提出新方法解决算法公平性中的结构偏见与个体孤立问题
Statistical and Structural Approaches to Algorithmic Fairness
- 用统计与结构化方法重构公平性审计框架
- 揭示现有方法依赖点估计的局限性
- 适合关注社会公平与模型可解释性的研究者
现代机器学习系统已超越单一预测工具，演变为复杂的人机技术架构，主动影响人类机会分配。随着算法越来越多地决定经济与社会资源的获取，人们普遍认识到这些系统深嵌于环境中的结构性不平等与偏见。算法公平性领域应运而生，以应对仅追求预测准确率的模型可能系统性歧视弱势群体的问题。然而，早期缓解策略基于脆弱的简化假设，在复杂社会技术环境中效果有限。本文识别并解决当代公平范式的两个根本局限：对审计中确定性点估计的依赖，以及将个体视为脱离结构背景的孤立实体。
原文摘要 · Abstract (English)
Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures that actively mediate human opportunity. As algorithms increasingly determine access to economic and social opportunities, it has become widely recognized that these systems are deeply embedded with the structural inequalities and prejudices of their environments. The field of algorithmic fairness emerged in response to the growing recognition that models optimized for predictive accuracy can systematically disadvantage marginalized groups. Early mitigation strategies, however, rested on fragile simplifications that limited their effectiveness in complex socio-technical environments. This thesis identifies and addresses two fundamental limitations of contemporary fairness paradigms: the reliance on deterministic point estimates for auditing and the treatment of individuals as isolated entities devoid of structural context.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。