English project

3/8/2024

Annotated Bibliography

The role of Artificial Intelligence in Society, Benefits, Gaps, challenges and vulnerabilities

Ahmad, Kashif, et al. "Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges."  Computer Science Review 43 (2022): 100452. https://doi.org/10.1016/j.cosrev.2021.100452

Ahmad, Kashif, et al. extensively explore challenges in deploying AI in smart cities, focusing on security, data management, and ethics. They advocate integrating technology with thoughtful policies and responsible governance for human welfare. Discussing risks like biases and adversarial attacks, the article reviews existing literature, identifies limitations, and suggests future research directions. Emphasizing interconnected challenges underscores the need for a holistic approach. The source, marked by detailed analysis and real-world examples, proves highly valuable for understanding complex issues in AI's role in smart cities. Its quality lies in a well-structured presentation and insightful emphasis on challenges' interdependencies, contributing significantly to societal impact evaluation.

Chen, Tao, et al. "AI-based self-service technology in public service delivery: User experience and influencing factors."  Government Information Quarterly 38.4 (2021): 101520. http://dx.doi.org/10.1016/j.giq.2020.101520

Chen, Tao, et al.'s article dives into the user experience and influencing factors of AI-based self-service technology (SST) in public service delivery. The study, conducted in a Chinese administrative bureau, identifies personalization, aesthetics, and perceived time spent as crucial to user satisfaction. The research emphasizes the need for aesthetically appealing, personalized AI-based SSTs and underscores their positive impact on citizen experience. Trust in government is found to moderate this relationship, with citizens trusting the government more likely to have positive experiences. The study's practical implications advise public managers on optimizing SST design, fostering trust, and enhancing citizens' service experience. The research contributes to understanding AI-based SST user dynamics and offers valuable insights for government adoption and implementation.

Cheng, Lu, Kush R. Varshney, and Huan Liu. "Socially responsible AI algorithms: Issues, purposes, and challenges."  Journal of Artificial Intelligence Research 71 (2021): 1137-1181. http://dx.doi.org/10.1613/jair.1.12814

Cheng et al. examine the societal impact of AI algorithms, stressing the importance of responsible AI development. The introduction of Socially Responsible AI Algorithms (SRAs) establishes a comprehensive framework encompassing principles, means, and objectives. They argue for AI's adherence to functional and legal responsibilities and ethical and philanthropic obligations, as outlined in a pyramid model. The paper addresses challenges, including AI defenses, long-term effects, and the responsible AI gap in industry, advocating for interdisciplinary research and emphasizing the application of SRAs for societal benefit. Despite lacking specific examples, the article is a foundational resource for understanding and advancing socially responsible AI.

Floridi, Luciano, and Josh Cowls. "A unified framework of five principles for AI in society."  Machine learning and the city: Applications in architecture and urban design (2022): 535-545. http://dx.doi.org/10.1162/99608f92.8cd550d1

Floridi and Cowls address the challenge of 'principle proliferation' in AI ethics, proposing a unified framework with five core principles: beneficence, non-maleficence, autonomy, justice, and explicability. They stress explicability, covering intelligibility and accountability, as crucial for ethical AI. A comparative analysis of six initiatives reveals coherence, forming a robust ethical foundation for laws and regulations. The paper contributes significantly to understanding foundational AI ethical principles globally, emphasizing the relevance of explicability in addressing accountability. While strong in theory, practical examples could enhance real-world applicability. The article is well-structured, acknowledging limitations and advocating for a diverse, global ethical framework, serving as a critical reference for shaping AI ethics.

Hermann, Isabella. "Artificial intelligence in fiction: between narratives and metaphors."  AI & society 38.1 (2023): 319-329. https://doi.org/10.1007/s00146-021-01299-6

Hermann's article talks about how science fiction (SF) shapes public perceptions of artificial intelligence (AI), particularly human-like AI, influencing developers, policymakers, and public acceptance. The analysis examines the tension between SF's portrayal of autonomous AI and its technological realities. Hermann advocates against literal interpretations, emphasizing SF's metaphorical role in societal reflection. The source enhances research by revealing SF's societal impact on AI perceptions and emphasizing the need for discernment in interpreting narratives. While Hermann's approach aligns with exploring diverse AI perspectives, including concrete examples or case studies illustrating real-world consequences could enhance the article's practical application. Overall, it contributes to understanding AI's societal discourse.

