We present a systematic approach for achieving fairness in a binary classification setting. While we focus on two well-known quantitative definitions of fairness, our approach encompasses many other previously studied definitions as special cases. The key idea is to reduce fair classification to a sequence of cost-sensitive classification problems, whose solutions yield a randomized classifier with the lowest (empirical) error subject to the desired constraints. We introduce two reductions that work for any representation of the cost-sensitive classifier and compare favorably to prior baselines on a variety of data sets, while overcoming several of their disadvantages.
translated by 谷歌翻译
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.
translated by 谷歌翻译
我们展示了如何采用回归函数$ \ hat {f} $,该{f} $适当地``多校准''并有效地将其后处理成近似错误的分类器,使分类器满足各种公平限制。后处理不需要标记的数据,只有一定数量的未标记数据和计算。计算$ \ hat f $的计算和样本复杂性要求与解决单个公平学习任务的要求相媲美,但实际上可以用来有效地解决许多不同的下游公平约束的学习问题。我们的后处理方法可以轻松处理相交组,从而将先前的工作推广到后处理回归功能上,以满足仅应用于分离组的公平约束。我们的工作扩展了最近的工作,表明多校准的回归函数是``omnipredictors''(即可以在后处理以最佳解决无约束的ERM问题)以进行约束优化。
translated by 谷歌翻译
尽管大规模的经验风险最小化(ERM)在各种机器学习任务中取得了高精度,但公平的ERM受到公平限制与随机优化的不兼容的阻碍。我们考虑具有离散敏感属性以及可能需要随机求解器的可能性大型模型和数据集的公平分类问题。现有的内部处理公平算法在大规模设置中要么是不切实际的,因为它们需要在每次迭代时进行大量数据,要么不保证它们会收敛。在本文中,我们开发了第一个具有保证收敛性的随机内处理公平算法。对于人口统计学,均衡的赔率和公平的机会均等的概念,我们提供了算法的略有变化,称为Fermi,并证明这些变化中的每一个都以任何批次大小收敛于随机优化。从经验上讲,我们表明Fermi适合具有多个(非二进制)敏感属性和非二进制目标的随机求解器,即使Minibatch大小也很小,也可以很好地表现。广泛的实验表明,与最先进的基准相比,FERMI实现了所有经过测试的设置之间的公平违规和测试准确性之间最有利的权衡,该基准是人口统计学奇偶校验,均衡的赔率,均等机会,均等机会。这些好处在小批量的大小和非二元分类具有大量敏感属性的情况下尤其重要,这使得费米成为大规模问题的实用公平算法。
translated by 谷歌翻译
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。
translated by 谷歌翻译
AI methods are used in societally important settings, ranging from credit to employment to housing, and it is crucial to provide fairness in regard to algorithmic decision making. Moreover, many settings are dynamic, with populations responding to sequential decision policies. We introduce the study of reinforcement learning (RL) with stepwise fairness constraints, requiring group fairness at each time step. Our focus is on tabular episodic RL, and we provide learning algorithms with strong theoretical guarantees in regard to policy optimality and fairness violation. Our framework provides useful tools to study the impact of fairness constraints in sequential settings and brings up new challenges in RL.
