Fairness Incentives for Myopic Agents

By Sampath Kannan, Michael Kearns, Jamie H. Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu. 2017

47 citations recorded by the source corpus.

We consider settings in which we wish to incentivize myopic agents (such as Airbnb landlords, who may emphasize short-term profits and property safety) to treat arriving clients fairly, in order to prevent overall discrimination against individuals or groups. We model such settings in both classical and contextual bandit models in which the myopic agents maximize rewards according to current empirical averages, but are also amenable to exogenous payments that may cause them to alter their choices. Our notion of fairness asks that more qualified individuals are never (probabilistically) preferred over less qualifie ones [8]. We investigate whether it is possible to design inexpensive subsidy or payment schemes for a principal to motivate myopic agents to play fairly in all or almost all rounds. When the principal has full information about the state of the myopic agents, we show it is possible to induce fair play on every round with a subsidy scheme of total cost o(T) (for the classic setting with k arms, ~{O}(\sqrtk3T), and for the d-dimensional linear contextual setting ~{O}(d\sqrtk3T)). If the principal has much more limited information (as might often be the case for an external regulator or watchdog), and only observes the number of rounds in which members from each of the k groups were selected, but not the empirical estimates maintained by the myopic agent, the design of such a scheme becomes more complex. We show both positive and negative results in the classic and linear bandit settings by upper and lower bounding the cost of fair subsidy schemes.

Subfields: Management Science and Operations Research, Marketing

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Citations in the corpus

The Price of Fair PCA: One Extra Dimension (2018, background): “Much of the other work on fairness for learning algorithms focuses on fairness in classiﬁcation or scoring [Dwork et al., 2012; Hardt et al., 2016; Kleinberg et al., 2016; Chouldechova, 2017], or online learning settings [Joseph et al., 2016; Kannan et al., 2017; Ensign et al., 2017b,a].”
The Frontiers of Fairness in Machine Learning (2018, background): “[KKM + 17] takes a preliminary stab at this task, showing how to incentivize a particular notion of individual fairness in a simple, stylized setting, using small monetary payments.”
Runaway Feedback Loops in Predictive Policing (2017, background): “In addition, it relates to the narrower question of defining notions of fairness in sequential learning settings such as the setting of bandits (regular, contextual and linear) and Markov decision processes[6, 5, 3, 4].”
Downstream Effects of Affirmative Action (2018, background): “Finally, [11] studied the regulator’s problem of providing financial incentives for a lender to satisfy fairness constraints in an online classification setting.”
On preserving non-discrimination when combining expert advice (2018, background): “One line of work focuses on imposing a particular fairness guarantee at all times for bandits and contextual bandits, either for individual fairness [22, 23] or for group fairness [9].”