We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in some group, while maintaining utility for the classifier (the university). The main conceptual contribution of this paper is a framework for fair classification comprising (1) a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand; (2) an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly. We also present an adaptation of our approach to achieve the complementary goal of "fair affirmative action," which guarantees statistical parity (i.e., the demographics of the set of individuals receiving any classification are the same as the demographics of the underlying population), while treating similar individuals as similarly as possible. Finally, we discuss the relationship of fairness to privacy: when fairness implies privacy, and how tools developed in the context of differential privacy may be applied to fairness.
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We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group are available, we show how to optimally adjust any learned predictor so as to remove discrimination according to our definition. Our framework also improves incentives by shifting the cost of poor classification from disadvantaged groups to the decision maker, who can respond by improving the classification accuracy.In line with other studies, our notion is oblivious: it depends only on the joint statistics of the predictor, the target and the protected attribute, but not on interpretation of individual features. We study the inherent limits of defining and identifying biases based on such oblivious measures, outlining what can and cannot be inferred from different oblivious tests.We illustrate our notion using a case study of FICO credit scores.
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Omnipredictors(Gopalan,Kalai,Reingold,Sharan和Wieder ITCS 2021)的概念提出了一种新的损失最小化范式。与损失损失$ c $相比,无需基于已知的损失功能学习预测指标,而是可以轻松地进行后处理以最大程度地减少任何丰富的损失功能家族。已经表明,这种杂手已经存在,并暗示(对于所有凸和Lipschitz损失函数),通过算法公平文献的多核概念的概念。然而,通常情况下,所选的动作必须遵守一些其他约束(例如能力或奇偶校验约束)。总体而言,全能器的原始概念并不适用于这种良好动机和大量研究的损失最小化的背景。在本文中,我们介绍了综合器,以进行约束优化并研究其复杂性和含义。我们介绍的概念使学习者不知道后来将分配的损失函数以及后来将施加的约束,只要已知用于定义这些约束的亚群的范围。该论文显示了如何依靠适当的多核变体获得限制优化问题的全能器。对于一些有趣的约束和一般损失函数以及一般约束和一些有趣的损失函数,我们显示了如何通过多核的变体隐含的,该变体的复杂性与标准的多核电相似。我们证明,在一般情况下,标准的数学启动不足,表明全能器是通过相对于包含$ c $中所有级别假设集的类的多核算来暗示的。我们还研究了约束是群体公平概念时的含义。
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We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the proportion in the population as a whole), and individual fairness (similar individuals should be treated similarly). We formulate fairness as an optimization problem of finding a good representation of the data with two competing goals: to encode the data as well as possible, while simultaneously obfuscating any information about membership in the protected group. We show positive results of our algorithm relative to other known techniques, on three datasets. Moreover, we demonstrate several advantages to our approach. First, our intermediate representation can be used for other classification tasks (i.e., transfer learning is possible); secondly, we take a step toward learning a distance metric which can find important dimensions of the data for classification.
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Recent discussion in the public sphere about algorithmic classification has involved tension between competing notions of what it means for a probabilistic classification to be fair to different groups. We formalize three fairness conditions that lie at the heart of these debates, and we prove that except in highly constrained special cases, there is no method that can satisfy these three conditions simultaneously. Moreover, even satisfying all three conditions approximately requires that the data lie in an approximate version of one of the constrained special cases identified by our theorem. These results suggest some of the ways in which key notions of fairness are incompatible with each other, and hence provide a framework for thinking about the trade-offs between them.
