1
GENERATIVE AI IN AUSTRALIA AND INTERNATIONAL LAW
by (Name)
The Name of the Class (Course)
Professor (Tutor)
The Name of the School (University)
The City and State where it is located
The Date
2
Generative AI In Australia And International Law
Generative AI is a form of artificial intelligence that primarily entails generating new
material, such as text, images, or music. It has also been recognised as a groundbreaking
technology that may redefine commerce. Its uses in creating tailored marketing messages,
improving logistics in trading companies, and intelligent customer support through better
chatbots hold much potential for creating more value in terms of access, uniqueness, and value.
For instance, generative AI in businesses assesses large volumes of consumer data that generates
excellent recommendations and unique product designs that meet market needs. Nevertheless, as
with any rapidly developing technology, the use of generative AI in commerce has its issues.
These include societal issues as well as legislative ones. This essay will fully explore these
challenges with the help of relevant scholarship and Australian regulatory frameworks to offer
viable solutions.
Legal Challenges in Generative AI: Australian Commonwealth Law vs. US Law and EU
Law
Australian Commonwealth Law
Generative AI technologies in Australia are governed by several laws of the
Commonwealth of Australia, although the most prominent is the Privacy Act 1988 (Cth).
According to this Act, the collection, use, disclosure, and security of personal information are
regulated so that all entities follow the APPs, which are guidelines on how personal information
is to be managed.1 The Act will help citizens have privacy rights, which are required to adhere to
the best practices in handling personal data, and provide citizens with the right to access and
correct their data. Moreover, the respective authorities, such as the Australian Competition and
1 Philippa Ryan. Trust and distrust in digital economies. Routledge, 2019. 24.
3
Consumer Commission (ACCC), regulate competition and consumer issues that, in turn, affect
the adoption of AI technologies.2 The regulatory functions of ACCC involve promoting fair
trading, controlling anti-competition practices, and safeguarding consumer interests, which
remains virtually relevant to the application of AI in the market. Thus, the ACCC's enforcement
of these regulations contributes to shaping a competitive and non-discriminatory market for AI
development and use, thereby protecting consumers' rights and encouraging innovation within
the existing legal framework.
Current Legislation: Privacy Act 1988 (Cth) and Consumer Data Right (CDR)
Data protection in Australia is primarily based on the Privacy Act 1988 (Cth). This Act
governs the handling of personal information by organisations and agencies and shall
obligatorily comply with the Australian Privacy Principles.3 The APPs articulate issues relating
to collecting, using, disclosing, and storing personal information relevant to data management. It
also ensures that people have the right to access and correct their data, promoting transparency
and ensuring accountability in practices with data handling. The Act areas are also solid in
provisions for data security; hence, these entities have to, under their legal obligation, take
reasonable steps in ensuring there is necessary protection of personal information from misuse,
interference, and loss as well as unauthorised access, modifications, or disclosure. Consumer
Data Rights is, in fact, complementary legislation that ensures that individuals' data is placed in
their own hands.4 In this way, it facilitates access, control, and sharing of data businesses hold
regarding a mechanism for consumers' consent through open banking, especially with third
parties. In doing so, real competition within the consumer marketplace can be assured
2 Eleanor Bird et al., The Ethics of Artificial Intelligence: Issues and Initiatives ([Brussels]: European Parliament,
2020), https://www.europarl.europa.eu/RegData/etudes/STUD/2020/634452/EPRS_STU(2020)634452_EN.pdf.
3 Philippa Ryan. Trust and distrust in digital economies. Routledge, 2019. 26.
4 Philippa Ryan. Trust and distrust in digital economies. Routledge, 2019. 27.
4
accordingly, with consumer choice in areas like banking, energy, and other telecommunications
services of importance to consumers.
US Law: CCPA and Algorithmic Accountability Act
The United States presents a more fragmented regulatory concern concerning data
protection and AI accountability through a mix of federal/state laws covering different
subaspects of data privacy. The California Consumer Privacy Act is a very codified law that
gives California residents many rights over their personal information.5 These rights include the
information regarding the nature of personal data to be collected, the access of the personal data
being held, and the deletion of the same. Further, the CCPA undermines business requirements
by releasing disclosures on their information practices and guarantees personal data safety from
probable breaches. Another vital piece of proposed legislation is the Algorithmic Accountability
Act, which will fill the exact lacuna that automated decision-making systems pose.6 It will be
seen that companies make impact assessments of their algorithms to identify and mitigate direct
discriminatory impacts. The fundamental point of the Algorithmic Accountability Act is
transparency and holding in check the operationalisation of well-working AI systems so that they
do not increase or aggravate already-existent forms of bias.
