The use of neuroimaging with AI

models eliminates the partiality layer; thus, the verification can be helpful in going against the

implicit bias that exists subconsciously. Authors Cerri, Testa, and Rizzi demonstrate that the trust

in information systems by the users is enhanced when they perceive that there is an ethical

alignment between the institutional goals and the moral expectations (Cerri, Testa, and Rizzi

345). This link between the two factors suggests that the transparent implementation could lead

to the perception of the legitimacy of judicial decisions being higher. As per Ryan points out that

apart from technological reliability, ethical accountability is also necessary to have trust that is

long-lasting. According to Ryan (2754), the fusion of moral transparency and scientific rigor has

the potential of making the courtroom practice more fair. In other words, deception detection

might be seen as a scientific means which not only supports the justice process but also serves as

a moral safeguard preserving justice integrity.

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The use of deception detection technologies in courtrooms is not just a technological

enhancement but is a sign of an essential shift in the fundamental ethics of societal understanding

of truth and responsibility. Spano argues that the future of the European court of human rights

lies in the balancing of legal tradition and process based innovation (Spano 482). The

modernization of the law system is therefore in a way not to eradicate its character, but on the

contrary, it is supposed to be as mature a personality as still has the old features, but has

developed and corrected them. The point is supported by the research of AI ethics done by Ryan.

According to him, trust in technology is not achieved because the technology is perfect and in

fact, trust is achieved because the technology is not a secret, it is a system that is open and aware

of its flaws and, at the same time, invites responsible interaction with the users (Ryan 2759).

Similarly, Pech and Platon believe that the judiciary will be the most autonomous when

technological advancements are incorporated in a properly developed constitutional system

(Pech and Platon 1833). On the one hand, it is possible to regard works by men as juxtaposition,

and on the other hand, they may be joined to create a coherent opinion that technological fight

against lies is not only to grab liars. Although they can be applied in generating epistemic

responsibility, they are tools that compel all the stakeholders to be wary in their pursuit of the

truth. In this respect, the court is not merely a place where final decisions are delivered. It

presents itself as the ship of the moral progress, of a common spot where technological

development and the judgment of man, as equals, participate in the pursuit of justice.

The Argument

In this paper, I am arguing that the use of lie detection systems should be applied in the

courtrooms because it is a helpful process that could lead to the punishment o offenders and

changing of behaviors that could harm others in the society. In criminal law, the issue at stake is

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always judging the behavior of reasons while in science, the issue is the understanding of human

behavior (The President’s Council on Bioethics). Even though these are two very different

things, there is a need to apply the use of lie detectors in the courtrooms to ensure that the

testimonies given are truthful r correct. What has been the case in the justice systems around the

world is that people who are able to hire expensive lawyers have always won cases despite being

guilty of perpetrating criminal acts. This means that over the years, criminals have always fund

their way out of justice since they were able to manipulate the court with their facts. Therefore,

the use of a lie detector system could ensure that what such criminals are saying is the truth.

Using it to access information from he could ensure that they are punished for their acts to serve

as a corrective measure and as a way of keeping his or her behaviors from affecting other people

in the society.

Legal and Moral Implications

The other reason why using it in court is important is based on the mens Rea doctrine. In

criminal law, what matters is not only the determination of whether a person is guilty or not.

Therefore, determining the intentions behind certain actions could help to map out the whole

issue and the appropriate steps to follow. Therefore, there is a need to determine if the person

was aware of the effects that such acts could lead to and if the acts were done through

recklessness or neglect. I, therefore, belie that given these premises, it is important to apply

neuroimaging and lie-detection systems because it is through these systems that it becomes

possible to determine whether someone did something intentionally or not. Reading the brain of

the person through neuroimaging technologies could allow the court to access such information

and make appropriate decisions (The President’s Council on Bioethics). For instance, through

deception detection devices and the neuroimaging technologies, the court could be able to

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determine whether someone had an intention of committing the crime or he or she committed the

crime out of anger or unintentionally. The other thing is that through the use of these

technologies, the court is able to determine if the act was committed through negligence or

recklessness. After that, it becomes possible to apply the appropriate decision based on the

impact of the act and what the law stipulates on such offenses.

Integrating neuroimaging with legal reasoning increases the ability of the courts to

evaluate moral blame by physically linking the brain with the behavior. Meijer et al. point out

that deception is an elaborate neural, behavioral, and autonomic process that is controlled from

both the conscious and the moral decision-making parts of the brain (Meijer et al. 595). This idea

signals that brain-based information could be instrumental in differentiating a complete lie from

a spontaneous emotional reaction. Also, Hauch et al. argue that the linguistic and physiological

alterations that accompany different mental states indicate that the deception is not just a speech

but a neurologically patterned process (Hauch et al. 309). These relationships give the courts a

chance to assess not only the content of the testimonies but also their cognitive authenticity.