Hilb, Michael. "Toward artificial governance? The role of artificial intelligence in shaping the future of corporate governance."  Journal of Management and Governance 24 (2020): 851-870. https://link.springer.com/article/10.1007/s10997-020-09519-9

Hilb's article analyzes AI's impact on corporate governance across business, technology, and society lenses, proposing governance scenarios from assisted to autopoietic intelligence. It navigates decision-making functions, predicts decision types benefiting from AI, and envisions three horizons, exploring implications for board members. The article is comprehensive, integrating business, legal, and ethical dimensions, offering nuanced insights into AI-driven governance evolution. While clear and valuable, a more in-depth exploration of drawbacks or ethical concerns is suggested. Overall, Hilb's work is a useful and forward-looking resource, contributing significantly to comprehending AI's potential influence on corporate governance in diverse contexts.

Nassar, Ahmed, and Mostafa Kamal. "Ethical dilemmas in AI-powered decision-making: a deep dive into big data-driven ethical considerations."  International Journal of Responsible Artificial Intelligence 11.8 (2021): 1-11. https://neuralslate.com/index.php/Journal-of-Responsible-AI/article/view/43

Nassar and Kamal's article goes in to the ethical dilemmas in AI-powered decision-making, focusing on the impact of big data. The research uses a mixed-method approach to dissect specific dilemmas like algorithmic bias, transparency, and accountability. It extensively reviews existing literature, highlighting ethical frameworks such as deontology, utilitarianism, and virtue ethics, offering a comprehensive perspective. The study emphasizes the urgent need for ethical guidance in AI and big data, asserting lapses' practical implications and consequences. Evaluating this source, it stands as a robust contribution to understanding the ethical landscape of AI and big data, providing valuable insights for analyzing societal implications and guiding responsible development.

Peeters, Marieke MM, et al. "Hybrid collective intelligence in a human–AI society."  AI & society 36 (2021): 217-238. https://link.springer.com/article/10.1007/s00146-020-01005-y

Peeters et al. compares three perspectives on AI's societal impact: techno-centric, human-centric, and collective intelligence-centric. The article provides a detailed analysis, exploring AI's historical context and current applications. The authors propose a comprehensive research framework merging these perspectives to tackle societal challenges. The techno-centric outlook anticipates AI surpassing humans, emphasizing problem-solving potential. The human-centric perspective raises concerns about AI's negative effects on well-being and autonomy. The collective intelligence approach stresses collaborative human-AI interactions. This source enriches the AI discourse, aiding comprehension of diverse viewpoints and contributing to my research on AI's societal role, aligning with a multi-faceted analysis. The article stands out for its depth and thought-provoking insights.

Rawashdeh, Awni. "The consequences of artificial intelligence: an investigation into the impact of AI on job displacement in accounting."  Journal of Science and Technology Policy Management (2023). https://doi.org/10.1108/JSTPM-02-2023-0030

Rawashdeh's study looks over AI's integration in accounting, focusing on job displacement (JD). Through a cross-sectional survey of accountants, the research establishes a substantial correlation between increasing AI involvement and elevated JD rates. Beyond employment concerns, the study highlights the impact on decision-making, economic well-being, professional dynamics, and social structures. Despite recognizing limitations like sample size and survey biases, the study suggests future research directions. Urgent policy implications include talent retention, upskilling, and support mechanisms. The study's originality lies in its thorough exploration of AI's multi-dimensional impacts, contributing to academic discourse and policy formulation. This source adds empirical evidence and policy insights to the analysis, particularly regarding AI-induced job displacement in accounting.

