Bias and Discrimination in AI: a cross-disciplinary perspective
arXiv:2008.07309 · doi:10.1109/MTS.2021.3056293
Abstract
With the widespread and pervasive use of Artificial Intelligence (AI) for automated decision-making systems, AI bias is becoming more apparent and problematic. One of its negative consequences is discrimination: the unfair, or unequal treatment of individuals based on certain characteristics. However, the relationship between bias and discrimination is not always clear. In this paper, we survey relevant literature about bias and discrimination in AI from an interdisciplinary perspective that embeds technical, legal, social and ethical dimensions. We show that finding solutions to bias and discrimination in AI requires robust cross-disciplinary collaborations.
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- Unleashing the Power of AI. A Systematic Review of Cutting-Edge Techniques in AI-Enhanced Scientometrics, Webometrics, and Bibliometrics
- Healthcare Voice AI Assistants: Factors Influencing Trust and Intention to Use
- Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Review
- Circular Systems Engineering
- AI-AI Bias: large language models favor communications generated by large language models
- Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions
- Fairness in Forecasting of Observations of Linear Dynamical Systems
- Discovering and Interpreting Biased Concepts in Online Communities
- Wisdom of the Crowd, Without the Crowd: A Socratic LLM for Asynchronous Deliberation on Perspectivist Data
- On Explaining Proxy Discrimination and Unfairness in Individual Decisions Made by AI Systems