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Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with. Data is frequently imperfect in ways that allow these algorithms to inherit the prejudices of prior decision makers. In other cases, data may simply reflect the widespread biases that persist in society at large. In still others, data mining can discover surprisingly useful regularities that are really just preexisting patterns of exclusion and inequality. Unthinking reliance on data mining can deny historically disadvantaged and vulnerable groups full participation in society. Worse still, because the resulting discrimination is almost always an unintentional emergent property of the algorithm's use rather than a conscious choice by its programmers, it can be unusually hard to identify the source of the problem or to explain it to a court. This Essay examines these concerns through the lens of American antidiscrimination law-more particularly, through Title
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在高赌注域中的机器学习工具的实际应用通常被调节为公平,因此预测目标应该满足相对于受保护属性的奇偶校验的一些定量概念。然而,公平性和准确性之间的确切权衡并不完全清楚,即使是对分类问题的基本范式也是如此。在本文中,我们通过在任何公平分类器的群体误差之和中提供较低的界限,在分类设置中表征统计奇偶校验和准确性之间的固有权衡。我们不可能的定理可以被解释为公平的某种不确定性原则:如果基本率不同,那么符合统计奇偶校验的任何公平分类器都必须在至少一个组中产生很大的错误。我们进一步扩展了这一结果,以便在学习公平陈述的角度下给出任何(大约)公平分类者的联合误差的下限。为了表明我们的下限是紧张的,假设Oracle访问贝叶斯(潜在不公平)分类器,我们还构造了一种返回一个随机分类器的算法,这是最佳和公平的。有趣的是,当受保护的属性可以采用超过两个值时,这个下限的扩展不承认分析解决方案。然而,在这种情况下,我们表明,通过解决线性程序,我们可以通过解决我们作为电视 - 重心问题的术语,电视距离的重心问题来有效地计算下限。在上面,我们证明,如果集团明智的贝叶斯最佳分类器是关闭的,那么学习公平的表示导致公平的替代概念,称为准确性奇偶校验,这使得错误率在组之间关闭。最后,我们还在现实世界数据集上进行实验,以确认我们的理论发现。
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公平性是确保机器学习(ML)预测系统不会歧视特定个人或整个子人群(尤其是少数族裔)的重要要求。鉴于观察公平概念的固有主观性,文献中已经引入了几种公平概念。本文是一项调查,说明了通过大量示例和场景之间的公平概念之间的微妙之处。此外,与文献中的其他调查不同,它解决了以下问题:哪种公平概念最适合给定的现实世界情景,为什么?我们试图回答这个问题的尝试包括(1)确定手头现实世界情景的一组与公平相关的特征,(2)分析每个公平概念的行为,然后(3)适合这两个元素以推荐每个特定设置中最合适的公平概念。结果总结在决策图中可以由从业者和政策制定者使用,以导航相对较大的ML目录。
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基于AI和机器学习的决策系统已在各种现实世界中都使用,包括医疗保健,执法,教育和金融。不再是牵强的,即设想一个未来,自治系统将推动整个业务决策,并且更广泛地支持大规模决策基础设施以解决社会最具挑战性的问题。当人类做出决定时,不公平和歧视的问题普遍存在,并且当使用几乎没有透明度,问责制和公平性的机器做出决定时(或可能会放大)。在本文中,我们介绍了\ textit {Causal公平分析}的框架,目的是填补此差距,即理解,建模,并可能解决决策设置中的公平性问题。我们方法的主要见解是将观察到数据中存在的差异的量化与基本且通常是未观察到的因果机制收集的因果机制的收集,这些机制首先会产生差异,挑战我们称之为因果公平的基本问题分析(FPCFA)。为了解决FPCFA,我们研究了分解差异和公平性的经验度量的问题,将这种变化归因于结构机制和人群的不同单位。我们的努力最终达到了公平地图,这是组织和解释文献中不同标准之间关系的首次系统尝试。最后,我们研究了进行因果公平分析并提出一本公平食谱的最低因果假设,该假设使数据科学家能够评估不同影响和不同治疗的存在。
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业务分析(BA)的广泛采用带来了财务收益和提高效率。但是,当BA以公正的影响为决定时,这些进步同时引起了人们对法律和道德挑战的不断增加。作为对这些关注的回应,对算法公平性的新兴研究涉及算法输出,这些算法可能会导致不同的结果或其他形式的对人群亚组的不公正现象,尤其是那些在历史上被边缘化的人。公平性是根据法律合规,社会责任和效用是相关的;如果不充分和系统地解决,不公平的BA系统可能会导致社会危害,也可能威胁到组织自己的生存,其竞争力和整体绩效。本文提供了有关算法公平的前瞻性,注重BA的评论。我们首先回顾有关偏见来源和措施的最新研究以及偏见缓解算法。然后,我们对公用事业关系的详细讨论进行了详细的讨论,强调经常假设这两种构造之间经常是错误的或短视的。最后,我们通过确定企业学者解决有效和负责任的BA的关键的有影响力的公开挑战的机会来绘制前进的道路。
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我们给出了第一个多项式算法来估计$ d $ -variate概率分布的平均值,从$ \ tilde {o}(d)$独立的样本受到纯粹的差异隐私的界限。此问题的现有算法无论是呈指数运行时间,需要$ \ OMEGA(D ^ {1.5})$样本,或仅满足较弱的集中或近似差分隐私条件。特别地,所有先前的多项式算法都需要$ d ^ {1+ \ omega(1)} $ samples,以保证“加密”高概率,1-2 ^ { - d ^ {\ omega(1) $,虽然我们的算法保留$ \ tilde {o}(d)$ SAMPS复杂性即使在此严格设置中也是如此。我们的主要技术是使用强大的方块方法(SOS)来设计差异私有算法的新方法。算法的证据是在高维算法统计数据中的许多近期作品中的一个关键主题 - 显然需要指数运行时间,但可以通过低度方块证明可以捕获其分析可以自动变成多项式 - 时间算法具有相同的可证明担保。我们展示了私有算法的类似证据现象:工作型指数机制的实例显然需要指数时间,但可以用低度SOS样张分析的指数时间,可以自动转换为多项式差异私有算法。我们证明了捕获这种现象的元定理,我们希望在私人算法设计中广泛使用。我们的技术还在高维度之间绘制了差异私有和强大统计数据之间的新连接。特别是通过我们的校验算法镜头来看,几次研究的SOS证明在近期作品中的算法稳健统计中直接产生了我们差异私有平均估计算法的关键组成部分。
