Paper Session 2
AbstractAsk a Question?
Corporate Social Responsibility via Multi-Armed Bandits
We propose a multi-armed bandit setting where each arm corresponds to a subpopulation, and pulling an arm is equivalent to granting an opportunity to this subpopulation. In this setting the decision-maker's fairness policy governs the number of opportunities each subpopulation should receive, which typically depends on the (unknown) reward from granting an opportunity to this subpopulation. The decision-maker can decide whether to provide these opportunities, or pay a pre-defined monetary value for every withheld opportunity. The decision-maker's objective is to maximize her utility, which is the sum of rewards minus the cost paid for withheld opportunities. We provide a no-regret algorithm that maximizes the decision-maker's utility and complement our analysis with an almost-tight lower bound. Finally, we discuss the fairness policy and demonstrate its downstream implications on the utility and opportunities via simulations.
Building and Auditing Fair Algorithms: A Case Study in Candidate Screening
Academics, activists, and regulators are increasingly urging companies to develop and deploy sociotechnical systems that are fair and unbiased. Achieving this goal, however, is complex: the developer must (1) deeply engage with social and legal facets of “fairness” in a given context, (2) develop software that concretizes these values, and (3) undergo an independent algorithm audit to ensure technical correctness and social accountability of their algorithms. To date, there are few examples of companies that have transparently undertaken all three steps. In this paper we outline a framework for algorithmic auditing by way of a case-study of pymetrics, a startup that uses machine learning to recommend job candidates to their clients. We discuss how pymetrics approaches the question of fairness given the constraints of ethical, regulatory, and client demands, and how pymetrics’ software implements adverse impact testing. We also present the results of an independent audit of pymetrics’ candidate screening tool. We conclude with recommendations on how to structure audits to be practical, independent, and constructive, so that companies have better incentive to participate in third party audits, and that watchdog groups can be better prepared to investigate companies.
When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces
Algorithmic recommendations mediate interactions between millions of customers and products (in turn, their producers and sellers) on large e-commerce marketplaces like Amazon. In recent years, the producers and sellers have raised concerns about the fairness of black-box recommendation algorithms deployed on these marketplaces. Many complaints are centered around marketplaces biasing the algorithms to preferentially favor their own 'private label' products over competitors. These concerns are exacerbated as marketplaces increasingly de-emphasize or replace 'organic' recommendations with ad-driven 'sponsored' recommendations, which include their own private labels. While these concerns have been covered in popular press and have spawned regulatory investigations, to our knowledge, there has not been any public audit of these marketplace algorithms. In this study, we bridge this gap by performing an end-to-end systematic audit of related item recommendations on Amazon. We propose a network-centric framework to quantify and compare the biases across organic and sponsored related item recommendations. Along a number of our proposed bias measures, we find that the sponsored recommendations are significantly more biased toward Amazon private label products compared to organic recommendations. While our findings are primarily interesting to producers and sellers on Amazon, our proposed bias measures are generally useful for measuring link formation bias in any social or content networks.