Rodrigues, Rowena. "Legal and human rights issues of AI: Gaps, challenges and vulnerabilities."  Journal of Responsible Technology 4 (2020): 100005. https://doi.org/10.1016/j.jrt.2020.100005

Rodrigues scrutinizes legal and human rights issues tied to artificial intelligence (AI), offering a comprehensive overview and highlighting gaps, challenges, and human rights principles affected. Addressing topics like algorithmic transparency, cybersecurity vulnerabilities, bias, and discrimination, the article connects AI legal concerns with vulnerability. It maps these issues to human rights treaties and explores solutions, emphasizing contestability and legal personhood challenges and proposing actions to protect vulnerable groups. While acknowledging the legal discourse's progress, the article underscores the evolving nature of this regulatory landscape. The broad coverage, insightful analysis, and connection of AI issues to vulnerability make it a valuable resource for understanding and addressing AI's societal impact.

Sætra, Henrik Skaug, and Eduard Fosch-Villaronga. "Research in AI has Implications for Society: How do we Respond?." (2021). http://dx.doi.org/10.5771/2747-5182-2021-1-60

Sætra and Fosch-Villaronga's article delves into the societal implications of AI, emphasizing the necessity to harmonize scientific progress with ethical and political dimensions. Rejecting the conflation of science, ethics, and politics, the authors critique arguments favoring AI research restrictions, advocating for a nuanced approach that preserves distinct roles for each domain. Their insights will inform the analysis of AI development challenges and governance models. However, the article's theoretical nature may constrain its applicability to practical policy recommendations. Nevertheless, it explores the relationship between AI research, ethics, and political considerations.

Shneiderman, Ben. "Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems."  ACM Transactions on Interactive Intelligent Systems (TiiS) 10.4 (2020): 1-31. https://dl.acm.org/doi/abs/10.1145/3419764

Shneiderman's article addresses the challenge of bridging ethical principles with practical governance for Human-centered AI (HCAI) systems. The author proposes 15 recommendations across team, organization, and industry levels to enhance reliability, safety, and trustworthiness. The recommendations cover software engineering practices, safety culture, and trustworthy certification. Shneiderman advocates for audit trails, verification testing, and bias testing within teams, fostering a safety culture through leadership commitment and internal review boards, and establishing trustworthiness certification through government interventions, external audits, and collaboration with civil society organizations. The article's emphasis on practical guidelines strengthens its utility, providing valuable insights into addressing ethical concerns in AI development.

Tschang, Feichin Ted, and Esteve Almirall. "Artificial intelligence as augmenting automation: Implications for employment."  Academy of Management Perspectives 35.4 (2021): 642-659. https://doi.org/10.5465/amp.2019.0062

Examination happens by Tschang and Almirall's on the relationship between artificial intelligence (AI), automation, and employment. The authors analyze the ongoing debate about AI's impact on employment, discussing both the 'replacement' and 'augmentation' views. They explore how AI augments automation, influencing employment structures within digitally transformed firms. The article underscores the potential displacement of routine work and the emergence of employment structures favoring either nonroutine low-skilled or high-skilled tasks. Tschang and Almirall emphasize the dynamic effects of combining AI with other technologies, leading to economies of scale and scope for firms. The source is valuable as it enhances understanding of the complex dynamics between AI, automation, and employment structures, contributing to exploring AI's societal implications. The authors provide insightful perspectives on how organizations may adapt to AI-induced changes in the workforce, emphasizing the need for critical societal discussions and new models of socially-minded production.

Vinuesa, Ricardo, et al. "The role of artificial intelligence in achieving the Sustainable Development Goals."  Nature communications 11.1 (2020): 1-10. https://doi.org/10.1038/s41467-019-14108-y

Using expert elicitation, Vinuesa et al.'s article talks about the details of AI's impact on Sustainable Development Goals (SDGs). Positive AI influences on 134 SDG targets (82% in Society), notably poverty, education, and energy, are identified. Negative impacts on cultural values, energy consumption, and inequalities are acknowledged. Economic impacts are primarily positive but raise concerns about data-driven inequalities. Positive environmental contributions are noted, with concerns about energy consumption and misuse. This foundational source provides insights into AI's potential for SDGs, emphasizing societal, economic, and environmental aspects. Methodological rigor enhances reliability, though literature search limitations and potential bias exist.