However, the ethical use of these tools calls for a limitation of their application and a critical

examination. Suchotzki et al. argue that reaction time, which is frequently used in deception

detection, shows the greatest source of variability which is attention and anxiety and not only

dishonesty (Suchotzki et al. 430). This warning post brain data should be interpreted in a legal

and ethical manner that takes into account the limitations of science and the complexity of

human beings. If employed with care, neuroimaging can elucidate our grasp of the intention

without the need to justify the moral nature by the law of the brain.

In addition to providing accurate evidence, neuroimaging technologies have a far-

reaching impact on the way justice deals with responsibility. Yao and Wang portray

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photoacoustic imaging as a method that can show brain activity both at the micro and the macro

level, thus, giving the most detailed understanding of the brain processing the moral decision-

making (Yao and Wang 011003). This ability may be of great help in courts when they have to

evaluate the mental faculties and impulse control in the case of a reduced sense of responsibility.

Baliyan et al. emphasize that diffusion-weighted imaging is a technique that points to the areas of

the brain where the neural pathways have been severed and that, as a result, the person may not

be able to make rational decision and, most importantly, the person may be traumatized or

mentally ill in this context (Baliyan et al. 788). This kind of information leads to more just

decisions as it bases the degree of blame on the neurobiological facts and not on mere

speculation. Besides, Castillo observes the change of brain imaging representing a temporal

change from the descriptive to the functional one, where the hidden inner world of the mind is

becoming visible (Castillo S113). However, a certain degree of moral hesitation should always

be present. The role of the courtroom is not to create a machine that assigns guilt but rather to

show the human side of the error. In this way, neuroimaging, when used properly, is a tool that

helps the justice system to be in line with science but at the same time to have the necessary

humanistic approach - to recognize both the biological side of the brain and the moral side of the

human nature.

Counterarguments and Assessment

Opponents of the use of neuroimaging for lie detection frequently argue that its visual

accuracy misleads people into confusing the image for the actual truth. As Orrison et al. argue,

pictures of the brain have an “aura of authority” because they seem to demonstrate the very

process of thought (112). This obvious lack of intermediary encourages both courts and the

public to take the visual data for absolute truth. However, such a view confuses correlation with

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causation. For example, when Castillo says that “translation of neural activation into

psychological meaning” is an interpretive act rather than a direct one (S115), he is pointing out

that the brain is always involved in imaging. The risk is that it is not the imaging itself but the

cultural overconfidence that often accompanies it. Therefore, “neurorealism,” to use the term of

Thibault, Lifshitz, and Raz, in courts is the phenomenon of the human reason being eclipsed by

the enchanting visuals (252). A rigorous evaluation should thus be geared towards distinguishing

the influence of the technology from its knowledge value. Neuroscience can only be an

instrument of justice, not a distortion of it, if we develop the necessary interpretive humility.

Moreover, supporters of such a viewpoint also state that neuroimaging is not reliable

enough to be used as legal evidence since the same neural processes can correspond to different

psychological states. Ajithkumar et al. point out that, in radiotherapy which is a highly precise

method, even there, the results of the imaging can differ due to the variations in the anatomy of

different individuals (e93). The differences referred to here make it very difficult to identify

universal lying markers when they are simply translated as different ways of detecting deception.

On the other hand, differences do not mean that the method cannot be useful; they show that it is

necessary to interpret the data in the context, which is what ethical science always leads to.

Fendler claims that validity and reliability should be considered “reciprocal ethical aims,” rather

than two separate concepts (219). The recognition of this point of view substantially changes the

position of the uncertainty from being a defect to being a condition of moral awareness. In the

same vein, Mohammadi et al. also point out that institutional and cultural context can have a

subtle influence on reliability aspects even when the methodology is completely standardized

(4). In the case of the legal system, this implies that uniformity should not only be promoted

together with, but also complemented by, the sensitivity towards the particular circumstances.

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The recognition of the presence of the interpretive variation instead of the denial of it is what

actually can turn neuroimaging from a very precise instrument for diagnosis into a morally aware

evidentiary practice.

Another critique of deception detection argues that it deeply invades cognitive privacy.