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近年来,解决机器学习公平性(ML)和自动决策的问题引起了处理人工智能的科学社区的大量关注。已经提出了ML中的公平定义的一种不同的定义,认为不同概念是影响人口中个人的“公平决定”的不同概念。这些概念之间的精确差异,含义和“正交性”尚未在文献中完全分析。在这项工作中,我们试图在这个解释中汲取一些订单。
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We study critical systems that allocate scarce resources to satisfy basic needs, such as homeless services that provide housing. These systems often support communities disproportionately affected by systemic racial, gender, or other injustices, so it is crucial to design these systems with fairness considerations in mind. To address this problem, we propose a framework for evaluating fairness in contextual resource allocation systems that is inspired by fairness metrics in machine learning. This framework can be applied to evaluate the fairness properties of a historical policy, as well as to impose constraints in the design of new (counterfactual) allocation policies. Our work culminates with a set of incompatibility results that investigate the interplay between the different fairness metrics we propose. Notably, we demonstrate that: 1) fairness in allocation and fairness in outcomes are usually incompatible; 2) policies that prioritize based on a vulnerability score will usually result in unequal outcomes across groups, even if the score is perfectly calibrated; 3) policies using contextual information beyond what is needed to characterize baseline risk and treatment effects can be fairer in their outcomes than those using just baseline risk and treatment effects; and 4) policies using group status in addition to baseline risk and treatment effects are as fair as possible given all available information. Our framework can help guide the discussion among stakeholders in deciding which fairness metrics to impose when allocating scarce resources.
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现有的制定公平计算定义的努力主要集中在平等的分布概念上,在这种情况下,平等是由系统中给出的资源或决策定义的。然而,现有的歧视和不公正通常是社会关系不平等的结果,而不是资源分配不平等。在这里,我们展示了对公平和平等的现有计算和经济定义的优化,无法防止不平等的社会关系。为此,我们提供了一个在简单的招聘市场中具有自我融合平衡的示例,该市场在关系上不平等,但满足了现有的公平分布概念。在此过程中,我们引入了公然的关系不公平的概念,对完整信息游戏进行了讨论,并讨论了该定义如何有助于启动一种将关系平等纳入计算系统的新方法。
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What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, gender) and an explicit description of the process.When computers are involved, determining disparate impact (and hence bias) is harder. It might not be possible to disclose the process. In addition, even if the process is open, it might be hard to elucidate in a legal setting how the algorithm makes its decisions. Instead of requiring access to the process, we propose making inferences based on the data it uses.We present four contributions. First, we link disparate impact to a measure of classification accuracy that while known, has received relatively little attention. Second, we propose a test for disparate impact based on how well the protected class can be predicted from the other attributes. Third, we describe methods by which data might be made unbiased. Finally, we present empirical evidence supporting the effectiveness of our test for disparate impact and our approach for both masking bias and preserving relevant information in the data. Interestingly, our approach resembles some actual selection practices that have recently received legal scrutiny.