Wamba-Taguimdje, Serge-Lopez, et al. "Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects."  Business Process Management Journal 26.7 (2020): 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411

Wamba-Taguimdje et al.'s study explores the impact of AI on firm performance, focusing on the business value derived from AI-based transformation projects. Utilizing a four-step approach and theory of IT capabilities, the research analyzes 500 case studies, highlighting AI's potential to optimize processes and enhance organizational performance in various sectors. Emphasizing a holistic view of AI as a combination of technologies, the study offers a valuable model for assessing AI's influence on organizational performance. The rich case study collection supports claims about AI's positive impact, making it a useful reference for researchers and practitioners seeking to understand and leverage the benefits of AI in improving firm performance.

Works Cited

Ahmad, Kashif, et al. "Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges."  Computer Science Review 43 (2022): 100452. https://doi.org/10.1016/j.cosrev.2021.100452

Chen, Tao, et al. "AI-based self-service technology in public service delivery: User experience and influencing factors."  Government Information Quarterly 38.4 (2021): 101520. http://dx.doi.org/10.1016/j.giq.2020.101520

Cheng, Lu, Kush R. Varshney, and Huan Liu. "Socially responsible ai algorithms: Issues, purposes, and challenges."  Journal of Artificial Intelligence Research 71 (2021): 1137-1181. http://dx.doi.org/10.1613/jair.1.12814

Floridi, Luciano, and Josh Cowls. "A unified framework of five principles for AI in society."  Machine learning and the city: Applications in architecture and urban design (2022): 535-545. http://dx.doi.org/10.1162/99608f92.8cd550d1

Hermann, Isabella. "Artificial intelligence in fiction: between narratives and metaphors."  AI & society 38.1 (2023): 319-329. https://doi.org/10.1007/s00146-021-01299-6

Hilb, Michael. "Toward artificial governance? The role of artificial intelligence in shaping the future of corporate governance."  Journal of Management and Governance 24 (2020): 851-870. https://link.springer.com/article/10.1007/s10997-020-09519-9

Nassar, Ahmed, and Mostafa Kamal. "Ethical dilemmas in AI-powered decision-making: a deep dive into big data-driven ethical considerations."  International Journal of Responsible Artificial Intelligence 11.8 (2021): 1-11. https://neuralslate.com/index.php/Journal-of-Responsible-AI/article/view/43

Peeters, Marieke MM, et al. "Hybrid collective intelligence in a human–AI society."  AI & society 36 (2021): 217-238. https://link.springer.com/article/10.1007/s00146-020-01005-y

Rawashdeh, Awni. "The consequences of artificial intelligence: an investigation into the impact of AI on job displacement in accounting."  Journal of Science and Technology Policy Management (2023). https://doi.org/10.1108/JSTPM-02-2023-0030

Rodrigues, Rowena. "Legal and human rights issues of AI: Gaps, challenges and vulnerabilities."  Journal of Responsible Technology 4 (2020): 100005. https://doi.org/10.1016/j.jrt.2020.100005

Sætra, Henrik Skaug, and Eduard Fosch-Villaronga. "Research in AI has Implications for Society: How do we Respond?." (2021). http://dx.doi.org/10.5771/2747-5182-2021-1-60

Shneiderman, Ben. "Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems."  ACM Transactions on Interactive Intelligent Systems (TiiS) 10.4 (2020): 1-31. https://dl.acm.org/doi/abs/10.1145/3419764

Tschang, Feichin Ted, and Esteve Almirall. "Artificial intelligence as augmenting automation: Implications for employment."  Academy of Management Perspectives 35.4 (2021): 642-659. https://doi.org/10.5465/amp.2019.0062

Vinuesa, Ricardo, et al. "The role of artificial intelligence in achieving the Sustainable Development Goals."  Nature communications 11.1 (2020): 1-10. https://doi.org/10.1038/s41467-019-14108-y

Wamba-Taguimdje, Serge-Lopez, et al. "Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects."  Business Process Management Journal 26.7 (2020): 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411

English 108 Writing Project #3: The formal proposal (with argumentative thesis)

In your future academic and professional careers, you will likely encounter some problem and be asked to develop a course of action to remedy it. After doing research, it is likely that you will be asked to develop a judgment and/or course of action (thesis) and arrange your research findings in a way that supports that judgment. Writing Project #3 asks you to do all of this in a 1500 word essay.

