There is substantial evidence that Artificial Intelligence (AI) and Machine Learning (ML) algorithms can generate bias against minorities, women, and other protected classes. Federal and state laws have been enacted to protect consumers from discrimination in credit, housing, and employment, where regulators and agencies are tasked with enforcing these laws. Additionally, there are laws in place to ensure that consumers understand why they are denied access to services and products, such as consumer loans. In this article, we provide an overview of the potential benefits and risks associated with the use of algorithms and data, and focus specifically on fairness. While our observations generalize to many contexts, we focus on the fairness concerns raised in consumer credit and the legal requirements of the Equal Credit and Opportunity Act. We propose a methodology for evaluating algorithmic fairness and minimizing algorithmic bias that aligns with the provisions of federal and state anti-discrimination statutes that outlaw overt, disparate treatment, and, specifically, disparate impact discrimination. We argue that while the use of AI and ML algorithms heighten potential discrimination risks, these risks can be evaluated and mitigated, but doing so requires a deep understanding of these algorithms and the contexts and domains in which they are being used.

中文翻译：

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人工智能简介和算法判别问题的解决方案

有大量证据表明，人工智能 (AI) 和机器学习 (ML) 算法可能会对少数族裔、女性和其他受保护的阶层产生偏见。联邦和州法律已经颁布，以保护消费者免受信贷、住房和就业方面的歧视，监管机构和机构的任务是执行这些法律。此外，还制定了法律以确保消费者了解他们被拒绝获得服务和产品（例如消费贷款）的原因。在本文中，我们概述了与使用算法和数据相关的潜在收益和风险，并特别关注公平性。虽然我们的观察适用于许多情况，但我们关注的是消费者信贷中提出的公平问题以及《平等信贷和机会法案》的法律要求。我们提出了一种评估算法公平性和最小化算法偏见的方法，该方法与联邦和州反歧视法规的规定一致，这些法规禁止公开的、不同的待遇，特别是不同的影响歧视。我们认为，虽然人工智能和机器学习算法的使用增加了潜在的歧视风险，但可以评估和减轻这些风险，但这样做需要深入了解这些算法以及使用它们的背景和领域。