At the core of insurance business lies classification between risky and non-risky insureds, actuarial fairness meaning that risky insureds should contribute more and pay a higher premium than non-risky or less-risky ones. Actuaries, therefore, use econometric or machine learning techniques to classify, but the distinction between a fair actuarial classification and "discrimination" is subtle. For this reason, there is a growing interest about fairness and discrimination in the actuarial community Lindholm, Richman, Tsanakas, and Wuthrich (2022). Presumably, non-sensitive characteristics can serve as substitutes or proxies for protected attributes. For example, the color and model of a car, combined with the driver's occupation, may lead to an undesirable gender bias in the prediction of car insurance prices. Surprisingly, we will show that debiasing the predictor alone may be insufficient to maintain adequate accuracy (1). Indeed, the traditional pricing model is currently built in a two-stage structure that considers many potentially biased components such as car or geographic risks. We will show that this traditional structure has significant limitations in achieving fairness. For this reason, we have developed a novel pricing model approach. Recently some approaches have Blier-Wong, Cossette, Lamontagne, and Marceau (2021); Wuthrich and Merz (2021) shown the value of autoencoders in pricing. In this paper, we will show that (2) this can be generalized to multiple pricing factors (geographic, car type), (3) it perfectly adapted for a fairness context (since it allows to debias the set of pricing components): We extend this main idea to a general framework in which a single whole pricing model is trained by generating the geographic and car pricing components needed to predict the pure premium while mitigating the unwanted bias according to the desired metric.
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机器学习模型在高赌注应用中变得普遍存在。尽管在绩效方面有明显的效益,但该模型可以表现出对少数民族群体的偏见,并导致决策过程中的公平问题,导致对个人和社会的严重负面影响。近年来,已经开发了各种技术来减轻机器学习模型的偏差。其中,加工方法已经增加了社区的关注,在模型设计期间直接考虑公平,以诱导本质上公平的模型,从根本上减轻了产出和陈述中的公平问题。在本调查中,我们审查了加工偏置减缓技术的当前进展。基于在模型中实现公平的地方,我们将它们分类为明确和隐性的方法,前者直接在培训目标中纳入公平度量,后者重点介绍精炼潜在代表学习。最后,我们在讨论该社区中的研究挑战来讨论调查,以激励未来的探索。
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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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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),
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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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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。
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本文解决了在水模型部署民主化中采用了机器学习的一些挑战。第一个挑战是减少了在主动学习的帮助下减少了标签努力(因此关注数据质量),模型推断与Oracle之间的反馈循环:如在保险中,未标记的数据通常丰富,主动学习可能会成为一个重要的资产减少标签成本。为此目的,本文在研究其对合成和真实数据集的实证影响之前,阐述了各种古典主动学习方法。保险中的另一个关键挑战是模型推论中的公平问题。我们将在此主动学习框架中介绍和整合一个用于多级任务的后处理公平,以解决这两个问题。最后对不公平数据集的数值实验突出显示所提出的设置在模型精度和公平性之间存在良好的折衷。
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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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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。
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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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It is important to guarantee that machine learning algorithms deployed in the real world do not result in unfairness or unintended social consequences. Fair ML has largely focused on the protection of single attributes in the simpler setting where both attributes and target outcomes are binary. However, the practical application in many a real-world problem entails the simultaneous protection of multiple sensitive attributes, which are often not simply binary, but continuous or categorical. To address this more challenging task, we introduce FairCOCCO, a fairness measure built on cross-covariance operators on reproducing kernel Hilbert Spaces. This leads to two practical tools: first, the FairCOCCO Score, a normalised metric that can quantify fairness in settings with single or multiple sensitive attributes of arbitrary type; and second, a subsequent regularisation term that can be incorporated into arbitrary learning objectives to obtain fair predictors. These contributions address crucial gaps in the algorithmic fairness literature, and we empirically demonstrate consistent improvements against state-of-the-art techniques in balancing predictive power and fairness on real-world datasets.
