"Wrong side of the tracks": Big Data and Protected Categories

By Simon DeDeo. 2014

16 citations recorded by the source corpus.

When we use machine learning for public policy, we find that many useful variables are associated with others on which it would be ethically problematic to base decisions. This problem becomes particularly acute in the Big Data era, when predictions are often made in the absence of strong theories for underlying causal mechanisms. We describe the dangers to democratic decision-making when high-performance algorithms fail to provide an explicit account of causation. We then demonstrate how information theory allows us to degrade predictions so that they decorrelate from protected variables with minimal loss of accuracy. Enforcing total decorrelation is at best a near-term solution, however. The role of causal argument in ethical debate urges the development of new, interpretable machine-learning algorithms that reference causal mechanisms.

Subfields: Artificial Intelligence, Safety Research, AI Ethics, Fairness & Society

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Citations in the corpus

Counterfactual Fairness (2017, methodology): “We describe how techniques from causal inference can be effective tools for designing fair algorithms and argue, as in DeDeo [9], that it is essential to properly address causality in fairness.”