研究机器学习对社会的影响，提出评估公平性的方法与减缓歧视的策略。
On the Societal Impact of Machine Learning
- 提出系统分解方法，预判算法偏见的演化路径。
- 开发可衡量公平性的工具，兼顾系统性能与公平性。
- 适合关注算法伦理、政策制定的研究者参考。
本博士论文研究机器学习（ML）的社会影响。随着数据驱动系统越来越多地参与关键决策，其对生活各方面产生深远影响。由于这些系统常缺乏显式公平性考量，存在歧视性风险。本文贡献包括：更准确测量ML系统公平性的方法，系统分解以预测偏见动态，以及在保持系统效用的同时有效减少算法歧视的干预措施。最后讨论了持续挑战与未来方向，尤其在生成式人工智能日益融入社会的背景下。该研究为确保机器学习的社会影响符合社会价值提供基础。
原文摘要 · Abstract (English)
This PhD thesis investigates the societal impact of machine learning (ML). ML increasingly informs consequential decisions and recommendations, significantly affecting many aspects of our lives. As these data-driven systems are often developed without explicit fairness considerations, they carry the risk of discriminatory effects. The contributions in this thesis enable more appropriate measurement of fairness in ML systems, systematic decomposition of ML systems to anticipate bias dynamics, and effective interventions that reduce algorithmic discrimination while maintaining system utility. I conclude by discussing ongoing challenges and future research directions as ML systems, including generative artificial intelligence, become increasingly integrated into society. This work offers a foundation for ensuring that ML's societal impact aligns with broader social values.
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