Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities
It synthesizes fractured literature for researchers and practitioners, but is incremental as it reviews and connects existing work without new empirical results.
The paper compares algorithmic fairness and ethical decision making in automated systems, exploring their normative concerns and potential cross-utility to address harms in decision-making algorithms.
As automated decision making and decision assistance systems become common in everyday life, research on the prevention or mitigation of potential harms that arise from decisions made by these systems has proliferated. However, various research communities have independently conceptualized these harms, envisioned potential applications, and proposed interventions. The result is a somewhat fractured landscape of literature focused generally on ensuring decision-making algorithms "do the right thing". In this paper, we compare and discuss work across two major subsets of this literature: algorithmic fairness, which focuses primarily on predictive systems, and ethical decision making, which focuses primarily on sequential decision making and planning. We explore how each of these settings has articulated its normative concerns, the viability of different techniques for these different settings, and how ideas from each setting may have utility for the other.