translated by 谷歌翻译
我们考虑为多类分类任务生产公平概率分类器的问题。我们以“投射”预先培训(且可能不公平的)分类器在满足目标群体对要求的一组模型上的“投影”来提出这个问题。新的投影模型是通过通过乘法因子后处理预训练的分类器的输出来给出的。我们提供了一种可行的迭代算法,用于计算投影分类器并得出样本复杂性和收敛保证。与最先进的基准测试的全面数值比较表明,我们的方法在准确性权衡曲线方面保持了竞争性能,同时在大型数据集中达到了有利的运行时。我们还在具有多个类别,多个相互保护组和超过1M样本的开放数据集上评估了我们的方法。
translated by 谷歌翻译
基于梯度提升决策树(GBDT)的机器学习(ML)算法在从医疗保健到金融的各种任务关键应用程序中的许多表格数据任务上仍然受到青睐。但是,GBDT算法并不能免于偏见和歧视性决策的风险。尽管GBDT的受欢迎程度和公平ML研究的迅速发展,但现有的经过处理的公平ML方法要么不适用GBDT,因此在大量的火车时间内开销,或者由于高级失衡的问题而不足。我们提出FairgBM,这是一个在公平限制下培训GBDT的学习框架,与无约束的LightGBM相比,对预测性能几乎没有影响。由于常见的公平指标是不可差异的,因此我们使用平滑的凸错误率代理采用``代理 - 拉格朗日''公式来实现基于梯度的优化。此外,与相关工作相比,我们的开源实施在训练时间中显示了一个数量级的加速顺序,这是一个关键方面,旨在促进现实世界实践者对FairgBM的广泛采用。
translated by 谷歌翻译
多集团不可知学习是一个正式的学习标准,涉及人口亚组内的预测因子的条件风险。标准解决了最近的实际问题,如亚组公平和隐藏分层。本文研究了对多组学习问题的解决方案的结构,为学习问题提供了简单和近最佳的算法。
translated by 谷歌翻译
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.
translated by 谷歌翻译
在联邦学习中,对受保护群体的公平预测是许多应用程序的重要限制。不幸的是,先前研究集团联邦学习的工作往往缺乏正式的融合或公平保证。在这项工作中,我们为可证明的公平联合学习提供了一个一般框架。特别是,我们探索并扩展了有限的群体损失的概念,作为理论上的群体公平方法。使用此设置,我们提出了一种可扩展的联合优化方法,该方法在许多群体公平限制下优化了经验风险。我们为该方法提供收敛保证,并为最终解决方案提供公平保证。从经验上讲,我们评估了公平ML和联合学习的共同基准的方法,表明它可以比基线方法提供更公平,更准确的预测。
translated by 谷歌翻译
解决机器学习模型的公平关注是朝着实际采用现实世界自动化系统中的至关重要的一步。尽管已经开发了许多方法来从数据培训公平模型,但对这些方法对数据损坏的鲁棒性知之甚少。在这项工作中,我们考虑在最坏情况下的数据操作下进行公平意识学习。我们表明,在某些情况下,对手可能会迫使任何学习者返回过度偏见的分类器,无论样本量如何,有或没有降解的准确性,并且多余的偏见的强度会增加数据中数据不足的受保护组的学习问题,而数据中有代表性不足的组。我们还证明,我们的硬度结果紧密到不断的因素。为此,我们研究了两种自然学习算法,以优化准确性和公平性,并表明这些算法在损坏比和较大数据限制中受保护的群体频率方面享有订单最佳的保证。
translated by 谷歌翻译
我们在禁用的对手存在下研究公平分类,允许获得$ \ eta $,选择培训样本的任意$ \ eta $ -flaction,并任意扰乱受保护的属性。由于战略误报,恶意演员或归责的错误,受保护属性可能不正确的设定。和现有的方法,使随机或独立假设对错误可能不满足其在这种对抗环境中的保证。我们的主要贡献是在这种对抗的环境中学习公平分类器的优化框架,这些普遍存在的准确性和公平性提供了可证明的保证。我们的框架适用于多个和非二进制保护属性,专为大类线性分数公平度量设计,并且还可以处理除了受保护的属性之外的扰动。我们证明了我们框架的近密性,对自然假设类别的保证:没有算法可以具有明显更好的准确性,并且任何具有更好公平性的算法必须具有较低的准确性。凭经验,我们评估了我们对统计率的统计税务统计税率为一个对手的统计税率产生的分类机。
translated by 谷歌翻译
针对社会福利计划中个人的干预措施的主要问题之一是歧视:个性化治疗可能导致跨年龄,性别或种族等敏感属性的差异。本文解决了公平有效的治疗分配规则的设计问题。我们采用了第一次的非遗憾视角,没有危害:我们选择了帕累托边境中最公平的分配。我们将优化投入到混合构成线性程序公式中,可以使用现成的算法来解决。我们对估计的政策功能的不公平性和在帕累托前沿的不公平保证在一般公平概念下的不公平性范围内得出了遗憾。最后,我们使用教育经济学的应用来说明我们的方法。
translated by 谷歌翻译
这项工作提供了在人口统计学限制下的最佳分类函数的几种基本特征。在意识框架中,类似于经典的不受限制的分类案例,我们表明,在这种公平性约束下,最大化准确性等于解决相应的回归问题,然后在级别$ 1/2 $上进行阈值。我们将此结果扩展到线性分类分类度量(例如,$ {\ rm f} $ - 得分,AM度量,平衡准确性等),突出了回归问题在此框架中所起的基本作用。我们的结果利用了最近在人口统计学限制与多界限最佳运输公式之间建立了联系。从非正式的角度来看,我们的结果表明,通过解决公平回归问题的解决方案来代替标签的有条件期望,可以实现无约束的问题与公平问题之间的过渡。最后,利用我们的分析,我们证明了在两个敏感群体的情况下,意识和不认识的设置之间的等效性。
translated by 谷歌翻译
