Stream-Based Monitoring of Algorithmic Fairness

2025-01-30

AI Summary

In high-stakes domains such as credit assessment and judicial risk prediction, real-time monitoring of cross-group fairness in automated decision-making systems remains challenging. Method: This paper proposes a runtime fairness monitoring framework for data streams, formally specifying algorithmic fairness requirements in Real-Time Lola (RTLola)—a temporal stream logic—and designing a lightweight architecture supporting dynamic verification, online statistical testing, and streaming execution. Contribution/Results: The approach overcomes expressiveness and scalability limitations of conventional static fairness analysis. Evaluated on the real-world COMPAS dataset and diverse synthetic benchmarks, it achieves millisecond-scale monitoring latency while accurately detecting group-level disparities. The framework ensures theoretical soundness—grounded in formal verification—and practical deployability, bridging the gap between rigorous fairness guarantees and production-grade streaming systems.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Time-Series/Data Streams
• Game Theory and Economic Paradigms: Fair Division

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Automatic decision and prediction systems are increasingly deployed in applications where they significantly impact the livelihood of people, such as for predicting the creditworthiness of loan applicants or the recidivism risk of defendants. These applications have given rise to a new class of algorithmic-fairness specifications that require the systems to decide and predict without bias against social groups. Verifying these specifications statically is often out of reach for realistic systems, since the systems may, e.g., employ complex learning components, and reason over a large input space. In this paper, we therefore propose stream-based monitoring as a solution for verifying the algorithmic fairness of decision and prediction systems at runtime. Concretely, we present a principled way to formalize algorithmic fairness over temporal data streams in the specification language RTLola and demonstrate the efficacy of this approach on a number of benchmarks. Besides synthetic scenarios that particularly highlight its efficiency on streams with a scaling amount of data, we notably evaluate the monitor on real-world data from the recidivism prediction tool COMPAS.

Problem

• Algorithmic Bias
• Fairness Monitoring
• Automated Decision Systems

Innovation

• Real-time Fairness Monitoring
• Stream-based Algorithm
• RTLola for Fairness Rules