EU Law: General Data Protection Regulation (GDPR)
Among the most extensive and restrictive data protection regimes worldwide is the
General Data Protection Regulation of the European Union. It took effect on May 25, 2018, and
has set an extremely high bar for data processing activities in the EU. Essential requirements
include explicit consent for data gathering, a right to have one's data deleted (right to oblivion),
and, in case of a breach, it should be reported within 72 hours of its discovery.7 GDPR has also
5 Regulation of Digital Economies. (n.d.).
6 Philippa Ryan. Trust and distrust in digital economies. Routledge, 2019. 27.
7 Regulation of Digital Economies. (n.d.).
5
made provisions to ensure that organisations have established responsive data protection and
officers for data protection in case of confidential data processing on a large scale. Failure to
follow its rules can result in severe financial penalties, the maximum being up to €20 million or
4% of the company's respective annual global turnover, whichever is greater.8 The regulation
allows the EU to remain committed to taking measures that will protect the privacy rights of its
people and to track the recommended norms through data processors while in practice. The strict
standards of the GDPR have projected a template for the protection of data law worldwide and
have also influenced different forms of legislation at various levels in other countries like
Australia and the US.
Gap Analysis: Legal Frameworks
Australia vs. the US.
Although Australia's privacy regulation is generally proactive, emanating from the base
of the Privacy Act 1988, a visible lack of definite legislation can address the special and unique
problems brought by artificial intelligence and algorithmic accountability. More specifically, the
general construct of the Privacy Act is mainly concerned with how personal information is
stored. It includes requirements to safeguard information, whose adherence is covered by the
Australian Privacy Principles (APPs) regarding security, access, and data quality. But even more
simply, there needs to be a more fine-grained framework involving questions of transparency in
algorithmic decision-making or mitigating biases with AI technologies.9 The United States is
leaning only toward targeted regulatory measures from the propositions of the Algorithmic
Accountability Act.10 These methods will, in particular, create mechanisms for conducting
impact assessments of the automated decision systems operated by companies to identify and
8 Philippa Ryan. Trust and distrust in digital economies. Routledge, 2019. 30.
9 Regulation of Digital Economies. (n.d.).
10 Regulation of Digital Economies. (n.d.).
6
mitigate direct discriminatory impacts. This approach will enhance accountability and
transparency in AI operations, guaranteeing that these technologies do not improve or entrench
existing societal collection biases. By directly addressing such precise issues, the USA is making
its holdout on this issue a solid departure from the existing checkbox mentality within Australia.
Australia vs. EU.
While Australia does possess the Privacy Act 1988, the General Data Protection
Regulation by the European Union is much larger and a must to abide by. In Australia, specific
consent from a person is required for conducted activities related to data processing, such as
reaching an individual with ads, ruled by the General Data Protection Regulation that became
effective on May 25, 2018.11 The latter ensures high data protection while being enforceable
through fines in case of an infringement. This kind of detailed regulation and vigorous
enforcement shows the EU's similarly fierce commitment to protecting individual privacy rights
and defending data processors with no evidence of harm. In contrast, though the Privacy Act
1988 in Australia provides a fair backdrop for data protection, it is more far-reaching in
gradation and particularly stringently forced than in the case of the GDPR.12 Besides this, it is
themed that the detailed focus on data processing, consent, and individual rights in GDPR gives
a higher standard from which Australia could work toward enhancing its privacy laws. It should
build in heavier personal data protection and place it closer to global personal data protection
standards using similar detailed provisions and enforcement mechanisms.
2. Societal Challenges In Generative AI.
Australian Context
11 Philippa Ryan. Trust and distrust in digital economies. Routledge, 2019. 29..
12 Matteo Fabbri, “Social Influence for Societal Interest: A Pro-Ethical Framework for Improving Human Decision
Making through Multi-Stakeholder Recommender Systems,” AI & SOCIETY, May 28, 2022,
https://doi.org/10.1007/s00146-022-01467-2 .
