7. Literature
- Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2023). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
- This paper examines the ethical challenges and potential risks related to the development and use of large-scale language models, highlighting issues such as bias, unexpected outcomes and limitations in understanding context.
- Chouldechova, A., & Roth, A. (2023). A snapshot of the frontiers of fairness in machine learning. Communications of the ACM, 66(3), 30–38.
- The authors present an overview of current research on fairness in machine learning, highlighting various approaches, definitions and technical challenges in reducing bias.
- Cheong, B. C. (2024). Transparency and accountability in AI systems: Safeguarding wellbeing in the age of algorithmic decision-making. Frontiers in Human Dynamics.
- This review paper examines the main legal and ethical challenges related to implementing transparency and accountability in AI systems.
- Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707.
- This paper presents an ethical framework for the development and application of artificial intelligence aimed at socially beneficial outcomes, including the principles of transparency, fairness and accountability.
- UNESCO (2021). Recommendation on the ethics of artificial intelligence.
- The UNESCO Recommendation provides an international ethical framework for the development and use of artificial intelligence, emphasising human rights, transparency, privacy protection and the promotion of fairness.
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