Towards a Standard for Identifying and Managing Bias in Artificial Intelligence
By Reva Schwartz, Apostol T. Vassilev, Kristen Greene, Lori A. Perine, Andrew Burt, Patrick Hall. 2022
concert are a panacea against bias and each brings its own set of pitfalls. What is missing from current remedies is guidance from a broader SOCIO - TECHNICAL perspective that connects these practices to societal values. Experts in the area of Trustworthy and Responsible AI counsel that to successfully manage the risks of AI bias we must operationalize these values and create new norms around how AI is built and deployed. This document, and work by the National Institute of Standards and Technology (NIST) in the area of AI bias, is based on a socio-technical perspective.
Subfields: Safety, Robustness & Interpretability
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Citations in the corpus
- Evaluating the Social Impact of Generative AI Systems in Systems and Society (2023, background): “Categories of bias, from system to human to statistical, interact with each other and are intertwined [211].”
- The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review (2024, background): “These facets include societal, population, representative, aggregation, feedback, and reuse biases throughout the machine-learning lifecycle (Schwartz et al., 2022).”
- Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies (2023, background): “A study by the National Institute of Standards and Technology (NIST) found that facial recognition technology was significantly less accurate for people with darker skin tones, leading to higher rates of false positives [16].”
- Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (2022, background): “Accord-ingly, individual scholarly analyses rarely capture the wide scope of harms that can arise from a given algorithmic system, which is a kind of anticipation work required for developing trustworthy and Responsible AI (see: [194]).”