To complete this assignment:

1. Use your topic and controversy from Project 2 and continue to refine it.

2. If necessary, do more research about your topic and document that research in MLA works cited citations. It won’t be necessary to annotate these additional sources, but if you have time, it would be a good idea.

3. Write a 1500 word essay that presents your controversy and argues a thesis within that controversy.

Project 3 schedule

Week of 3.31: Academic thesis proposal Wed 4.3

Week of 4.7: First three paragraphs and thesis. 4.10

Week of 4.14: Rough Draft 4.15

Week of 4.21: Final draft 4.22

Week of 4.21: Presentations 4.26

3/8/2024

Annotated Bibliography

The role of Artificial Intelligence in Society, Benefits, Gaps, challenges and vulnerabilities

Ahmad, Kashif, et al. "Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges."  Computer Science Review 43 (2022): 100452. https://doi.org/10.1016/j.cosrev.2021.100452

Ahmad, Kashif, et al. extensively explore challenges in deploying AI in smart cities, focusing on security, data management, and ethics. They advocate integrating technology with thoughtful policies and responsible governance for human welfare. Discussing risks like biases and adversarial attacks, the article reviews existing literature, identifies limitations, and suggests future research directions. Emphasizing interconnected challenges underscores the need for a holistic approach. The source, marked by detailed analysis and real-world examples, proves highly valuable for understanding complex issues in AI's role in smart cities. Its quality lies in a well-structured presentation and insightful emphasis on challenges' interdependencies, contributing significantly to societal impact evaluation.

Chen, Tao, et al. "AI-based self-service technology in public service delivery: User experience and influencing factors."  Government Information Quarterly 38.4 (2021): 101520. http://dx.doi.org/10.1016/j.giq.2020.101520

Chen, Tao, et al.'s article dives into the user experience and influencing factors of AI-based self-service technology (SST) in public service delivery. The study, conducted in a Chinese administrative bureau, identifies personalization, aesthetics, and perceived time spent as crucial to user satisfaction. The research emphasizes the need for aesthetically appealing, personalized AI-based SSTs and underscores their positive impact on citizen experience. Trust in government is found to moderate this relationship, with citizens trusting the government more likely to have positive experiences. The study's practical implications advise public managers on optimizing SST design, fostering trust, and enhancing citizens' service experience. The research contributes to understanding AI-based SST user dynamics and offers valuable insights for government adoption and implementation.

Cheng, Lu, Kush R. Varshney, and Huan Liu. "Socially responsible AI algorithms: Issues, purposes, and challenges."  Journal of Artificial Intelligence Research 71 (2021): 1137-1181. http://dx.doi.org/10.1613/jair.1.12814

Cheng et al. examine the societal impact of AI algorithms, stressing the importance of responsible AI development. The introduction of Socially Responsible AI Algorithms (SRAs) establishes a comprehensive framework encompassing principles, means, and objectives. They argue for AI's adherence to functional and legal responsibilities and ethical and philanthropic obligations, as outlined in a pyramid model. The paper addresses challenges, including AI defenses, long-term effects, and the responsible AI gap in industry, advocating for interdisciplinary research and emphasizing the application of SRAs for societal benefit. Despite lacking specific examples, the article is a foundational resource for understanding and advancing socially responsible AI.

Floridi, Luciano, and Josh Cowls. "A unified framework of five principles for AI in society."  Machine learning and the city: Applications in architecture and urban design (2022): 535-545. http://dx.doi.org/10.1162/99608f92.8cd550d1

Floridi and Cowls address the challenge of 'principle proliferation' in AI ethics, proposing a unified framework with five core principles: beneficence, non-maleficence, autonomy, justice, and explicability. They stress explicability, covering intelligibility and accountability, as crucial for ethical AI. A comparative analysis of six initiatives reveals coherence, forming a robust ethical foundation for laws and regulations. The paper contributes significantly to understanding foundational AI ethical principles globally, emphasizing the relevance of explicability in addressing accountability. While strong in theory, practical examples could enhance real-world applicability. The article is well-structured, acknowledging limitations and advocating for a diverse, global ethical framework, serving as a critical reference for shaping AI ethics.

