Existing regulations prohibit model developers from accessing protected attributes (gender, race, etc.), often resulting in fairness assessments on populations without knowing their protected groups. In such scenarios, institutions often adopt a separation between the model developers (who train models with no access to the protected attributes) and a compliance team (who may have access to the entire dataset for auditing purpose). However, the model developers might be allowed to test their models for bias by querying the compliance team for group fairness metrics. In this paper, we first demonstrate that simply querying for fairness metrics, such as statistical parity and equalized odds can leak the protected attributes of individuals to the model developers. We demonstrate that there always exist strategies by which the model developers can identify the protected attribute of a targeted individual in the test dataset from just a single query. In particular, we show that one can reconstruct the protected attributes of all the individuals from O(Nk log n/Nk) queries when Nk<<n using techniques from compressed sensing (n: size of the test dataset, Nk: size of smallest group). Our results pose an interesting debate in algorithmic fairness: should querying for fairness metrics be viewed as a neutral-valued solution to ensure compliance with regulations? Or, does it constitute a violation of regulations and privacy if the number of queries answered is enough for the model developers to identify the protected attributes of specific individuals? To address this supposed violation, we also propose Attribute-Conceal, a novel technique that achieves differential privacy by calibrating noise to the smooth sensitivity of our bias query, outperforming naive techniques such as Laplace mechanism. We also include experimental results on the Adult dataset and synthetic data (broad range of parameters).
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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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联合学习允许许多设备在机器学习模型的培训中进行协作。与传统的机器学习一样,越来越关注的是,接受联合学习的模型可能会对不同的人群组表现出不同的表现。现有的解决方案来衡量和确保跨小组的平等模型绩效需要访问有关小组成员的信息,但是此访问并不总是可用或可取的,尤其是在联邦学习的隐私愿望下。我们研究了衡量此类性能差异的可行性,同时保护用户组成员资格的隐私以及联合模型在用户数据上的性能。保护两者对于隐私至关重要,因为它们可能是相关的,因此学习一个可能会揭示另一个。另一方面,从公用事业的角度来看,保留隐私的数据应保持相关性,以确保能够对性能差异进行准确的测量。我们通过开发当地差异化的私人机制来实现这两个目标,从而保留小组成员和模型绩效之间的相关性。为了分析机制的有效性,我们在对给定隐私预算进行优化时估算差异时的错误,并在合成数据上验证这些界限。我们的结果表明,对于参与的客户数量的实际数量,错误迅速减少,这表明,与先前的工作相反,保护受保护属性的隐私不一定与确定联合模型性能的差异相抵触。
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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.
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近年来,关于如何在公平限制下学习机器学习模型的越来越多的工作,通常在某些敏感属性方面表达。在这项工作中,我们考虑了对手对目标模型具有黑箱访问的设置,并表明对手可以利用有关该模型公平性的信息,以增强他对训练数据敏感属性的重建。更确切地说,我们提出了一种通用的重建校正方法,该方法将其作为对手进行的初始猜测,并纠正它以符合某些用户定义的约束(例如公平信息),同时最大程度地减少了对手猜测的变化。提出的方法对目标模型的类型,公平感知的学习方法以及对手的辅助知识不可知。为了评估我们的方法的适用性,我们对两种最先进的公平学习方法进行了彻底的实验评估,使用四个具有广泛公差的不同公平指标以及三个不同大小和敏感属性的数据集。实验结果证明了提出的方法改善训练集敏感属性的重建的有效性。
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差异隐私通常使用比理论更大的隐私参数应用于理想的理想。已经提出了宽大隐私参数的各种非正式理由。在这项工作中,我们考虑了部分差异隐私(DP),该隐私允许以每个属性为基础量化隐私保证。在此框架中,我们研究了几个基本数据分析和学习任务,并设计了其每个属性隐私参数的算法,其较小的人(即所有属性)的最佳隐私参数比最佳的隐私参数。
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除了近年来数据收集和分析技术的快速开发外,还越来越强调需要解决与此类数据使用相关的信息泄漏。为此,隐私文献中的许多工作都致力于保护个人用户和数据贡献者。但是,某些情况需要不同的数据机密性概念,涉及数据集记录的全局属性。这样的信息保护概念尤其适用于业务和组织数据,在这些数据中,全球财产可能反映商业秘密或人口统计数据,如果不当行为可能是有害的。最新关于财产推断攻击的工作还显示了数据分析算法如何容易泄漏数据的这些全局性能,从而强调了开发可以保护此类信息的机制的重要性。在这项工作中,我们演示了如何应用分发隐私框架来形式化保护数据集的全球属性的问题。鉴于此框架,我们研究了一些提供数据机密性概念的机制及其权衡。我们分析了这些机制在各种数据假设下提供的理论保护保证,然后对几个数据分析任务进行实施并经验评估这些机制。我们的实验结果表明,我们的机制确实可以降低实用性推理攻击的有效性,同时提供的实用性大大超过了原油差异的隐私基线。因此,我们的工作为保护数据集的全球性质的理论支持机制提供了基础。