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公平性是在算法决策中的重要考虑因素。当具有较高优异的代理人获得比具有较低优点的试剂更差的代理人时,发生不公平。我们的中心点是,不公平的主要原因是不确定性。制定决策的主体或算法永远无法访问代理的真实优点,而是使用仅限于不完全预测优点的代理功能(例如,GPA,星形评级,推荐信)。这些都没有完全捕捉代理人的优点;然而,现有的方法主要基于观察到的特征和结果直接定义公平概念。我们的主要观点是明确地承认和模拟不确定性更为原则。观察到的特征的作用是产生代理商的优点的后部分布。我们使用这个观点来定义排名中近似公平的概念。我们称之为algorithm $ \ phi $ -fair(对于$ \ phi \ in [0,1] $)如果它具有以下所有代理商$ x $和所有$ k $:如果代理商$ x $最高$ k $代理以概率至少为$ \ rho $(根据后部优点分配),那么该算法将代理商在其排名中以概率排名,至少$ \ phi \ rho $。我们展示了如何计算最佳地互惠对校长进行近似公平性的排名。除了理论表征外,我们还提出了对模拟研究中的方法的潜在影响的实证分析。对于真实世界的验证,我们在纸质建议系统的背景下应用了这种方法,我们在KDD 2020会议上建立和界定。
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最近的工作突出了因果关系在设计公平决策算法中的作用。但是,尚不清楚现有的公平因果概念如何相互关系,或者将这些定义作为设计原则的后果是什么。在这里,我们首先将算法公平性的流行因果定义组装成两个广泛的家庭:(1)那些限制决策对反事实差异的影响的家庭; (2)那些限制了法律保护特征(如种族和性别)对决策的影响。然后,我们在分析和经验上表明,两个定义的家庭\ emph {几乎总是总是} - 从一种理论意义上讲 - 导致帕累托占主导地位的决策政策,这意味着每个利益相关者都有一个偏爱的替代性,不受限制的政策从大型自然级别中绘制。例如,在大学录取决定的情况下,每位利益相关者都不支持任何对学术准备和多样性的中立或积极偏好的利益相关者,将不利于因果公平定义的政策。的确,在因果公平的明显定义下,我们证明了由此产生的政策要求承认所有具有相同概率的学生,无论学术资格或小组成员身份如何。我们的结果突出了正式的局限性和因果公平的常见数学观念的潜在不利后果。
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作为一种预测模型的评分系统具有可解释性和透明度的显着优势,并有助于快速决策。因此,评分系统已广泛用于各种行业,如医疗保健和刑事司法。然而,这些模型中的公平问题长期以来一直受到批评,并且使用大数据和机器学习算法在评分系统的构建中提高了这个问题。在本文中,我们提出了一般框架来创建公平知识,数据驱动评分系统。首先,我们开发一个社会福利功能,融入了效率和群体公平。然后,我们将社会福利最大化问题转换为机器学习中的风险最小化任务,并在混合整数编程的帮助下导出了公平感知评分系统。最后,导出了几种理论界限用于提供参数选择建议。我们拟议的框架提供了适当的解决方案,以解决进程中的分组公平问题。它使政策制定者能够设置和定制其所需的公平要求以及其他特定于应用程序的约束。我们用几个经验数据集测试所提出的算法。实验证据支持拟议的评分制度在实现利益攸关方的最佳福利以及平衡可解释性,公平性和效率的需求方面的有效性。
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Virtually all machine learning tasks are characterized using some form of loss function, and "good performance" is typically stated in terms of a sufficiently small average loss, taken over the random draw of test data. While optimizing for performance on average is intuitive, convenient to analyze in theory, and easy to implement in practice, such a choice brings about trade-offs. In this work, we survey and introduce a wide variety of non-traditional criteria used to design and evaluate machine learning algorithms, place the classical paradigm within the proper historical context, and propose a view of learning problems which emphasizes the question of "what makes for a desirable loss distribution?" in place of tacit use of the expected loss.
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算法公平吸引了机器学习社区越来越多的关注。文献中提出了各种定义,但是它们之间的差异和联系并未清楚地解决。在本文中,我们回顾并反思了机器学习文献中先前提出的各种公平概念,并试图与道德和政治哲学,尤其是正义理论的论点建立联系。我们还从动态的角度考虑了公平的询问,并进一步考虑了当前预测和决策引起的长期影响。鉴于特征公平性的差异,我们提出了一个流程图,该流程图包括对数据生成过程,预测结果和诱导的影响的不同类型的公平询问的隐式假设和预期结果。本文展示了与任务相匹配的重要性(人们希望执行哪种公平性)和实现预期目的的手段(公平分析的范围是什么,什么是适当的分析计划)。
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