Module IV·Article II·~1 min read
Data Ethics and Algorithmic Fairness
Ethics at the Cutting Edge
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Data as Power
Data are not neutral. They are collected for specific purposes, stored by specific entities, and used in specific contexts. Three key ethical questions: consent, transparency, fairness.
Predictive policing algorithms (PredPol): trained on historical arrest data—which reflects existing biases. Result: increased patrols in areas with historically more arrests → more arrests → more “confirmation” for the algorithm. A self-reinforcing biased cycle.
Fairness through Justice
What does a “fair algorithm” mean? Mathematically incompatible definitions of fairness: equal accuracy for all groups vs equal false positive rates vs equal false negative rates. It is impossible to optimize all at the same time.
Question for reflection: Does your organization use algorithms to make decisions about people? Who bears responsibility for their bias?
§ Act · what next