建立公平的推荐系统是一个具有挑战性且至关重要的研究领域，因为它对社会产生了巨大影响。我们将两个普遍公认的公平概念的定义扩展到了推荐系统，即机会平等和均衡的赔率。这些公平措施确保同样对待“合格”（或“不合格”）候选人，无论其受保护的属性状况如何（例如性别或种族）。我们提出了可扩展的方法，以实现机会平等和在存在位置偏见的情况下排名均等的几率，这通常会困扰推荐系统产生的数据。我们的算法是模型不可知论，因为它们仅依赖于模型提供的最终分数，因此很容易适用于几乎所有Web尺度推荐系统。我们进行广泛的模拟以及现实世界实验，以显示我们方法的功效。

Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterward, through a review of more than 150 scholarly publications, we present an overview of how research in this field is currently operationalized, e.g., in terms of general research methodology, fairness measures, and algorithmic approaches. Overall, our analysis of recent works points to specific research gaps. In particular, we find that in many research works in computer science, very abstract problem operationalizations are prevalent, and questions of the underlying normative claims and what represents a fair recommendation in the context of a given application are often not discussed in depth. These observations call for more interdisciplinary research to address fairness in recommendation in a more comprehensive and impactful manner.

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.

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。

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。

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。

尽管机器学习和基于排名的系统在广泛用于敏感决策过程（例如，确定职位候选者，分配信用评分）时，他们对成果的意外偏见充满了疑虑，这使算法公平（例如，人口统计学公平）平等，机会平等）的目标。 “算法追索”提供了可行的恢复动作，通过修改属性来改变不良结果。我们介绍了排名级别的追索权公平的概念，并开发了一个“追索意识的排名”解决方案，该解决方案满足了排名的追索公平约束，同时最大程度地减少了建议的修改成本。我们的解决方案建议干预措施可以重新排序数据库记录的排名列表并减轻组级别的不公平性；具体而言，子组的不成比例表示和追索权成本不平衡。此重新排列可确定对数据点的最小修改，这些属性修改根据其易于解决方案进行了加权。然后，我们提出了一个有效的基于块的扩展，该扩展可以在任何粒度上重新排序（例如，银行贷款利率的多个括号，搜索引擎结果的多页）。对真实数据集的评估表明，尽管现有方法甚至可能加剧诉求不公平，但我们的解决方案 - raguel-可以显着改善追索性的公平性。 Raguel通过反事实生成和重新排列的结合过程优于改善追索性公平的替代方案，同时对大型数据集保持了有效的效率。

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it is the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a real-world problem of fair prediction of success in law school. * Equal contribution. This work was done while JL was a Research Fellow at the Alan Turing Institute. 2 https://obamawhitehouse.archives.gov/blog/2016/05/04/big-risks-big-opportunities-intersection-big-dataand-civil-rights 31st Conference on Neural Information Processing Systems (NIPS 2017),

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。

公平定理是算法公平文献中的基本结果。它指出，在特殊情况之外，人们不能准确和同时满足公平性的所有三个共同和直观的定义 - 人口统计学奇偶，均衡的赔率和预测率的均等。这一结果促使大多数作品专注于一个或两个指标的解决方案。与其效仿，在本文中，我们提出了一个框架，该框架可以推动不可能定理的限制，以便尽可能地满足所有三个指标。我们开发了一种基于整数编程的方法，该方法可以产生一种认证的最佳后处理方法，以同时满足小违规情况下的多重公平标准。我们显示的实验表明，我们的后处理器可以同时降低模型性能的同时提高不同定义的公平性。我们还讨论了我们在模型选择和公平性解释性方面的应用程序，从而试图回答以下问题：谁是最公平的？

由于越来越多的用户使用它们来寻求和决策，推荐制度对人类和社会的影响增加了对人类和社会的影响。因此，在建议中解决潜在的不公平问题至关重要。就像用户在物品上具有个性化的偏好，用户对公平性的要求也是个性化的许多情况。因此，为用户提供个性化的公平建议，以满足其个性化的公平需求。此外，以前的公平建议作品主要关注基于关联的公平性。但是，重要的是从联合公平概念前进，以便在推荐系统中更适当地评估公平性的因果公平概念。本文根据上述考虑，侧重于为推荐系统中的用户实现个性化的反事实公平。为此，我们介绍了一个框架，通过对建议产生特征 - 独立的用户嵌入来实现通过对抗学习来实现反转公平的建议。该框架允许推荐系统为用户实现个性化的公平，同时也涵盖非个性化情况。在浅层和深刻的推荐算法上的两个现实数据集的实验表明，我们的方法可以为具有理想的推荐性能的用户生成更公平的建议。

In recent years, several metrics have been developed for evaluating group fairness of rankings. Given that these metrics were developed with different application contexts and ranking algorithms in mind, it is not straightforward which metric to choose for a given scenario. In this paper, we perform a comprehensive comparative analysis of existing group fairness metrics developed in the context of fair ranking. By virtue of their diverse application contexts, we argue that such a comparative analysis is not straightforward. Hence, we take an axiomatic approach whereby we design a set of thirteen properties for group fairness metrics that consider different ranking settings. A metric can then be selected depending on whether it satisfies all or a subset of these properties. We apply these properties on eleven existing group fairness metrics, and through both empirical and theoretical results we demonstrate that most of these metrics only satisfy a small subset of the proposed properties. These findings highlight limitations of existing metrics, and provide insights into how to evaluate and interpret different fairness metrics in practical deployment. The proposed properties can also assist practitioners in selecting appropriate metrics for evaluating fairness in a specific application.