Hermann, Isabella. "Artificial intelligence in fiction: between narratives and metaphors."  AI & society 38.1 (2023): 319-329. https://doi.org/10.1007/s00146-021-01299-6

Hermann's article talks about how science fiction (SF) shapes public perceptions of artificial intelligence (AI), particularly human-like AI, influencing developers, policymakers, and public acceptance. The analysis examines the tension between SF's portrayal of autonomous AI and its technological realities. Hermann advocates against literal interpretations, emphasizing SF's metaphorical role in societal reflection. The source enhances research by revealing SF's societal impact on AI perceptions and emphasizing the need for discernment in interpreting narratives. While Hermann's approach aligns with exploring diverse AI perspectives, including concrete examples or case studies illustrating real-world consequences could enhance the article's practical application. Overall, it contributes to understanding AI's societal discourse.

Hilb, Michael. "Toward artificial governance? The role of artificial intelligence in shaping the future of corporate governance."  Journal of Management and Governance 24 (2020): 851-870. https://link.springer.com/article/10.1007/s10997-020-09519-9

Hilb's article analyzes AI's impact on corporate governance across business, technology, and society lenses, proposing governance scenarios from assisted to autopoietic intelligence. It navigates decision-making functions, predicts decision types benefiting from AI, and envisions three horizons, exploring implications for board members. The article is comprehensive, integrating business, legal, and ethical dimensions, offering nuanced insights into AI-driven governance evolution. While clear and valuable, a more in-depth exploration of drawbacks or ethical concerns is suggested. Overall, Hilb's work is a useful and forward-looking resource, contributing significantly to comprehending AI's potential influence on corporate governance in diverse contexts.

Nassar, Ahmed, and Mostafa Kamal. "Ethical dilemmas in AI-powered decision-making: a deep dive into big data-driven ethical considerations."  International Journal of Responsible Artificial Intelligence 11.8 (2021): 1-11. https://neuralslate.com/index.php/Journal-of-Responsible-AI/article/view/43

Nassar and Kamal's article goes in to the ethical dilemmas in AI-powered decision-making, focusing on the impact of big data. The research uses a mixed-method approach to dissect specific dilemmas like algorithmic bias, transparency, and accountability. It extensively reviews existing literature, highlighting ethical frameworks such as deontology, utilitarianism, and virtue ethics, offering a comprehensive perspective. The study emphasizes the urgent need for ethical guidance in AI and big data, asserting lapses' practical implications and consequences. Evaluating this source, it stands as a robust contribution to understanding the ethical landscape of AI and big data, providing valuable insights for analyzing societal implications and guiding responsible development.

Peeters, Marieke MM, et al. "Hybrid collective intelligence in a human–AI society."  AI & society 36 (2021): 217-238. https://link.springer.com/article/10.1007/s00146-020-01005-y

Peeters et al. compares three perspectives on AI's societal impact: techno-centric, human-centric, and collective intelligence-centric. The article provides a detailed analysis, exploring AI's historical context and current applications. The authors propose a comprehensive research framework merging these perspectives to tackle societal challenges. The techno-centric outlook anticipates AI surpassing humans, emphasizing problem-solving potential. The human-centric perspective raises concerns about AI's negative effects on well-being and autonomy. The collective intelligence approach stresses collaborative human-AI interactions. This source enriches the AI discourse, aiding comprehension of diverse viewpoints and contributing to my research on AI's societal role, aligning with a multi-faceted analysis. The article stands out for its depth and thought-provoking insights.

Rawashdeh, Awni. "The consequences of artificial intelligence: an investigation into the impact of AI on job displacement in accounting."  Journal of Science and Technology Policy Management (2023). https://doi.org/10.1108/JSTPM-02-2023-0030

Rawashdeh's study looks over AI's integration in accounting, focusing on job displacement (JD). Through a cross-sectional survey of accountants, the research establishes a substantial correlation between increasing AI involvement and elevated JD rates. Beyond employment concerns, the study highlights the impact on decision-making, economic well-being, professional dynamics, and social structures. Despite recognizing limitations like sample size and survey biases, the study suggests future research directions. Urgent policy implications include talent retention, upskilling, and support mechanisms. The study's originality lies in its thorough exploration of AI's multi-dimensional impacts, contributing to academic discourse and policy formulation. This source adds empirical evidence and policy insights to the analysis, particularly regarding AI-induced job displacement in accounting.

