Continuous Fairness On Data Streams

TL;DR

This paper introduces a novel approach for enforcing continuous group fairness at a finer granularity within data stream windows, using efficient monitoring and reordering algorithms supported by theoretical guarantees, demonstrated on real-world data.

Contribution

It proposes a new block-level fairness model for data streams, along with sketch-based monitoring and optimal reordering algorithms, enabling real-time fairness enforcement with high efficiency.

Findings

Achieves millisecond-level processing and 30,000 queries/sec throughput.

Reordering improves block fairness by up to 95%.

Block-level fairness outperforms window-level fairness qualitatively.

Abstract

We study the problem of enforcing continuous group fairness over windows in data streams. We propose a novel fairness model that ensures group fairness at a finer granularity level (referred to as block) within each sliding window. This formulation is particularly useful when the window size is large, making it desirable to enforce fairness at a finer granularity. Within this framework, we address two key challenges: efficiently monitoring whether each sliding window satisfies block-level group fairness, and reordering the current window as effectively as possible when fairness is violated. To enable real-time monitoring, we design sketch-based data structures that maintain attribute distributions with minimal overhead. We also develop optimal, efficient algorithms for the reordering task, supported by rigorous theoretical guarantees. Our evaluation on four real-world streaming scenarios demonstrates the practical effectiveness of our approach. We achieve millisecond-level processing and a throughput of approximately 30,000 queries per second on average, depending on system parameters. The stream reordering algorithm improves block-level group fairness by up to 95% in certain cases, and by 50-60% on average across datasets. A qualitative study further highlights the advantages of block-level fairness compared to window-level fairness.