How Do We Create a Fair AI System? Algorithms and Label Bias
An AI tool can be accurate and still be unfair when it predicts a convenient label rather than the outcome a decision requires. This technique helps practitioners compare the ideal and actual target, test who may be disadvantaged, and decide whether to retrain, redesign, restrict or stop an internal tool (Chicago Booth Center for Applied AI, 2021).
Technique Overview
How Do We Create a Fair AI System? Algorithms and Label Bias Definition
Label choice bias is the distortion created when the measurable label used to train or evaluate an algorithm does not adequately represent the ideal outcome needed for the decision. A tool may predict cost, attendance, previous selection, manager ratings or system activity while practitioners actually care about need, capability, risk or benefit. Because the chosen label may act as a proxy that encodes historic inequality, accurate prediction of it may still produce unfair decisions (Chicago Booth Center for Applied AI, 2021; Favier et al., 2023).
How Do We Create a Fair AI System? Algorithms and Label Bias Description *
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Further Reading
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How Do We Create a Fair AI System? Algorithms and Label Bias references (4 of up to 20) *
- Barocas, S., Hardt, M. and Narayanan, A. (2023) Fairness and Machine Learning: Limitations and Opportunities. Cambridge, MA: MIT Press.
- Chicago Booth Center for Applied AI (2021) Algorithmic Bias Playbook. Available at: www.chicagobooth.edu/-/media/project/chicago-booth/centers/caai/docs/algorithmic-bias-playbook-june-2021.pdf
- Department for Science, Innovation and Technology (DSIT) (2024) Responsible AI in Recruitment. Available at: www.gov.uk/government/publications/responsible-ai-in-recruitment-guide/responsible-ai-in-recruitment
- European Union (2024) Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence. Available at: eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
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