Uncovering Discrimination Clusters: Quantifying and Explaining Systematic Fairness Violations
This program is tentative and subject to change.
Fairness in algorithmic decision-making is often framed in terms of individual fairness, which requires that similar individuals receive similar outcomes. A system violates individual fairness if there exists a pair of inputs differing only in protected attributes (such as race or gender) that lead to significantly different outcomes—for example, one favorable and the other unfavorable. While this notion highlights isolated instances of unfairness, it fails to capture broader patterns of systematic or \emph{clustered discrimination} that may affect entire subgroups.
We introduce and motivate the concept of \emph{discrimination clustering}, a generalization of individual fairness violations. Rather than detecting single counterfactual disparities, we seek to uncover regions of the input space where small perturbations in protected features lead to \emph{k-significantly distinct clusters} of outcomes. That is, for a given input, we identify a local neighborhood—differing only in protected attributes—whose members’ outputs separate into many distinct clusters. These clusters reveal significant arbitrariness in treatment solely based on protected attributes that help expose patterns of algorithmic bias that elude pairwise fairness checks.
We present HyFair, a hybrid technique that combines formal symbolic analysis (via SMT and MILP solvers) to certify individual fairness with randomized search to discover discriminatory clusters. This combination enables both formal guarantees—when no counterexamples exist—and the detection of severe violations that are computationally challenging for symbolic methods alone. Given a set of inputs exhibiting high k-unfairness, we introduce a novel explanation method to generate interpretable, decision-tree-style artifacts. Our experiments demonstrate that HyFair outperforms state-of-the-art fairness verification and local explanation methods. In particular, HyFair reveals that some benchmarks exhibit significant discrimination clustering, while others show limited or no disparities with respect to protected attributes. It also provides intuitive explanations to support understanding and mitigation of unfairness.
This program is tentative and subject to change.
Tue 18 NovDisplayed time zone: Seoul change
| 11:00 - 12:30 | |||
| 11:0010m Talk | SMTgazer: Learning to Schedule SMT Algorithms via Bayesian Optimization Research Papers Chuan Luo Beihang University, Shaoke Cui Beihang University, Jianping Song Beihang University, Xindi Zhang State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China, Wei Wu Central South University; Xiangjiang Laboratory, Chanjuan Liu Dalian University of Technology, Shaowei Cai Institute of Software at Chinese Academy of Sciences, Chunming Hu Beihang University | ||
| 11:1010m Talk | Efficient and Verifiable Proof Logging for MaxSAT Solving Research Papers | ||
| 11:2010m Talk | Destabilizing Neurons to Generate Challenging Neural Network Verification Benchmarks Research Papers | ||
| 11:3010m Talk | RELIA: Accelerating Analysis of Cloud Access Control Policies Research Papers Dan Wang Xi'an Jiaotong University, Peng Zhang Xi'an Jiaotong University, Zhenrong Gu Xi'an Jiaotong University, Weibo Lin Huawei Cloud, Shibiao Jiang Huawei Cloud, Zhu He Huawei Cloud, Xu Du Huawei Cloud, Longfei Chen Huawei Cloud, Jun Li Huawei, Xiaohong Guan Xi'an Jiaotong University | ||
| 11:4010m Talk | Evolution-Aware Heuristics for GR(1) Realizability Checking Research Papers Dor Ma'ayan Tel Aviv University, Shahar Maoz Tel Aviv University, Jan Oliver Ringert Bauhaus-University Weimar | ||
| 11:5010m Talk | Programmers’ Visual Attention on Function Call Graphs During Code Summarization Research Papers Samantha McLoughlin Vanderbilt University, Zachary Karas Vanderbilt University, Robert Wallace University of Notre Dame, Aakash Bansal Louisiana State University, Collin McMillan University of Notre Dame, Yu Huang Vanderbilt University | ||
| 12:0010m Talk | LLM-Assisted Synthesis of High-Assurance C Programs Research Papers Prasita Mukherjee Purdue University, Minghai Lu Purdue University, Benjamin Delaware Purdue UniversityPre-print | ||
| 12:1010m Talk | Faster Runtime Verification during Testing via Feedback-Guided Selective Monitoring Research Papers Shinhae Kim Cornell University, Saikat Dutta Cornell University, Owolabi Legunsen Cornell University | ||
| 12:2010m Talk | Uncovering Discrimination Clusters: Quantifying and Explaining Systematic Fairness Violations Research Papers Ranit Debnath Akash University of Illinois Chicago, Ashish Kumar Pennsylvania State University, Verya Monjezi University of Texas at El Paso, Ashutosh Trivedi University of Colorado Boulder, Gang (Gary) Tan Pennsylvania State University, Saeid Tizpaz-Niari University of Illinois Chicago | ||