Rodrigues, Rowena. "Legal and human rights issues of AI: Gaps, challenges and vulnerabilities."  Journal of Responsible Technology 4 (2020): 100005. https://doi.org/10.1016/j.jrt.2020.100005

Rodrigues scrutinizes legal and human rights issues tied to artificial intelligence (AI), offering a comprehensive overview and highlighting gaps, challenges, and human rights principles affected. Addressing topics like algorithmic transparency, cybersecurity vulnerabilities, bias, and discrimination, the article connects AI legal concerns with vulnerability. It maps these issues to human rights treaties and explores solutions, emphasizing contestability and legal personhood challenges and proposing actions to protect vulnerable groups. While acknowledging the legal discourse's progress, the article underscores the evolving nature of this regulatory landscape. The broad coverage, insightful analysis, and connection of AI issues to vulnerability make it a valuable resource for understanding and addressing AI's societal impact.

Sætra, Henrik Skaug, and Eduard Fosch-Villaronga. "Research in AI has Implications for Society: How do we Respond?." (2021). http://dx.doi.org/10.5771/2747-5182-2021-1-60

Sætra and Fosch-Villaronga's article delves into the societal implications of AI, emphasizing the necessity to harmonize scientific progress with ethical and political dimensions. Rejecting the conflation of science, ethics, and politics, the authors critique arguments favoring AI research restrictions, advocating for a nuanced approach that preserves distinct roles for each domain. Their insights will inform the analysis of AI development challenges and governance models. However, the article's theoretical nature may constrain its applicability to practical policy recommendations. Nevertheless, it explores the relationship between AI research, ethics, and political considerations.

Shneiderman, Ben. "Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems."  ACM Transactions on Interactive Intelligent Systems (TiiS) 10.4 (2020): 1-31. https://dl.acm.org/doi/abs/10.1145/3419764

Shneiderman's article addresses the challenge of bridging ethical principles with practical governance for Human-centered AI (HCAI) systems. The author proposes 15 recommendations across team, organization, and industry levels to enhance reliability, safety, and trustworthiness. The recommendations cover software engineering practices, safety culture, and trustworthy certification. Shneiderman advocates for audit trails, verification testing, and bias testing within teams, fostering a safety culture through leadership commitment and internal review boards, and establishing trustworthiness certification through government interventions, external audits, and collaboration with civil society organizations. The article's emphasis on practical guidelines strengthens its utility, providing valuable insights into addressing ethical concerns in AI development.

Tschang, Feichin Ted, and Esteve Almirall. "Artificial intelligence as augmenting automation: Implications for employment."  Academy of Management Perspectives 35.4 (2021): 642-659. https://doi.org/10.5465/amp.2019.0062

Examination happens by Tschang and Almirall's on the relationship between artificial intelligence (AI), automation, and employment. The authors analyze the ongoing debate about AI's impact on employment, discussing both the 'replacement' and 'augmentation' views. They explore how AI augments automation, influencing employment structures within digitally transformed firms. The article underscores the potential displacement of routine work and the emergence of employment structures favoring either nonroutine low-skilled or high-skilled tasks. Tschang and Almirall emphasize the dynamic effects of combining AI with other technologies, leading to economies of scale and scope for firms. The source is valuable as it enhances understanding of the complex dynamics between AI, automation, and employment structures, contributing to exploring AI's societal implications. The authors provide insightful perspectives on how organizations may adapt to AI-induced changes in the workforce, emphasizing the need for critical societal discussions and new models of socially-minded production.

Vinuesa, Ricardo, et al. "The role of artificial intelligence in achieving the Sustainable Development Goals."  Nature communications 11.1 (2020): 1-10. https://doi.org/10.1038/s41467-019-14108-y

Using expert elicitation, Vinuesa et al.'s article talks about the details of AI's impact on Sustainable Development Goals (SDGs). Positive AI influences on 134 SDG targets (82% in Society), notably poverty, education, and energy, are identified. Negative impacts on cultural values, energy consumption, and inequalities are acknowledged. Economic impacts are primarily positive but raise concerns about data-driven inequalities. Positive environmental contributions are noted, with concerns about energy consumption and misuse. This foundational source provides insights into AI's potential for SDGs, emphasizing societal, economic, and environmental aspects. Methodological rigor enhances reliability, though literature search limitations and potential bias exist.

