TIM-6074

Cheong, B. C. (2024). Transparency and accountability in AI systems: Safeguarding wellbeing in the age of algorithmic decision-making. Frontiers in Human Dynamics, 6, 1421273. https://www.frontiersin.org/articles/10.3389/fhumd.2024.1421273/full
This article reviews challenges related to AI transparency and accountability from the perspectives of users, providers, and regulators to inform responsible AI governance and protect individual and societal well-being.

Elliott, M. T. J., P, D., & MacCarthaigh, M. (2025). Evolving generative AI: Entangling the accountability relationship. Digital Government: Research and Practice, 6(1), 1–13. https://doi.org/10.1145/3664823
This paper investigates how the evolving capabilities of generative AI complexities the accountability relationship within socio-technical systems, potentially affecting public administrations.

Herrera-Poyatos, A., Ser, J. D., Prado, M. L. de, Wang, F.-Y., Herrera-Viedma, E., & Herrera, F. (2025). Responsible artificial intelligence systems: A roadmap to society’s trust through trustworthy AI, auditability, accountability, and governance (No. arXiv:2503.04739). arXiv. https://doi.org/10.48550/arXiv.2503.04739
This preprint outlines a roadmap for developing responsible AI systems, integrating trustworthy AI, auditability, accountability, and governance throughout the AI lifecycle to foster society's trust by addressing a wide range of ethical, legal, social, economic, and cultural issues.

Islam, M. M. (2024). Ethical considerations in AI: Navigating the complexities of bias and accountability. Journal of Artificial Intelligence General Science (JAIGS) ISSN: 3006-4023, 3(1), 2–30. http://newjaigs.com/index.php/JAIGS/article/view/62
This paper examines the crucial ethical considerations of bias and accountability in AI, detailing their manifestations and discussing strategies and frameworks needed for responsible AI development and deployment.

Mensah, G. B. (n.d.). Liability, accountability, and errors in AI telemedicine. Retrieved May 13, 2025, from https://www.researchgate.net/profile/George-Benneh-Mensah/publication/381600327_Liability_Accountability_and_Errors_in_AI_Telemedicine/links/66758cc38408575b837d2f75/Liability-Accountability-and-Errors-in-AI-Telemedicine.pdf
This preprint scrutinizes the existing healthcare regulatory environment regarding its ability to adequately address the complexities of liability, accountability, and error management introduced by AI integration in telemedicine.

Novelli, C., Taddeo, M., & Floridi, L. (2024). Accountability in artificial intelligence: What it is and how it works. AI & SOCIETY, 39(4), 1871–1882. https://doi.org/10.1007/s00146-023-01635-y
This article provides a conceptual definition of accountability in AI as a relation of answerability, dissecting its structure through seven features and four goals while acknowledging the challenges posed by AI's opacity and its nature as a sociotechnical system.

Simuni, G. (2024). Explainable AI in ml: The path to transparency and accountability. International Journal of Recent Advances In. https://www.ijramr.com/sites/default/files/issues-pdf/5590.pdf
This paper advocates for Explainable Artificial Intelligence (XAI) as a method to enhance transparency and accountability in machine learning by improving interpretability and building trust.

Staszkiewicz, P., Horobiowski, J., Szelągowska, A., & Strzelecka, A. M. (2024). Artificial intelligence legal personality and accountability: Auditors’ accounts of capabilities and challenges for instrument boundary. Meditari Accountancy Research, 32(7), 120–146. https://doi.org/10.1108/MEDAR-10-2023-2204
This study explores the arguments for AI legal personality and proposes a model of joint accountability focused on the human beneficiary, emphasizing the need for human-understandable AI decision-reasoning capabilities.

Yanisky-Ravid, S. (2024). Generating rembrandt: Artificial intelligence, copyright, and accountability in the 3A era-the human-like authors are already here-a new model. Trademark Rep., 114, 998. https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/thetmr114§ion=41
This article proposes an "AI Work Made for Hire" model to establish accountability for creative works generated by autonomous AI systems, placing ownership, control, and responsibility with the humans or legal entities utilizing the AI.