7
In Australia, society's concerns about AI technologies centre mainly on data privacy, job
displacement, and ethical use. On the other hand, with the growing reach of AI appearing in
every corner of society, there is an acknowledgment of the importance of public awareness and
acceptance of such technologies. Notwithstanding, most are seriously harbouring worries about
the transparency and accountability of AI systems. Most are worried about the said applications'
collection, storage, and use of personal data. Indeed, turned worse by uninterpretable, nonclear
explanations regarding the rationale for AI-powered decisions, many hear of the logic
underpinning some forms of conclusions and draw blanks.13 It can be a source of disbelief in the
fairness and trustworthiness of AI systems, especially in critical areas of finance, healthcare, and
employment, where the decisions will be able to have substantial impacts on individual lives.
Key Issues:
More important are the critical issues of transparency and ethical use of AI. Most
essential is transparency and trust; AI systems need clear and understandable descriptions in
natural language for their decision-making and use of data etiquette. The absence of this will
cause users to continually distrust the suites of AI technologies for possible misuse or abuse of
their information. Other societal challenges include the ethical use of AI to ensure technologies
are developed with no bias or discrimination in the deployment process.14. There will be a need
to take great care to ensure AI emerges as a solution so that it does not perpetuate existing biases
but serves equitably and fairly within all demographics. There is also an increasing call for new
regulatory frameworks by which developers and operators may be held accountable for the
13 Eleanor Bird et al., The Ethics of Artificial Intelligence: Issues and Initiatives ([Brussels]: European Parliament,
2020), https://www.europarl.europa.eu/RegData/etudes/STUD/2020/634452/EPRS_STU(2020)634452_EN.pdf.
14 Denis Dennehy, Anastasia Griva, Nancy Pouloudi, Yogesh K. Dwivedi, Ilias Pappas, and Matti
Mäntymäki.MResponsible AI and analytics for an ethical and inclusive digitised society. Springer Publishing
Company, 2021.
8
ethical implications of their AI systems so that such technologies will be positive contributors to
society and not a factor in wider social inequalities.
US Context
In the United States, mirroring concerns in Australian society is the issue relating to the
domination of enormous tech companies and the exaggeration of their influence in the
development and deployment of AI. The rise of big tech firms, in turn, has led to the
concentration of this power and control of AI technologies in the hands of a few.15 This kind of
concentration reminds me that questions about monopolistic practice and a lack of competition in
the AI market are seriously raised. The dominance of such tech giants may prevent the
competition that drives innovation, close opportunities for consumers, and block new, smaller
companies and startups from entering the AI space. Their hands-on massive data enable them to
create their AI systems in a more enabling way, possibly uncontrolled, with a significant impact
on different parts of society.
Key Issues
Due to the scale of the algorithms in machine learning, AI can recycle all existing
inequalities in credit, employment, policing, and even treatment in the healthcare sector. For
example, the systems in face recognition work differently for people of colour, which makes
problems based on racial discrimination credible.16 Overcoming the same biases requires
proactive steps to ensure that diverse and fair datasets and AI algorithms are used during the
training phase.
15 Susan Lund, James Manyika, and Michael Spence. "The global economy’s next winners."MForeign AffairsM98, no.
4 (2019): 126.
16 Jordan Brewer et al., “Navigating the Challenges of Generative Technologies: Proposing the Integration of
Artificial Intelligence and Blockchain,” Business Horizons, April 1, 2024,
https://doi.org/10.1016/j.bushor.2024.04.011.
9
However, the ownership and control of AI by large global technology organisations is of
essential concern. Dominant platforms such as Google, Facebook, Amazon, and Microsoft have
created monopolistic control over digital markets.17 Since the operation of these companies is
based on the use of proprietary data and algorithms, often, little can be done to contest these
behaviours. For this reason, their behaviour is hard to observe and constrain, casting doubt on
data security, proper use and abuse, and political influence.18 Therefore, new legislation is geared
towards ensuring that the implementation of AI technologies guarantees improvement in
fairness, competition, and accountability.
EU Context
Societal concerns concerning AI technologies often drive stringent regulative measures
in the European Union, which equally give due regard to a balance between innovation and
fundamental rights of privacy and data protection in the said region. For example, the
organisation passed the General Data Protection Regulation, which is associated with accurate
data protection of each person's rights.19 It enforces that data processing activity must always get
consent before doing so, guarantees the right to be forgotten, and mandates reporting in case of
any breach within 72 hours.20 In this way, there is the protection of personal data and an
individual's privacy rights, which undoubtedly helps establish the faith to be had in digital
technology. Nevertheless, such a highly regulated environment results in a constant debate about
the balance between encouraging innovation and ensuring stringent data protection. Some will
argue that regulations that are too strong could stifle innovation and competitiveness, particularly
in AI, where its potential speed of experimentation and iteration lies very much at the heart.