Wamba-Taguimdje, Serge-Lopez, et al. "Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects."  Business Process Management Journal 26.7 (2020): 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411

Wamba-Taguimdje et al.'s study explores the impact of AI on firm performance, focusing on the business value derived from AI-based transformation projects. Utilizing a four-step approach and theory of IT capabilities, the research analyzes 500 case studies, highlighting AI's potential to optimize processes and enhance organizational performance in various sectors. Emphasizing a holistic view of AI as a combination of technologies, the study offers a valuable model for assessing AI's influence on organizational performance. The rich case study collection supports claims about AI's positive impact, making it a useful reference for researchers and practitioners seeking to understand and leverage the benefits of AI in improving firm performance.

Works Cited

Ahmad, Kashif, et al. "Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges."  Computer Science Review 43 (2022): 100452. https://doi.org/10.1016/j.cosrev.2021.100452

Chen, Tao, et al. "AI-based self-service technology in public service delivery: User experience and influencing factors."  Government Information Quarterly 38.4 (2021): 101520. http://dx.doi.org/10.1016/j.giq.2020.101520

Cheng, Lu, Kush R. Varshney, and Huan Liu. "Socially responsible ai algorithms: Issues, purposes, and challenges."  Journal of Artificial Intelligence Research 71 (2021): 1137-1181. http://dx.doi.org/10.1613/jair.1.12814

Floridi, Luciano, and Josh Cowls. "A unified framework of five principles for AI in society."  Machine learning and the city: Applications in architecture and urban design (2022): 535-545. http://dx.doi.org/10.1162/99608f92.8cd550d1

Hermann, Isabella. "Artificial intelligence in fiction: between narratives and metaphors."  AI & society 38.1 (2023): 319-329. https://doi.org/10.1007/s00146-021-01299-6

Hilb, Michael. "Toward artificial governance? The role of artificial intelligence in shaping the future of corporate governance."  Journal of Management and Governance 24 (2020): 851-870. https://link.springer.com/article/10.1007/s10997-020-09519-9

Nassar, Ahmed, and Mostafa Kamal. "Ethical dilemmas in AI-powered decision-making: a deep dive into big data-driven ethical considerations."  International Journal of Responsible Artificial Intelligence 11.8 (2021): 1-11. https://neuralslate.com/index.php/Journal-of-Responsible-AI/article/view/43

Peeters, Marieke MM, et al. "Hybrid collective intelligence in a human–AI society."  AI & society 36 (2021): 217-238. https://link.springer.com/article/10.1007/s00146-020-01005-y

Rawashdeh, Awni. "The consequences of artificial intelligence: an investigation into the impact of AI on job displacement in accounting."  Journal of Science and Technology Policy Management (2023). https://doi.org/10.1108/JSTPM-02-2023-0030

Rodrigues, Rowena. "Legal and human rights issues of AI: Gaps, challenges and vulnerabilities."  Journal of Responsible Technology 4 (2020): 100005. https://doi.org/10.1016/j.jrt.2020.100005

Sætra, Henrik Skaug, and Eduard Fosch-Villaronga. "Research in AI has Implications for Society: How do we Respond?." (2021). http://dx.doi.org/10.5771/2747-5182-2021-1-60

Shneiderman, Ben. "Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems."  ACM Transactions on Interactive Intelligent Systems (TiiS) 10.4 (2020): 1-31. https://dl.acm.org/doi/abs/10.1145/3419764

Tschang, Feichin Ted, and Esteve Almirall. "Artificial intelligence as augmenting automation: Implications for employment."  Academy of Management Perspectives 35.4 (2021): 642-659. https://doi.org/10.5465/amp.2019.0062

Vinuesa, Ricardo, et al. "The role of artificial intelligence in achieving the Sustainable Development Goals."  Nature communications 11.1 (2020): 1-10. https://doi.org/10.1038/s41467-019-14108-y

Wamba-Taguimdje, Serge-Lopez, et al. "Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects."  Business Process Management Journal 26.7 (2020): 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411