How to spot and quantify hidden biases in machine learning data
In short: Building fair AI requires more than good intentions; it demands a rigorous audit of the underlying data. This video explores how to identify sources of bias—from historical injustices to proxy variables—using specific quantitative methods.
The video provides a framework for identifying potential biases that can lead to unfair machine learning models. It focuses on three primary types of issues: unbalanced samples, historical injustice, and the use of proxy variables. To move beyond intuition, it introduces technical metrics such as prevalence, mutual information, and feature importance scores to measure these inequalities. By mastering these tools, developers can actively promote inclusivity within AI systems.
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Sources
- Summary by Intellect · source: Exploratory Fairness Analysis | Quantifying Unfairness in Data
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