分类，一种重大研究的数据驱动机器学习任务，驱动越来越多的预测系统，涉及批准的人类决策，如贷款批准和犯罪风险评估。然而，分类器经常展示歧视性行为，特别是当呈现有偏置数据时。因此，分类公平已经成为一个高优先级的研究区。数据管理研究显示与数据和算法公平有关的主题的增加和兴趣，包括公平分类的主题。公平分类的跨学科努力，具有最大存在的机器学习研究，导致大量的公平概念和尚未系统地评估和比较的广泛方法。在本文中，我们对13个公平分类方法和额外变种的广泛分析，超越，公平，公平，效率，可扩展性，对数据误差的鲁棒性，对潜在的ML模型，数据效率和使用各种指标的稳定性的敏感性和稳定性现实世界数据集。我们的分析突出了对不同指标的影响的新颖见解和高级方法特征对不同方面的性能方面。我们还讨论了选择适合不同实际设置的方法的一般原则，并确定以数据管理为中心的解决方案可能产生最大影响的区域。

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）。

A recent explosion of research focuses on developing methods and tools for building fair predictive models. However, most of this work relies on the assumption that the training and testing data are representative of the target population on which the model will be deployed. However, real-world training data often suffer from selection bias and are not representative of the target population for many reasons, including the cost and feasibility of collecting and labeling data, historical discrimination, and individual biases. In this paper, we introduce a new framework for certifying and ensuring the fairness of predictive models trained on biased data. We take inspiration from query answering over incomplete and inconsistent databases to present and formalize the problem of consistent range approximation (CRA) of answers to queries about aggregate information for the target population. We aim to leverage background knowledge about the data collection process, biased data, and limited or no auxiliary data sources to compute a range of answers for aggregate queries over the target population that are consistent with available information. We then develop methods that use CRA of such aggregate queries to build predictive models that are certifiably fair on the target population even when no external information about that population is available during training. We evaluate our methods on real data and demonstrate improvements over state of the art. Significantly, we show that enforcing fairness using our methods can lead to predictive models that are not only fair, but more accurate on the target population.

虽然传统的排名系统仅关注最大化排名项目的效用，但公平感知的排名系统另外尝试平衡不同保护属性（如性别或种族）的曝光。为了实现这种类型的排名，我们基于分布鲁棒性的第一个原则推导出新的排名系统。我们在选择分布的球员之间制定最小的游戏，以最大限度地提高实用程序，同时满足公平的限制，针对对攻击性匹配统计训练数据的统计数据来最小化实用性。我们表明，我们的方法提供比现有基线方法高度公平的排名更好的效用。

The fair-ranking problem, which asks to rank a given set of items to maximize utility subject to group fairness constraints, has received attention in the fairness, information retrieval, and machine learning literature. Recent works, however, observe that errors in socially-salient (including protected) attributes of items can significantly undermine fairness guarantees of existing fair-ranking algorithms and raise the problem of mitigating the effect of such errors. We study the fair-ranking problem under a model where socially-salient attributes of items are randomly and independently perturbed. We present a fair-ranking framework that incorporates group fairness requirements along with probabilistic information about perturbations in socially-salient attributes. We provide provable guarantees on the fairness and utility attainable by our framework and show that it is information-theoretically impossible to significantly beat these guarantees. Our framework works for multiple non-disjoint attributes and a general class of fairness constraints that includes proportional and equal representation. Empirically, we observe that, compared to baselines, our algorithm outputs rankings with higher fairness, and has a similar or better fairness-utility trade-off compared to baselines.

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.

我们在分类的背景下研究公平，其中在接收器的曲线下的区域（AUC）下的区域测量的性能。当I型（误报）和II型（假阴性）错误都很重要时，通常使用AUC。然而，相同的分类器可以针对不同的保护组具有显着变化的AUC，并且在现实世界中，通常希望减少这种交叉组差异。我们解决如何选择其他功能，以便最大地改善弱势群体的AUC。我们的结果表明，功能的无条件方差不会通知我们关于AUC公平，而是类条件方差。使用此连接，我们基于功能增强（添加功能）来开发一种新颖的方法Fairauc，以减轻可识别组之间的偏差。我们评估综合性和现实世界（Compas）数据集的Fairauc，并发现它对于相对于基准，最大限度地提高了总体AUC并最大限度地减少了组之间的偏见的基准，它显着改善了弱势群体的AUC。

学习 - 排名问题旨在排名，以最大限度地曝光与用户查询相关的那些。这种排名系统的理想特性是保证指定项目组之间的一些公平概念。虽然最近在学习排名系统的背景下审议了公平性，但目前的方法无法提供拟议的排名政策的公平性的担保。本文解决了这一差距，并介绍了智能预测，并优化了公平排名（SPOFR），综合优化和学习框架，以便进行公平受限学习。端到端的SPOFR框架包括受约束的优化子模型，并产生保证的排名策略，以满足公平限制，同时允许对公平实用权概况进行精细控制。SPOFR显示出在既定的性能指标方面显着提高当前最先进的公平学习系统。