Chung, Demiris, and Thompson indicate that technologies aimed at monitoring one’s internal

states may take away individual autonomy if they work without giving clear instructions or

seeking consent from the person concerned (159). The comparison between smart home

surveillance and neuroimaging points to a common danger: the loss of agency due to observation

that is done without the knowledge of the person. Such worries being powerful from an ethical

view, they assume that all observation is coercive by nature. However, Ryan argues that reliable

systems can still be compatible with autonomy if transparency and accountability are their

characteristics (2753). This framework sees neuroimaging not as a means of surveillance but as a

controlled source of evidence. When people involved know the way the data are used, the ethical

basis of consent moves from being a mere symbol to becoming real. Fendler, in his argument,

states that moral trustworthiness depends on “procedural respect”, understanding that fairness

comes from involvement, not just from obedience (224). Therefore, the issue is not whether the

imaging is an intrusion but if its use respects the subject’s moral and epistemic agency.

One of the objections to this argument is the chance of algorithmic bias in automatic

lying detection systems. Raji et al. have demonstrated that even a well-intentioned AI audit

reproduces social inequities embedded when training data reflect biased norms (4). This suggests

that technical neutrality is only a facade, machines get the ethical frameworks from their

creators. The result for the lie detection field is that very significant one: fairness cannot be

programmed; it has to be constantly supervised. Ryan’s idea of “epistemic automation,” where

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moral trust gets mixed with technical accuracy, suggests that if we rely on algorithmic precision

without checking, we may become morally indifferent (2751). However, transparency in

algorithmic operations may be a remedy for that. When Alsaawi insists on the necessity of

reflexivity in the qualitative interpretation (77), his point also holds true for computational ethics:

being aware of the limitations of interpretation leads to the engagement of responsible

innovation. Therefore, in court, algorithmic systems should be considered not as moral agents

but as interpretive partners, capable of making mistakes, providing information, and being

corrected by humans.

Ethical philosophers of the same field also argue that the objective nature of

neuroimaging, as it is claimed, might hide the fact that the truth is a social construct. Fendler

argues that even the concept of validity as such is an ethical decision because it correlates with

the forms of knowledge considered credible (216). When the court considers neuroscientific data

as the most accurate, it is in fact very likely to silence other voice-ways of knowing – for

instance, emotion-based or culturally-experienced testimony. Markovina et al. discovered that to

have behavioral reliability measures that are both cross-cultural and accurate, one must change

the survey instruments according to local meanings so as not to distort the concepts (27). This

point brings out that ethical loss always accompanies the universalization of truth. In the same

vein, by faking that all brains work similarly when lying, deception detection may unnecessarily

be setting a monocultural standard of cognition. Ryan states that the fairness of AI and

neuroscience can be realized only through “context-aware adaptability” which considers

diversity not as a problem that needs technical solving but as a moral asset (2756). Being an

ethical tribunal, the assessment is therefore compelled to recognize pluralism as a prerequisite for

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its own trustworthiness and to be sure that the technology does not diminish but, on the contrary,

promotes the human variety of truth-telling.

A different counterargument to this issue refers to the possibility of the emotional

distortion of the courtroom when it heavily relies on neuroscientific evidence. Cerri, Testa, and

Rizzi state that "moral persuasion depends more often than not on the emotional side of things

rather than on the factual one" (346). In case the members of the jury get more impressed with

the picture of a brain "lighting up" rather than by a logical line of the argument, thus justice

would be at risk of turning into an emotional show. Nevertheless, emotion does not have to be at

odds with the truth; instead, it can be a moral guide in the process of recognizing the evidence.

Alsaawi’s review of qualitative inquiry argues that empathetic understanding, contrary to

traditional views about it, actually strengthens the trustworthiness of the study as it uncovers the

lived experience of the data (80). Correspondingly, Orrison et al. argue that "functional imaging

is not only about cognition but also the embodied experience of human thought" (118).

Consequently, it is the role of the courtroom to neither get rid of emotion nor to weigh up its

interpretative power with a more skeptical and reflective attitude. The ethical evaluation, thus,

necessitates acknowledgment that both brain data and human feeling are elements of the same

moral ecosystem of truth.

Detractors of such practice argue that the deployment of neuroimaging can make the

judiciary a system operated solely by machines without any human intervention. But as Ryan

points out, even ethically and logically AI and neuroscience still need human ethics that ensure

they are understandable and that someone can be held accountable (2753). Essentially, the

apprehension of a technocracy is about the systems becoming unexplainable rather than being

technologically advanced. The ways to get over such a fear are being open to inspection, peer

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review, and participation of more disciplines. Fendler holds the view that the moral evaluation

should be always self-reflective, indefinitely reconsidering its viewpoints (227). This reflexivity

prevents the law from mistaking infallibility for expertise. Besides that, Ajithkumar et al. show

that in radiological medicine, continuous ethical review through the improvement of the

technique leads to less cognitive harm over time (e96); hence, the same method can be adopted

for the introduction of imaging in the court. These revelations support the idea that the morality

of technology depends on not abstaining from its use but rather on cultivating an ever-evolving,

self-regulating moral code. The scrutiny of neuroimaging is not about making a trust versus fear

call, rather it is more about understanding the manner of responsibility with precision.

