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中取得了成功(例如读者会点击哪些添加？或者说话者读的是哪个词？)，越来越多的证据表明，“最先进的”预测指标对少数群体的预测精度明显低于对多数群体的预测。事实上，一项对三种商用人脸识别系统的显著研究--被称为“性别阴影”项目--表明，在自然分类任务中，不同亚群的表现存在显著差异。对代表性不足的亚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人造成物质伤害。为了解决机器学习中的精确度差异和系统性偏差，该项目对不同人群存在的学习进行了原则性研究。该项目高度重视教育，为研究界提供服务，并广泛传播知识。研究活动将与课程开发、研究咨询(为所有级别的学生)、服务以及与其他科学界和大众写作的宣传相结合，并与之相结合。此外，在机器学习和大数据的时代，该项目的社会影响是双重的：确保算法适用于每个人，同时也确保算法发现所有社区中存在的所有潜在人才。该项目结合理论和经验调查，开发算法工具，以减轻子群体之间的系统性偏见，并回答为什么子群体之间的准确率差异最初会出现的基本科学问题。具体地说，该项目旨在提出并解决在从不同人群中沿着三个主要轴线学习时出现的问题：(1)改善对代表性不足人群的预测：能否开发出可证明不会忽视重要亚群的学习算法；(2)代表个人提高审计和修复模型的能力；(3)了解机器常见学习模型和算法中产生偏差的原因。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。

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.