III: Small: Learning From Diverse Populations: A Complexity-Theoretic Perspective

尽管机器学习在复杂的预测和分类任务上取得了成功(比如添加一个阅读器会点击？，越来越多的证据表明，“最先进”的预测方法在少数族裔人群中的准确性明显低于在多数族裔人群中的准确性。事实上，一项著名的对三个商业人脸识别系统的研究，即“性别阴影”项目，证明了不同亚群在自然分类任务中的显著表现差距。对代表性不足的亚种群的系统性错误限制了机器学习预测系统的整体效用，并可能对少数群体的个体造成物质伤害。为了解决机器学习过程中的准确性差异和系统性偏差，该项目对不同人群下的学习进行了原则性研究。该项目高度重视教育、服务研究界和广泛传播知识。这些研究活动将伴随着课程开发、研究建议（针对所有层次的学生）、服务以及向其他科学界和大众写作的拓展，并与之相结合。此外，在机器学习和大数据时代，该项目的社会影响是双重的：确保算法适用于每个人，同时确保算法发现所有潜在的人才，这些人才存在于所有社区。该项目将理论和实证调查相结合，开发算法工具，以减轻亚种群之间的系统性偏差，并回答有关为什么亚种群之间的准确性差异首先出现的基本科学问题。具体而言，该项目旨在沿三个主要轴询问和解决在从不同人群中学习的背景下出现的问题：(1)改进对代表性不足人群的预测：是否可以开发出可以证明不会忽略重要子人群的学习算法，(2)代表个人提高审计和修复模型的能力，(3)了解机器通用学习模型和算法中偏差的原因。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。

Despite the successes of machine learning at complex prediction and classification tasks (such as which add a reader will click? or which word a speaker pronounced?), there is growing evidence that "state-of-the-art" predictors can perform significantly less accurately on minority populations than on the majority population. Indeed, a notable study of three commercial face recognition systems, known as the "Gender Shades" project demonstrated significant performance gaps across different subpopulations at natural classification tasks. Systematic errors on underrepresented subpopulations limit the overall utility of machine-learned prediction systems and may cause material harm to individuals from minority groups. To address accuracy disparity and systematic biases throughout machine learning, the project pursue a principled study of learning in the presence of diverse populations. The project puts high value on education, service to the research community, and wide dissemination of knowledge. The research activities will be accompanied by and integrated with curriculum development, research advising (for students at all levels), service, and outreach to other scientific communities and in popular writing. In addition, in the age of machine-learning and big data, the project's societal impact is twofold: making sure that algorithms work for everyone but also making sure algorithms uncover all potential talent, which exists in all communities.The project combines theoretical and empirical investigations to develop algorithmic tools for mitigating systematic bias across subpopulations and to answer basic scientific questions about why discrepancy in accuracy across subpopulations emerges in the first place. Specifically, the project aims to ask and resolve questions that arise in the context of learning from diverse populations along three main axes: (1) Improving predictions for underrepresented populations: Can learning algorithms be developed that provably do not overlook significant subpopulations, (2) Representing individuals to improve the ability to audit and repair models, (3) Understanding the causes for biases in machine common learning models and 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.