CRII: III: Fair Machine Learning with Restricted Access to Sensitive Personal Data

机器学习越来越多地被应用于辅助结果决策，通常是通过学习一个模型来自动对人的潜力进行评分，并对得分较高的人进行有利决策的优先排序。虽然这可以使决策更加有效和基于证据，但最近的研究表明，许多模型评分对少数群体存在偏见，并可能导致负面的社会影响。这引发了人们对开发公平的机器学习技术的浓厚研究兴趣，这些技术可以缓解模型评分中的人口统计学偏差。问题是，现有的发展正在与数据隐私法规发生冲突。具体地说，大多数公平的学习技术需要自由访问一个人的敏感人口统计数据，但后者越来越多地被限制在保护个人隐私的使用上。关于是否允许或有必要在公平的机器学习中使用敏感的人口统计数据，人们一直在争论，但由于缺乏科学调查，迄今尚未达成共识。该项目旨在填补这一空白，不仅是为了建立公平和隐私之间的基本关系，也是为了扩大公平学习技术在现实世界应用中的部署和影响；该项目还将通过让未被充分代表的学生参与计算机科学研究和创建关于伦理机器学习的新课程来产生重要的教育影响，以培训下一代具有伦理意识的数据科学家。该项目将开发新的公平机器学习技术，并限制访问敏感人口统计数据(SDD)。将制定和解决三种情况：SDD不可访问、SDD可以付费访问以及SDD可以通过私人第三方访问。为了应对这些情况，这项计划将把公平目标与各种复杂的学习技术结合起来，包括迁移学习、主动学习、分布式学习和私人学习。该项目还将调查机器学习中公平和隐私之间的一个基本关系，即如果一个人在学习模型时必须保护敏感人口统计数据的某些隐私，那么在模型的评分中可以实现多少公平。开发的解决方案将在会议和期刊上展示，项目网站将提供对结果的访问，并提供开发和评估算法的代码参考。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Machine learning is increasingly applied to assist consequential decision makings, typically by learning a model to automatically score people's potential and prioritizing advantaged decisions on those receiving higher scores. While this enables more efficient and evidence-based decision makings, recent studies show that many model scorings are biased against minority people and can result in negative societal impacts. This has triggered intensive research interests in developing fair machine learning techniques that can mitigate demographic bias in model scoring. The problem is, existing developments are running into a conflict with data privacy regulations. To be specific, most fair learning techniques require free access to one's sensitive demographic data, but the latter is increasingly restricted to use for protecting one's privacy. There are ongoing debates on whether it is permissible or necessary to use sensitive demographic data in fair machine learning, but so far no consensus has been reached due to the lack of scientific investigations. This project aims to fill this gap, not only for establishing a fundamental relation between fairness and privacy but also for broadening the deployment and impact of fair learning techniques in real-world applications; the project will also have an important educational impact via the involvement of underrepresented students in computer science research and the creation of a new curriculum on ethical machine learning to train the next-generation ethics-aware data scientists.This project will develop novel fair machine learning techniques with restricted access to sensitive demographic data (SDD). Three scenarios will be formulated and solved: where SDD is not accessible, where SDD can be accessed with cost, and where SDD can be accessed through a private third party. To tackle these scenarios, this project will integrate fairness objectives with a variety of sophisticated learning techniques including transfer learning, active learning, distributed learning and private learning. The project will also investigate a fundamental relation between fairness and privacy in machine learning, that is, how much fairness can be achieved in a model's scoring if one has to protect certain privacy of the sensitive demographic data when learning the model. The developed solutions will be presented in conference and journal venues, and the project website will provide access to the results, with references to the codes for the developed and evaluated algorithms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.