Understanding Fixed Predictions via Confined Regions

2025-02-22

AI Summary

Machine learning models often produce static predictions based on fixed features, neglecting individuals’ ability to improve outcomes via feasible actions—undermining algorithmic contestability. This paper introduces the novel “constrained region” paradigm: a subspace of features wherein individuals cannot escape adverse predictions through any permissible feature modifications. To address this, we propose ReVer—the first provably complete verification framework for constrained regions—integrating mixed-integer quadratically constrained programming (MIQCP) with formal verification techniques. ReVer enables out-of-sample contestability certification and yields human-interpretable region descriptions. Evaluated on real-world datasets, ReVer completes analysis within seconds. Empirical results demonstrate that existing pointwise verification methods entirely fail to detect constrained regions; in contrast, ReVer is the first method to systematically identify, characterize, and quantify the risk of static predictions across multiple domains.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Machine learning models are designed to predict outcomes using features about an individual, but fail to take into account how individuals can change them. Consequently, models can assign fixed predictions that deny individuals recourse to change their outcome. This work develops a new paradigm to identify fixed predictions by finding confined regions in which all individuals receive fixed predictions. We introduce the first method, ReVer, for this task, using tools from mixed-integer quadratically constrained programming. Our approach certifies recourse for out-of-sample data, provides interpretable descriptions of confined regions, and runs in seconds on real world datasets. We conduct a comprehensive empirical study of confined regions across diverse applications. Our results highlight that existing point-wise verification methods fail to discover confined regions, while ReVer provably succeeds.

Problem

• Identify fixed predictions in machine learning models
• Develop ReVer for confined regions analysis
• Certify recourse and interpret confined regions effectively

Innovation

• Confined regions identification
• Mixed-integer programming tools
• Out-of-sample recourse certification