AF: Medium: Collaborative Research: Foundations of Fair Data Analysis

机器学习算法越来越多地做出或告知影响人们日常生活的关键决策。例如，算法做出关于招聘、大学招生、信用卡和抵押贷款批准、对被监禁的第一反应人员的判决和假释，以及用户在互联网上看到什么广告和搜索结果的决定。一个吸引人的特点是，这些算法在做出这些决策时可以高效地处理大量数据，从而有望提高经济和社会效益。因为这样的决定是如此重要，它们的公平性已经成为一个越来越令人担忧的问题。有人争辩说，自动化通过去除人的因素来保证公平，但事实并非如此--几项实证研究表明，自动化不是万能的。此外，不公平和歧视的原因可能是复杂和不明显的。本项目将研究算法决策中可能导致不公平的摩擦，以及缓解不公平的成本--即公平与其他期望数据之间的定量权衡，包括准确性、计算效率和经济效率。具体地说，本项目将研究几个因素对公平的摩擦。可能没有关于少数族裔人口的足够数据。可能会出现反馈循环，因为只有在采取了危险的行动时，才能对个人进行观察，例如，该人被授予贷款或被聘用。决策者可能目光短浅，选择将短期收益最大化，而不是探索风险更高、可能会在长期内获得回报的选择。经济摩擦包括自我确认的均衡-对机会的不同主观认知导致个人和社区做出维持这些认知的选择，以及分类者(例如信贷机构)之间的竞争导致均衡中不太准确的限定词。最后，找到公平和准确的分类器的问题在计算上可能是棘手的。这个项目将寻求方法来缓解这些摩擦造成的不公平。它将研究激励近视代理人的成本，以探索和检查此类激励的短期成本，以及它们对公平的长期影响。它还将寻求设计易于计算的分类器，以实现公平和准确的可证明的良好近似。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Machine learning algorithms increasingly make or inform critical decisions that affect peoples' every day lives. For instance, algorithms make decisions pertaining to hiring, college admissions, credit card and mortgage approvals, sentencing and parole of the incarcerated, first-responder deployment, and what advertisements and search results a user sees on the internet. An attractive feature is that these algorithms can efficiently process large amounts of data in making these decisions, thus hopefully improving economic and social efficiency. Because such decisions are so consequential, their fairness has become a matter of increasing concern. It has been argued that automation, by removing the human element, guarantees fairness, but this is not so -- several empirical studies have demonstrated that automation is no panacea. Further, the reasons for unfairness and discrimination can be complex and non-obvious. This project will study the frictions that may cause unfairness in algorithmic decision making, and the costs of mitigating unfairness -- that is, quantitative trade-offs between fairness and other desiderata, including accuracy, computational efficiency, and economic efficiency.Specifically, this project will study frictions to fairness arising from several factors. There may not be sufficient data about minority populations. There can be feedback loops arising from the fact that observations can only be made on an individual if a risky action is taken, e.g., the person is granted a loan, or hired. Decision makers can be myopic, choosing to maximize short-term gains rather than exploring riskier options that may pay off in the long run. Economic frictions include self-confirming equilibria---differing subjective perceptions of opportunities leading to choices by individuals and communities which sustain those perceptions, and competition among classifiers (for example, credit agencies) leading to less accurate qualifiers in equilibrium. Finally, the problem of finding fair and accurate classifiers can be computationally intractable. This project will seek ways to mitigate the unfairness arising from these frictions. It will study the cost of incentivizing myopic agents to explore and examine the short-term costs of such incentives, and their long-term impact on fairness. It will also seek to design computationally tractable classifiers that achieve provably good approximations for fairness and accuracy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.