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在过去几年中,图像分析的工业和社会应用中,神经网络分类器的越来越常见使用令人印象深刻的进展。然而,这种方法对算法偏压敏感,即阳性预测的欠或过度表示或在图像的特定子组中的更高预测误差。然后,我们在本文中介绍了一种新的方法来发动基于神经网络的分类器中的算法偏压。我们的方法是神经网络架构不可知的和缩放到大规模训练的图像集。它确实只使用基于Wassersein-2的损失函数超载了基于Wasserstein-2的正则化术语,我们基于预测分布的Gateaux衍生物,我们使用新模型对特定输出预测的影响传播了特定输出预测的影响。该型号是算法的合理性,使我们可以使用标准随机梯度 - 下降策略来使用我们的正则损耗。它的良好行为是在参考成人人口普查,Mnist,Celeba数据集中进行评估。
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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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算法公平旨在识别和校正机器学习算法中的偏差源。混淆,确保公平往往以准确性为止。我们在这项工作中提供正式工具,以便在算法公平中调和这一基本紧张。具体而言,我们将帕累托最优性的概念从多目标优化中寻求神经网络分类器的公平准确性帕累托。我们证明许多现有的算法公平方法正在执行所谓的线性标定方案,其具有恢复帕累托最佳解决方案的严重限制。相反,与线性方案相比,我们将Chebyshev标准化方案从理论上提供优越,并且在恢复Pareto最佳解决方案时没有更加计算繁重。
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机器学习渗透到许多行业,这为公司带来了新的利益来源。然而,在人寿保险行业中,机器学习在实践中并未被广泛使用,因为在过去几年中,统计模型表明了它们的风险评估效率。因此,保险公司可能面临评估人工智能价值的困难。随着时间的流逝,专注于人寿保险行业的修改突出了将机器学习用于保险公司的利益以及通过释放数据价值带来的利益。本文回顾了传统的生存建模方法论,并通过机器学习技术扩展了它们。它指出了与常规机器学习模型的差异,并强调了特定实现在与机器学习模型家族中面对审查数据的重要性。在本文的补充中,已经开发了Python库。已经调整了不同的开源机器学习算法,以适应人寿保险数据的特殊性,即检查和截断。此类模型可以轻松地从该SCOR库中应用,以准确地模拟人寿保险风险。
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Algorithmic fairness is becoming increasingly important in data mining and machine learning. Among others, a foundational notation is group fairness. The vast majority of the existing works on group fairness, with a few exceptions, primarily focus on debiasing with respect to a single sensitive attribute, despite the fact that the co-existence of multiple sensitive attributes (e.g., gender, race, marital status, etc.) in the real-world is commonplace. As such, methods that can ensure a fair learning outcome with respect to all sensitive attributes of concern simultaneously need to be developed. In this paper, we study the problem of information-theoretic intersectional fairness (InfoFair), where statistical parity, a representative group fairness measure, is guaranteed among demographic groups formed by multiple sensitive attributes of interest. We formulate it as a mutual information minimization problem and propose a generic end-to-end algorithmic framework to solve it. The key idea is to leverage a variational representation of mutual information, which considers the variational distribution between learning outcomes and sensitive attributes, as well as the density ratio between the variational and the original distributions. Our proposed framework is generalizable to many different settings, including other statistical notions of fairness, and could handle any type of learning task equipped with a gradient-based optimizer. Empirical evaluations in the fair classification task on three real-world datasets demonstrate that our proposed framework can effectively debias the classification results with minimal impact to the classification accuracy.
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Causal inference is the process of using assumptions, study designs, and estimation strategies to draw conclusions about the causal relationships between variables based on data. This allows researchers to better understand the underlying mechanisms at work in complex systems and make more informed decisions. In many settings, we may not fully observe all the confounders that affect both the treatment and outcome variables, complicating the estimation of causal effects. To address this problem, a growing literature in both causal inference and machine learning proposes to use Instrumental Variables (IV). This paper serves as the first effort to systematically and comprehensively introduce and discuss the IV methods and their applications in both causal inference and machine learning. First, we provide the formal definition of IVs and discuss the identification problem of IV regression methods under different assumptions. Second, we categorize the existing work on IV methods into three streams according to the focus on the proposed methods, including two-stage least squares with IVs, control function with IVs, and evaluation of IVs. For each stream, we present both the classical causal inference methods, and recent developments in the machine learning literature. Then, we introduce a variety of applications of IV methods in real-world scenarios and provide a summary of the available datasets and algorithms. Finally, we summarize the literature, discuss the open problems and suggest promising future research directions for IV methods and their applications. We also develop a toolkit of IVs methods reviewed in this survey at https://github.com/causal-machine-learning-lab/mliv.
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为了减轻模型中不希望的偏差的影响,几种方法建议预先处理输入数据集,以通过防止敏感属性的推断来减少歧视风险。不幸的是,这些预处理方法中的大多数导致一代新分布与原始分布有很大不同,因此通常导致不切实际的数据。作为副作用,这种新的数据分布意味着需要重新训练现有模型才能做出准确的预测。为了解决这个问题,我们提出了一种新颖的预处理方法,我们将根据保护组的分布转换为所选目标一个,并具有附加的隐私约束,其目的是防止敏感敏感的推断属性。更确切地说,我们利用Wasserstein Gan和Attgan框架的最新作品来实现数据点的最佳运输以及强制保护属性推断的歧视器。我们提出的方法可以保留数据的可解释性,并且可以在不定义敏感组的情况下使用。此外,我们的方法可以专门建模现有的最新方法,从而提出对这些方法的统一观点。最后,关于真实和合成数据集的一些实验表明,我们的方法能够隐藏敏感属性,同时限制数据的变形并改善了后续数据分析任务的公平性。
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在本文中,我们提出了一个通用框架,用于估计以用户定义的公平程度来估算回归模型。我们将公平性作为模型选择步骤,在该步骤中,我们选择山脊惩罚的价值来控制敏感属性的效果。然后,我们估计模型的参数,条件是所选的惩罚值。我们的建议在数学上很简单,其解决方案部分为封闭形式,并产生回归系数的估计值,这些系数直观地解释为公平水平的函数。此外,它很容易扩展到广义线性模型,内核回归模型和其他惩罚。它可以适应公平的多种定义。我们将我们的方法与Komiyama等人的回归模型进行了比较。 (2018年),它实现了一个理想的线性回归模型;以及Zafar等人的公平模型。 (2019)。我们在六个不同的数据集上对这些方法进行了经验评估,我们发现我们的建议提供了更好的合适性和更好的预测准确性,以达到相同的公平水平。此外,我们强调了Komiyama等人的原始实验评估中的偏见来源。 (2018)。
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Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of their current shortcomings. One of them is that neural networks are correlation machines prone to model biases within the data instead of focusing on actual useful causal relationships. This problem is particularly serious in application domains affected by aspects such as race, gender, and age. To prevent models from incurring on unfair decision-making, the AI community has concentrated efforts in correcting algorithmic biases, giving rise to the research area now widely known as fairness in AI. In this survey paper, we provide an in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research. We propose a novel taxonomy to better organize the literature on debiasing methods for fairness, and we discuss the current challenges, trends, and important future work directions for the interested researcher and practitioner.
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