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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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机器学习模型在许多领域都表现出了有希望的表现。但是,担心他们可能会偏向特定的群体,阻碍了他们在高级申请中的采用。因此,必须确保机器学习模型中的公平性。以前的大多数努力都需要访问敏感属性以减轻偏见。尽管如此,由于人们对隐私和法律依从性的认识日益增加,获得具有敏感属性的大规模数据通常是不可行的。因此,一个重要的研究问题是如何在隐私下做出公平的预测?在本文中,我们研究了半私人环境中公平分类的新问题,其中大多数敏感属性都是私有的,只有少量的干净敏感属性可用。为此,我们提出了一个新颖的框架Fairsp,可以首先学会通过利用有限的清洁敏感属性来纠正隐私保证下的嘈杂敏感属性。然后,它以对抗性方式共同建模校正和清洁数据以进行歧义和预测。理论分析表明,当大多数敏感属性都是私有的时,提出的模型可以确保公平。现实世界数据集的实验结果证明了所提出的模型在隐私下做出公平预测并保持高精度的有效性。
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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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The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of high-level, pre-defined groups (such as race or gender), and then ask for approximate parity of some statistic of the classifier (like positive classification rate or false positive rate) across these groups. Constraints of this form are susceptible to (intentional or inadvertent) fairness gerrymandering, in which a classifier appears to be fair on each individual group, but badly violates the fairness constraint on one or more structured subgroups defined over the protected attributes (such as certain combinations of protected attribute values). We propose instead to demand statistical notions of fairness across exponentially (or infinitely) many subgroups, defined by a structured class of functions over the protected attributes. This interpolates between statistical definitions of fairness, and recently proposed individual notions of fairness, but it raises several computational challenges. It is no longer clear how to even check or audit a fixed classifier to see if it satisfies such a strong definition of fairness. We prove that the computational problem of auditing subgroup fairness for both equality of false positive rates and statistical parity is equivalent to the problem of weak agnostic learning -which means it is computationally hard in the worst case, even for simple structured subclasses. However, it also suggests that common heuristics for learning can be applied to successfully solve the auditing problem in practice.We then derive two algorithms that provably converge to the best fair distribution over classifiers in a given class, given access to oracles which can optimally solve the agnostic learning problem. The algorithms are based on a formulation of subgroup fairness as a two-player zero-sum game between a Learner (the primal player) and an Auditor (the dual player). Both algorithms compute an equilibrium of this game. We obtain our first algorithm by simulating play of the game by having Learner play an instance of the no-regret Follow the Perturbed Leader algorithm, and having Auditor play best response. This algorithm provably converges to an approximate Nash equilibrium (and thus to an approximately optimal subgroup-fair distribution over classifiers) in a polynomial number of steps. We obtain our second algorithm by simulating play of the game by having both players play Fictitious Play, which enjoys only provably asymptotic convergence, but has the merit of simplicity and faster per-step computation. We implement the Fictitious Play version using linear regression as a heuristic oracle, and show that we can effectively both audit and learn fair classifiers on real datasets.
