Fair Participation via Sequential Policies

Leading approaches to algorithmic fairness and policy-induced distribution shift are often misaligned with long-term objectives in sequential settings. We aim to correct these shortcomings by ensuring that both the objective and fairness constraints account for policy-induced distribution shift. First, we motivate this problem using an example in which individuals subject to algorithmic predictions modulate their willingness to participate with the policy maker. Fairness in this example is measured by the variance of group participation rates. Next, we develop a method for solving the resulting constrained, non-linear optimization problem and prove that this method converges to a fair, locally optimal policy given first-order information. Finally, we experimentally validate our claims in a semi-synthetic setting.

Authors

• Reilly Raab University of California, Santa Cruz
• Ross Boczar University of Washington
• Maryam Fazel University of Washington
• Yang Liu College of Computer Science and Software Engineering, Shenzhen University

Keywords

• ML: Ethics, Bias, and Fairness
• PEAI: Bias, Fairness & Equity