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

机器学习越来越多地应用于辅助重大决策，通常是通过学习一个模型来自动对人们的潜力进行评分，并优先考虑得分较高的人做出有利的决策。虽然这有助于更有效和基于证据的决策，但最近的研究表明，许多模型评分对少数民族有偏见，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强烈研究兴趣，这些技术可以减轻模型评分中的人口统计学偏见。问题是，现有的发展与数据隐私法规发生了冲突。具体来说，大多数公平的学习技术都需要免费获取个人敏感的人口统计数据，但后者越来越多地被限制用于保护个人隐私。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，一直存在争论，但由于缺乏科学调查，到目前为止还没有达成共识。该项目旨在填补这一空白，不仅建立公平与隐私之间的基本关系，而且扩大公平学习技术在现实世界应用中的部署和影响；该项目还将通过让代表性不足的学生参与计算机科学研究，以及创建关于伦理机器学习的新课程，培养下一代具有伦理意识的数据科学家，从而产生重要的教育影响。该项目将开发新的公平机器学习技术，限制对敏感人口统计数据（SDD）的访问。制定并解决三种场景：SDD不可访问、SDD可以有成本访问、SDD可以通过私有第三方访问。为了解决这些问题，该项目将公平目标与各种复杂的学习技术相结合，包括迁移学习、主动学习、分布式学习和私有学习。该项目还将研究机器学习中公平与隐私之间的基本关系，即如果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场所展示，项目网站将提供对结果的访问，并参考开发和评估的算法的代码。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.