arXiv cs.CY· Theresa Elstner, Martin Potthast·· 11 小时前AI 评分20
面向算法决策中人类表征的唯一性感知特征工程框架
Uniqueness-Aware Feature Engineering for Representing Humans in Algorithmic Decision Making
AI 导读
研究者提出唯一性感知特征工程框架，将特征工程建模为对有限训练人群候选特征的随机选择过程，用多元超几何模型度量个体被输入表征覆盖的程度并识别"最未被代表"的个体。第二层以集合相似度在群体表征拟合与预测准确率之间寻优，并落地为六步流水线，用合成数据完成原理验证。该框架不预设未见个体的先验知识，可输出可发布的表征拟合报告，为部署机构提供问责依据、为被决策者提供个人表征透明度。
正文 · 原文
Abstract:Fairness analyses of algorithmic decision systems typically treat the feature representation of individuals as an a priori fixed input. How well a feature representation captures what is actually decision-relevant for different people and their unique backgrounds, however, is typically left unexamined. We address this gap by modeling feature engineering as a stochastic selection process over the pooled characteristics as candidate features for a finite training population. Based on this model, we develop a new framework for uniqueness-aware feature engineering which operates in two layers. The first layer measures population heterogeneity using a multivariate hypergeometric model of feature prevalence. It shows how well an individual is represented by any given input representation and which individual of the population is "least represented" compared to their personal, reference characteristics. The second layer acts on this assessment using set-similarity measures to search for an optimum between representational fit of the population and predictive accuracy. Without assuming prior knowledge about unseen individuals, our framework grants represented humans agency over what counts as decision-relevant in their case, gives deploying institutions accountability through a publishable representation fit report, and provides each person subject to a decision transparency about their personal representation fit. We operationalize our framework as a six-step pipeline and show a proof-of-principle using synthetic data. Our approach offers a blueprint for making algorithmic decision systems accountable for how well they represent individuals during development and in production.
| Subjects: | Computers and Society (cs.CY); Human-Computer Interaction (cs.HC) |
| ACM classes: | K.4 |
| Cite as: | arXiv:2611.09403 [cs.CY] |
| (or arXiv:2611.09403v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2611.09403 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Theresa Elstner [view email]
[v1]
Fri, 6 Nov 2026 12:37:12 UTC (539 KB)
来源：arXiv cs.CY · arxiv.org