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On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
Hoda Heidari · Vedant Nanda · Krishna Gummadi

Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #137

Most existing notions of algorithmic fairness are one-shot: they ensure some form of allocative equality at the time of decision making, but do not account for the adverse impact of the algorithmic decisions today on the long-term welfare and prosperity of certain segments of the population. We take a broader perspective on algorithmic fairness. We propose an effort-based measure of fairness and present a data-driven framework for characterizing the long-term impact of algorithmic policies on reshaping the underlying population. Motivated by the psychological literature on social learning and the economic literature on equality of opportunity, we propose a micro-scale model of how individuals may respond to decision-making algorithms. We employ existing measures of segregation from sociology and economics to quantify the resulting macro- scale population-level change. Importantly, we observe that different models may shift the group- conditional distribution of qualifications in different directions. Our findings raise a number of important questions regarding the formalization of fairness for decision-making models.

Author Information

Hoda Heidari (ETHZ)
Vedant Nanda (MPI-SWS)
Krishna Gummadi (MPI-SWS)

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