Assessment

Despite the contributions that the field of neuroimaging and the use of deception

detection systems have had on the field of science, there are various arguments against its use.

One of them states that the use of neuroimaging is flawed, inaccurate and unreliable given the cat

that its results are at times inaccurate. The other argument is that it is unethical to scan human

brains because it is against human rights. Racine, et al., (159) asserts that neuroinflation is

considered more problematic because it is believed that it reveals more information than what is

required to about a person. It then becomes a problem because accessing what is not needed

brings about undesirable effects on the subject. Other arguments state that the use of lie-detection

systems are problematic and should not be applied in the court system because the court or the

justice system is not only about determining guilt but the judgment of behavior based on some

other external factors the could have led to the commitment of certain crimes.

The above objections are valid, hover, it is shallow to argue that they should not be used

because of the few flaws it has. The reason why I argue that it should be used is that even the

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justice system does not need a hundred percent guilt or evidence that is a hundred percent

accurate. Therefore, since the court does not need everything to be 100% accurate, it is possible

and very useful to use a process that is over 90% in regard to accuracy which makes lie-detection

systems very useful an applicable (The President’s Council on Bioethics). What I would like to

add is that given the few problems encountered with the use of neuroimaging technologies in

determining whether someone is telling a lie or a truth, the entire progress that has been made in

the field of neuroscience should not be flashed away overnight. Refinements and other

improvements will make the technologies better and more reliable.

Conclusion and Future Prospects

In conclusion, it is clear that the advancement in technology has brought tremendous

changes in the field of neuroscience and despite the various arguments against its applications in

the courtroom; its adoption is inevitable given the various refinements that are shaping its

accuracy and reliability. The deception detection technologies are currently I use in various

companies and organization and the adoption the courtroom has been a controversial topic in

recent years. Lie-detection systems such as the polygraphs have been used over the years and

most scientists regard it as one of the most reliable sources of the truth in the field. It measures

the physiological changes such as blood pressure, electrical skin impulses among others. The

others scan the human brain and allow the experts to read the brain and access information which

could be used to determine if someone is guilty or not. There are various arguments that assert

that given its lack of 100% accuracy, unreliability and the ethical concerns discussed such as

neuroinflation, it does not make sense to say that it should not be used in the courtroom because

even the courts do not need 100% accurate data to convict a criminal. I believe that refinements

and advancement in technologies coupled with the various formations affirming about its

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reliability could help in court’s decision making processes. Therefore, deception detection

systems could be applied in the courtroom

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Works Cited

Ajithkumar, Thankamma, et al. "Prevention of radiotherapy-induced neurocognitive dysfunction

in survivors of paediatric brain tumours: the potential role of modern imaging and

radiotherapy techniques." The Lancet Oncology 18.2 (2017): e91-e100.

Fendler, Lynn. "Ethical implications of validity-vs.-reliability trade-offs in educational

research." Ethics and Education 11.2 (2016): 214-229.

Mohammadi, Mohammad, et al. "A study of the validity and reliability of the questionnaire

entitled “physicians' approach to and disclosure of medical errors and the related ethical

issues”." Journal of medical ethics and history of medicine 12 (2019): 2.

Aletras, Nikolaos, et al. "Predicting judicial decisions of the European Court of Human Rights: A

natural language processing perspective." PeerJ computer science 2 (2016): e93.

Alsaawi, Ali. "A critical review of qualitative interviews." European Journal of Business and

Social Sciences 3.4 (2014).

Baliyan, Vinit, et al. "Diffusion weighted imaging: technique and applications." World journal of

radiology 8.9 (2016): 785.

Belsuzarri, Telmo Augusto Barba, Raphael Martinelli Anson Sangenis, and João Flavio Mattos

Araujo. "Brain tumor surgery: supplemental intra-operative imaging techniques and

future challenges." Journal of Cancer Metastasis and Treatment 2 (2016): 70-79.

Castillo, Mauricio. "History and evolution of brain tumor imaging: insights through

radiology." Radiology 273.2S (2014): S111-S125.

Niu 45

Cerri, Jacopo, Francesco Testa, and Francesco Rizzi. "The more I care, the less I will listen to

you: How information, environmental concern and ethical production influence

consumers' attitudes and the purchasing of sustainable products." Journal of Cleaner

Production 175 (2018): 343-353.

