AI Challenges for Society and Ethics
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Abstract (arXiv)
Artificial intelligence is already being applied in and impacting many important sectors in society, including healthcare, finance, and policing. These applications will increase as AI capabilities continue to progress, which has the potential to be highly beneficial for society, or to cause serious harm. The role of AI governance is ultimately to take practical steps to mitigate this risk of harm while enabling the benefits of innovation in AI. This requires answering challenging empirical questions about current and potential risks and benefits of AI: assessing impacts that are often widely distributed and indirect, and making predictions about a highly uncertain future. It also requires thinking through the normative question of what beneficial use of AI in society looks like, which is equally challenging. Though different groups may agree on high-level principles that uses of AI should respect (e.g., privacy, fairness, and autonomy), challenges arise when putting these principles into practice. For example, it is straightforward to say that AI systems must protect individual privacy, but there is presumably some amount or type of privacy that most people would be willing to give up to develop life-saving medical treatments. Despite these challenges, research can and has made progress on these questions. The aim of this chapter will be to give readers an understanding of this progress, and of the challenges that remain.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jun 2022.
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Citing papers per year: 2023: 2; 2024: 3.
5 papers.
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- 2024Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challenges16 Dec 2024
- 2024Beyond Algorithmic Fairness: A Guide to Develop and Deploy Ethical AI-Enabled Decision-Support Tools17 Sep 2024
- 2024Navigating LLM Ethics: Advancements, Challenges, and Future Directions14 May 2024
- 2023Model Explainability in Physiological and Healthcare-based Neural Networks3 Apr 2023
- 2023Auditing large language models: a three-layered approach16 Feb 2023