CAREER: From Dirty Data to Fair Prediction: Data Preparation Framework for End-to-End Equitable Machine Learning

职业：从脏数据到公平预测：端到端公平机器学习的数据准备框架

• 批准号: 2341055
• 负责人: Haewon Jeong
• 金额: $ 55.82万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2029-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2341055&HistoricalAwards=false
• 关键词: CAREER; Dirty; Data; Fair; Prediction

In an era where AI is becoming integrated into every facet of life, the need for AI systems that respect ethical expectations has never been more crucial. Modern AI algorithms learn from examples, and the creation of more ethical systems should start by supplying better examples to the algorithm. While collecting more quality data is usually very expensive, improving the quality of data by making better choices in the data-preparation stage adds minimal extra cost. This research departs from the current focus of considering ethical goals in the training phase, which is merely a small part of the end-to-end data science lifecycle, and targets the data-preparation pipeline as a strategic opportunity for eliminating unwanted bias and bolstering desirable ethical objectives. The project’s education outreach includes enhancing the understanding of ethical implications among AI students and the wider community and attracting and retraining female talents in the AI field.This award centers around the critical question: What are the fundamental downstream costs arising from fairness-unaware data preparation and how can we move toward end-to-end fairness through improved data preparation? Employing an information-theoretic lens, the PI will investigate how biased information flows from the original, dirty data to the clean training set, to the trained prediction model through the data-preparation pipeline. Specifically, the PI delves into prevalent real-world dataset problems, such as missing values, heterogeneity, and data imbalance, to examine how bias can be either amplified or mitigated while handling these issues. Motivated by this analysis, the project will devise fairness-aware data imputation, data encoding, and data-balancing techniques that can attain end-to-end ethical goals more effectively and efficiently.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.

在一个AI融入生活各个方面的时代，尊重道德期望的AI系统的需求从未如此重要。现代的AI算法从示例中学到的学习，更伦理的系统的创建应从为算法提供更好的示例开始。虽然收集更多质量数据通常非常昂贵，但通过在数据准备阶段做出更好的选择来提高数据质量，从而增加了最小的额外成本。这项研究偏离了当前在训练阶段考虑道德目标的重点，这仅仅是端到端数据科学生命周期的一小部分，而将数据预先准备管道作为消除不必要的偏见和不必要的道德目标的战略机会。该项目的教育宣传包括增强对AI学生和更广泛社区的道德意义的理解，并吸引和培训AI领域的女性才能。此奖项围绕一个关键问题的奖项：下游的基本下游成本是什么是由公平 - 统一数据准备以及我们如何通过改进的数据制备来实现终端的公平性？ PI采用信息理论镜头，将研究如何通过数据预先准备管道从原始的，肮脏的数据到清洁训练集到经过训练的预测模型。具体而言，PI研究了普遍的现实数据集问题，例如缺失值，异质性和数据不平衡，以研究如何在处理这些问题时如何扩展或减轻偏见。在该分析的促进的过程中，该项目将设计公平意识的数据插补，数据编码和数据平衡技术，这些技术可以更有效，更有效地实现端到端的道德目标。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力和更广泛的影响来通过评估来获得支持的珍贵的，以审查Criteria。