我们提出了一种可扩展的后处理算法,用于衰减培训的型号,包括深度神经网络(DNN),我们证明是通过限制其多余的贝叶斯风险而近乎最佳。我们在经典算法以及现代DNN架构上凭经验验证其对标准基准数据集的优势,并证明它在以前处理的同时表现出先前的后处理方法。此外,我们表明,该算法对于在刻度培训的模型是特别有效的,其中后处理是自然和实际的选择。
translated by 谷歌翻译
As machine learning being used increasingly in making high-stakes decisions, an arising challenge is to avoid unfair AI systems that lead to discriminatory decisions for protected population. A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints, which achieves Pareto efficiency when trading off performance against fairness. Among various fairness metrics, the ones based on the area under the ROC curve (AUC) are emerging recently because they are threshold-agnostic and effective for unbalanced data. In this work, we formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints. This problem can be reformulated as a min-max optimization problem with min-max constraints, which we solve by stochastic first-order methods based on a new Bregman divergence designed for the special structure of the problem. We numerically demonstrate the effectiveness of our approach on real-world data under different fairness metrics.
translated by 谷歌翻译
最近的研究表明,看似公平的机器学习模型在为对人们的生活或福祉产生影响的决策提供信息(例如,涉及教育,就业和贷款的申请)可能会在长期内无意中增加社会不平等。这是因为先前的公平意识算法仅考虑静态公平限制,例如机会均等或人口统计奇偶。但是,强制执行这种类型的限制可能会导致模型对处境不利的个人和社区产生负面影响。我们介绍ELF(执行长期公平性),这是第一个分类算法,可提供高信任公平保证,以长期或延迟影响。我们证明,ELF返回不公平解决方案的概率小于用户指定的公差,并且(在轻度假设下),如果有足够的培训数据,ELF能够找到并返回公平的解决方案,如果存在一个公平的解决方案。我们通过实验表明,我们的算法可以成功缓解长期不公平。
translated by 谷歌翻译
Algorithmic fairness plays an increasingly critical role in machine learning research. Several group fairness notions and algorithms have been proposed. However, the fairness guarantee of existing fair classification methods mainly depends on specific data distributional assumptions, often requiring large sample sizes, and fairness could be violated when there is a modest number of samples, which is often the case in practice. In this paper, we propose FaiREE, a fair classification algorithm that can satisfy group fairness constraints with finite-sample and distribution-free theoretical guarantees. FaiREE can be adapted to satisfy various group fairness notions (e.g., Equality of Opportunity, Equalized Odds, Demographic Parity, etc.) and achieve the optimal accuracy. These theoretical guarantees are further supported by experiments on both synthetic and real data. FaiREE is shown to have favorable performance over state-of-the-art algorithms.
translated by 谷歌翻译
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。
translated by 谷歌翻译