17 James Manyika, Susan Lund, Jacques Bughin, Kelsey Robinson, Jan Mischke, and Deepa Mahajan.MIndependent-
Work-Choice-necessity-and-the-gig-economy. McKinsey Global Institute, 2016.
18 Ian Lowrie, “Algorithms and Automation: An Introduction,” Cultural Anthropology 33, no. 3 (August 21, 2018):
349–59, https://doi.org/10.14506/ca33.3.01.
19 Regulation of Digital Economies. (n.d.).
20 Philippa Ryan. Trust and distrust in digital economies. Routledge, 2019. 17.
10
Major issues
The EU strongly believes AI technologies should be designed and deployed ethically,
underscoring fairness, accountability, and transparency. The ethical orientation intends to open
up a pathway through which AI systems would not perpetuate biases or get into discriminatory
practices but ensure that AI serves all segments of society without bias.21 The strategy that the
EU has conceptualised involves the Ethics Guidelines for Trustworthy AI, outlining essential
requirements for AI systems related to human agency and oversight, technical robustness and
safety, privacy and data governance, transparency, diversity and non-discrimination, and
fairness, societal and environmental well-being, accountability, and so on.22. In this way, they
help fuel a human-centred approach towards the development of AI—based on the infusion of
ethical considerations at all stages in the AI life cycle. While much already exists to flesh out this
action, codifying these principles and ensuring that they translate effectively into practice across
all use cases of AI will be difficult. The constant process through which regulators, industry
stakeholders, and the public keep fine-tuning these ethical frameworks and often creating
complex trade-offs between knowing when to innovate and regulate cannot be overemphasised.
Gap Analysis: Societal Contexts
Australia vs. the US.
The challenges are also evident in Australia across the use of ethical nature and
transparency in AI technologies. Most Australians are concerned with how AI systems make
decisions using data and the possible biases they might perpetuate. The public is calling for more
precise explanations and increased transparency of AI operations to have trust in these
technologies and ensure that they are used fairly and responsibly. This focuses on creating
21 Mahmood Masoodifar, İsmet Kahraman Arslan, and Aşkım Nurdan Tümbek Tekeoğlu. "Artificial Intelligence in
Global Business and Its Communication." Journal of International Trade, Logistics and Law 9, no. 1 (2023): 282.
22 Mahmood Masoodifar, İsmet Kahraman Arslan, and Aşkım Nurdan Tümbek Tekeoğlu. "Artificial Intelligence in
Global Business and Its Communication." Journal of International Trade, Logistics and Law 9, no. 1 (2023): 282
11
regulatory frameworks that promote standards of ethics and observance of transparency when
developing and deploying AI. While there is increasing awareness and regulatory effort to adapt
to emerging issues, lateral conversion by technology remains in flux at varying speeds.
At the most, it mirrors the apparent concerns in the US, with one sizeable dimensional
difference: firm corporate control by big tech corporations of AI technologies.23 Concern was on
the dominance of companies such as Google, Facebook, Amazon, and Microsoft in respect of
suspicions of being monopolies – especially in the control over vast amounts of data – coupled
with the very research and development concerning A.I. Such corporations could determine the
course of AI developments and influence market forces in ways that may negatively insulate
market entry and innovating change. There are also issues with its algorithms not being
transparent and praxes related to data, which can give way to eliminating problems of decision-
making bias and lack of overview. However, these aspects will take place in a slightly different
context from the US approach, where considerations on issues of ethics and transparency tap into
more far-reaching concerns, i.e., the issue of that kind of corporate dominance in AI.
Australia vs. EU
A sharp difference concerning the regulation dedicated to registering such aspects in
Australia can be seen in the example of the European Union. In setting high bars in data
protection and privacy, European Union legislation has been quite thorough, and the most crucial
element is the General Data Protection Regulation (GDPR). This particular characterisation of
the GDPR is found in most areas of social concern, which it explicitly embeds into rigorous
requirements on data processing, consent, and breach notifications—all with the powerful
protection of the rights of individuals.24 This regulation has been favourable toward public trust
23 Martha Farah J., and Cayce J. Hook. "Trust and the poverty trap."MProceedings of the National Academy of
SciencesM114, no. 21 (2017): 5328.
