用马氏距离引导玻尔兹曼探索性检验提高可推广公平性

IF 5.6 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Kaixiang Dong;Peng Wu;Yanting Chen
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引用次数: 0

摘要

尽管机器学习模型在就业、保险和刑事司法等决策任务中非常有效,但确保模型预测的可靠性和社会公平性仍然是紧迫而具有挑战性的。这相当于用真实的测试数据广泛地检测和修复机器学习模型的潜在歧视性缺陷。本文提出了一种新的Mahalanobis距离引导的自适应探索性公平性测试(MAEFT)方法,该方法通过深度强化学习和玻尔兹曼探索的自适应扩展来搜索个体歧视实例(IDIs),显著降低了高估。MAEFT使用马氏距离来指导搜索与输入特征之间的现实相关性。因此,通过学习更精确的状态-动作值近似值,MAEFT可以触及更广泛的有效输入空间,大幅减少访问的重复实例数量,并识别更多针对现实特征相关性校准的唯一测试和id。与目前最先进的黑盒和白盒公平性测试方法相比,我们的方法平均多生成4.65% ~ 161.66%的唯一测试,多识别154.60% ~ 634.80%的idi,性能提升12.54% ~ 1313.47%。此外,MAEFT识别出的id可以很好地利用再训练来修复原始模型。这些idi导致模型公平性平均提高59.15%,比最先进的公平性测试方法高出15.94%-48.73%。用MAEFT再训练的模型的泛化能力比用最先进的公平性测试方法训练的模型强37.66% ~ 46.81%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Boosting Generalizable Fairness With Mahalanobis Distances Guided Boltzmann Exploratory Testing
Although machine learning models have been remarkably effective for decision-making tasks such as employment, insurance, and criminal justice, it remains urgent yet challenging to ensure model predictions are reliable and socially fair. This amounts to detecting and repairing potential discriminatory defects of machine learning models extensively with authentic testing data. In this paper, we propose a novel Mahalanobis distance guided Adaptive Exploratory Fairness Testing (MAEFT) approach, which searches for individual discriminatory instances (IDIs) through deep reinforcement learning with an adaptive extension of Boltzmann exploration, and significantly reduces overestimation. MAEFT uses Mahalanobis distances to guide the search with realistic correlations between input features. Thus, through learning a more accurate state-action value approximation, MAEFT can touch a much wider valid input space, reducing sharply the number of duplicate instances visited, and identify more unique tests and IDIs calibrated for the realistic feature correlations. Compared with state-of-the-art black-box and white-box fairness testing methods, our approach generates on average 4.65%-161.66% more unique tests and identifies 154.60%-634.80% more IDIs, with a performance speed-up of 12.54%-1313.47%. Moreover, the IDIs identified by MAEFT can be well exploited to repair the original models through retraining. These IDIs lead to, on average, a 59.15% boost in model fairness, 15.94%-48.73% higher than those identified by the state-of-the-art fairness testing methods. The models retrained with MAEFT also exhibit 37.66%-46.81% stronger generalization ability than those retrained with the state-of-the-art fairness testing methods.
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来源期刊
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering 工程技术-工程:电子与电气
CiteScore
9.70
自引率
10.80%
发文量
724
审稿时长
6 months
期刊介绍: IEEE Transactions on Software Engineering seeks contributions comprising well-defined theoretical results and empirical studies with potential impacts on software construction, analysis, or management. The scope of this Transactions extends from fundamental mechanisms to the development of principles and their application in specific environments. Specific topic areas include: a) Development and maintenance methods and models: Techniques and principles for specifying, designing, and implementing software systems, encompassing notations and process models. b) Assessment methods: Software tests, validation, reliability models, test and diagnosis procedures, software redundancy, design for error control, and measurements and evaluation of process and product aspects. c) Software project management: Productivity factors, cost models, schedule and organizational issues, and standards. d) Tools and environments: Specific tools, integrated tool environments, associated architectures, databases, and parallel and distributed processing issues. e) System issues: Hardware-software trade-offs. f) State-of-the-art surveys: Syntheses and comprehensive reviews of the historical development within specific areas of interest.
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