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本文调查了差异隐私(DP)与公平性交集中的最新工作。它审查了隐私和公平性可能使目标对准或对比目标的条件,分析了DP如何以及为什么在决策问题和学习任务中加剧偏见和不公平性,并描述了DP系统中出现的公平问题的可用缓解措施。该调查提供了对在公平镜头下部署隐私制度学习或决策任务时,对主要挑战和潜在风险的统一理解。
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自几十年前以来,已经证明了机器学习评估贷款申请人信誉的实用性。但是,自动决策可能会导致对群体或个人的不同治疗方法,可能导致歧视。本文基准了12种最大的偏见缓解方法,讨论其绩效,该绩效基于5个不同的公平指标,获得的准确性以及为金融机构提供的潜在利润。我们的发现表明,在确保准确性和利润的同时,实现公平性方面的困难。此外,它突出了一些表现最好和最差的人,并有助于弥合实验机学习及其工业应用之间的差距。
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The ''Propose-Test-Release'' (PTR) framework is a classic recipe for designing differentially private (DP) algorithms that are data-adaptive, i.e. those that add less noise when the input dataset is nice. We extend PTR to a more general setting by privately testing data-dependent privacy losses rather than local sensitivity, hence making it applicable beyond the standard noise-adding mechanisms, e.g. to queries with unbounded or undefined sensitivity. We demonstrate the versatility of generalized PTR using private linear regression as a case study. Additionally, we apply our algorithm to solve an open problem from ''Private Aggregation of Teacher Ensembles (PATE)'' -- privately releasing the entire model with a delicate data-dependent analysis.
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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.
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随着算法治理的快速发展,公平性已成为机器学习模型的强制性属性,以抑制无意的歧视。在本文中,我们着重于实现公平性的预处理方面,并提出了一种数据重新拨打的方法,该方法仅在培训阶段调整样本的重量。与通常为每个(子)组分配均匀权重的大多数以前的重新校正方法不同,我们对每个训练样本在与公平相关的数量和预测效用方面的影响进行颗粒片,并根据在从影响下的影响下对单个权重进行计算。公平和效用。实验结果表明,以前的方法以不可忽略的实用性成本达到公平性,而为了取得重大优势,我们的方法可以从经验上释放权衡并获得无需成本的公平就可以平等机会。与多个现实世界表格数据集中的基线方法相比,我们通过香草分类器和标准培训过程证明了通过香草分类器和标准培训过程的公平性。可在https://github.com/brandeis-machine-learning/influence-fairness上获得代码。
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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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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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多核电是一个理想的公平标准,该标准限制了数据中灵活定义的组之间的校准误差,同时保持整体校准。但是,当结果概率与群体成员资格相关时,基本速率较低的组的校准误差比基本速率较高的组显示出更高的校准误差。结果,决策者仍然有可能学习对特定群体的信任或不信任模型预测。为了减轻这一点,我们提出了比例的数字净化,该标准限制了组之间和预测箱之间的校准误差百分比。我们证明,满足比例的多中心范围界定了模型的数字以及它的差异校准,这是一个受充分性的公平概念启发的更强的公平标准。我们为后处理风险预测模型提供了有效的算法,以进行比例的多核电并进行经验评估。我们进行仿真研究,并研究PMC-POSTPROCESSSPOCESS在急诊科患者入院预测中的现实应用。我们观察到,比例的数字启动是控制模型在分类性能方面几乎没有成本的校准公平度的同时衡量量标准的有希望的标准。
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最大信息系数(MIC)是一个强大的统计量,可以识别变量之间的依赖性。但是,它可以应用于敏感数据,并且发布可能会泄漏私人信息。作为解决方案,我们提出算法以提供差异隐私的方式近似麦克风。我们表明,经典拉普拉斯机制的自然应用产生的精度不足。因此,我们介绍了MICT统计量,这是一种新的MIC近似值,与差异隐私更加兼容。我们证明MICS是麦克风的一致估计器,我们提供了两个差异性私有版本。我们对各种真实和合成数据集进行实验。结果表明,私人微统计数据极大地超过了拉普拉斯机制的直接应用。此外,对现实世界数据集的实验显示出准确性,当样本量至少适中时可用。
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