24 Regulation of Digital Economies. (n.d.).
12
and has enabled artificial intelligence technologies to be developed and used to respect privacy
and data protection principles. The EU also emphasises the ethical use of AI in guidelines that
encourage fairness, accountability, and transparency in product design.25 Ultimately, the above
institutional steps pave the way for a proactive integration of ethical reviews into the making and
using AI, from scratch to their development and deployment.
By contrast, the Australian approach to ethics and transparency in AI is still being
developed. Data protection is based on the Privacy Act 1988. However, it must afford the broad
scope of the GDPR and enforceability.26 Regulatory efforts are changing concerning the ethical
and transparency issues associated with AI in Australia, but they have yet to reach the
comprehensiveness of work within the EU. The ongoing development of the regulatory blueprint
for Australia balances innovation against ethical standards, learning from the experience of the
EU to underpin measures taken by its legislation.27 Such a gap prompts Australia's need to
enhance its approach toward regulation to ensure the ethical and transparent use of AI
technologies and, subsequently, enhance public trust in these innovative applications consistent
with global best practices.
Conclusion
Australia's adoption and regulation of generative AI technologies will also face legal and
social challenges. While the privacy laws offer a sure foundation, they need to be more mindful
of the detailing and muscularity of those of the EU under the GDPR. There is a need to
understand society's issues sensibly and work towards addressing them with enhanced
transparency and improved ethical guidelines. The journey of the US and the EU should be
25 Martha Farah J., and Cayce J. Hook. "Trust and the poverty trap." Proceedings of the National Academy of
Sciences 114, no. 21 (2017): 5329.
26 Regulation of Digital Economies. (n.d.).
27 Regulation of Digital Economies. (n.d.).
13
learned from. This gap analysis focuses on providing efficacy for the enhancement points in
Australia's legal frameworks and meeting concerned social interests so that a trustworthy AI
ecosystem is ushered.
References
Bird, Eleanor, Jasmin Fox-Skelly, Nicola Jenner, Ruth Larbey, Emma Weitkamp, and Alan
Winfield. The Ethics of Artificial Intelligence: Issues and Initiatives. [Brussels]:
European Parliament, 2020.
https://www.europarl.europa.eu/RegData/etudes/STUD/2020/634452/EPRS_STU(2020)6
34452_EN.pdf.
14
Brewer, Jordan, Dhru Patel, Dennie Kim, and Alex Murray. "Navigating the Challenges of
Generative Technologies: Proposing the Integration of Artificial Intelligence and
Blockchain." Business Horizons, April 1, 2024.
https://doi.org/10.1016/j.bushor.2024.04.011.
Council, A. U. P. P. "Statement on algorithmic transparency and accountability."MCommun.
ACMM(2017).
Dennehy, Denis, Anastasia Griva, Nancy Pouloudi, Yogesh K. Dwivedi, Ilias Pappas, and Matti
Mäntymäki.MResponsible AI and analytics for an ethical and inclusive digitised society.
Springer Publishing Company, 2021.
Fabbri, Matteo. "Social Influence for Societal Interest: A Pro-Ethical Framework for Improving
Human Decision Making through Multi-Stakeholder Recommender Systems." AI &
SOCIETY, May 28, 2022. https://doi.org/10.1007/s00146-022-01467-2.
Farah, Martha J., and Cayce J. Hook. "Trust and the poverty trap."MProceedings of the National
Academy of SciencesM114, no. 21 (2017): 5327-5329.
Lowrie, Ian. "Algorithms and Automation: An Introduction." Cultural Anthropology 33, no. 3
(August 21, 2018): 349–59. https://doi.org/10.14506/ca33.3.01.
Lund, Susan, James Manyika, and Michael Spence. "The global economy's next
winners."MForeign AffairsM98, no. 4 (2019): 121-130.
Manyika, James, Susan Lund, Jacques Bughin, Kelsey Robinson, Jan Mischke, and Deepa
Mahajan.MIndependent-Work-Choice-necessity-and-the-gig-economy. McKinsey Global
Institute, 2016.
15
Masoodifar, Mahmood, İsmet Kahraman Arslan, and Aşkım Nurdan Tümbek Tekeoğlu.
"Artificial Intelligence in Global Business and Its Communication."MJournal of
International Trade, Logistics and LawM9, no. 1 (2023): 278-284.
Ryan, Philippa.MTrust and distrust in digital economies. Routledge, 2019.
Regulation of Digital Economies. (n.d.).