Chung, Jane, George Demiris, and Hilaire J. Thompson. "Ethical considerations regarding the

use of smart home technologies for older adults: an integrative review." Annual review of

nursing research 34 (2016): 155.

Conroy, Nadia K., Victoria L. Rubin, and Yimin Chen. "Automatic deception detection:

Methods for finding fake news." Proceedings of the association for information science

and technology 52.1 (2015): 1-4.

Fornaciari, Tommaso, and Massimo Poesio. "Automatic deception detection in Italian court

cases." Artificial intelligence and law 21.3 (2013): 303-340.

Gerards, Janneke. "How to improve the necessity test of the European Court of Human

Rights." International journal of constitutional law 11.2 (2013): 466-490.

Hauch, Valerie, et al. "Are computers effective lie detectors? A meta-analysis of linguistic cues

to deception." Personality and social psychology Review 19.4 (2015): 307-342.

Lai, Ying-Fang, Mu-Yen Chen, and Hsiu-Sen Chiang. "Constructing the lie detection system

with fuzzy reasoning approach." Granular Computing 3.2 (2018): 169-176.

Mabray, Marc C., Ramon F. Barajas Jr, and Soonmee Cha. "Modern brain tumor

imaging." Brain tumor research and treatment 3.1 (2015): 8.

Niu 46

Markovina, Jerko, et al. "Food4Me study: Validity and reliability of Food Choice Questionnaire

in 9 European countries." Food quality and preference 45 (2015): 26-32.

Meijer, Ewout H., et al. "Deception detection with behavioral, autonomic, and neural measures:

Conceptual and methodological considerations that warrant

modesty." Psychophysiology 53.5 (2016): 593-604.

Orrison, William W., et al. Functional brain imaging. Elsevier Health Sciences, 2017.

Pech, Laurent, and Sébastien Platon. "Judicial Independence under Threat: The Court of Justice

to the Rescue in the ASJP Case-Case C-64/16, Associacao Sindical dos Juizes

Portugueses, Judgment of the Court of Justice (Grand Chamber) of 27 February 2018,

EU: C: 2018: 117." Common Market L. Rev. 55 (2018): 1827.

Raji, Inioluwa Deborah, et al. "Saving face: Investigating the ethical concerns of facial

recognition auditing." Proceedings of the AAAI/ACM Conference on AI, Ethics, and

Society. 2020.

Rizvi, Tanvir, Prem Batchala, and Sugoto Mukherjee. "Brain death: diagnosis and imaging

techniques." Seminars in Ultrasound, CT and MRI. Vol. 39. No. 5. WB Saunders, 2018.

Ryan, Mark. "In AI we trust: ethics, artificial intelligence, and reliability." Science and

Engineering Ethics 26.5 (2020): 2749-2767.

Salmanowitz, Natalie. "The case for pain neuroimaging in the courtroom: Lessons from

deception detection." Journal of Law and the Biosciences 2.1 (2015): 139-148.

Scarpazza, Cristina, and Giuseppe Sartori. "Deception in Court—Open Issues and Detection

Techniques." Frontiers in Psychiatry 11 (2020): 476.

Niu 47

Şen, M. Umut, et al. "Multimodal deception detection using real-life trial data." IEEE

Transactions on Affective Computing 13.1 (2020): 306-319.

Spano, Robert. "The future of the European court of human rights—Subsidiarity, process-based

review and the rule of law." Human Rights Law Review 18.3 (2018): 473-494.

Suchotzki, Kristina, et al. "Lying takes time: A meta-analysis on reaction time measures of

deception." Psychological Bulletin 143.4 (2017): 428.

Thibault, Robert T., Michael Lifshitz, and Amir Raz. "The self-regulating brain and

neurofeedback: Experimental science and clinical promise." cortex 74 (2016): 247-261.

Vicianova, Martina. "Historical techniques of lie detection." Europe's journal of psychology 11.3

(2015): 522.

Vrij, Aldert, Ronald P. Fisher, and Hartmut Blank. "A cognitive approach to lie detection: A

meta‐analysis." Legal and Criminological Psychology 22.1 (2017): 1-21.

Wintermark, Max, et al. "Imaging evidence and recommendations for traumatic brain injury:

conventional neuroimaging techniques." Journal of the American College of

Radiology 12.2 (2015): e1-e14.

Yao, Junjie, and Lihong V. Wang. "Photoacoustic brain imaging: from microscopic to

macroscopic scales." Neurophotonics 1.1 (2014): 011003-011003.

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