FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data

2026-06-22 · 16:02 UTC ·Marcel Hedman, Emily Alger, Brieuc Lehmann... ·1 min read

Brief

FairBED presents a fairness-aware Bayesian experimental design approach that quantifies dataset fairness by treating fair datasets as uninformative about sensitive attributes, then optimizes acquisitions to maximize expected information gain about target variables while minimizing gain about sensitive attributes. The authors derive a theoretical connection to demographic parity and report empirical improvements in fairness–accuracy trade-offs; summary based on the abstract (full text not reviewed).

Why it matters

Introduces FairBED (Marcel Hedman, Emily Alger, Brieuc Lehmann, Chris Holmes, Tom Rainforth): a fairness-aware Bayesian experimental design framework that quantifies dataset fairness by requiring datasets be uninformative about sensitive attributes and builds BED objectives to optimize data acquisition accordingly.

Key details

FairBED's objectives maximize expected information gain about target quantities while minimizing expected information gain about sensitive attributes, and the paper derives a theoretical link between FairBED and demographic parity.

Empirically, models trained on data gathered with FairBED yield improved fairness–accuracy trade-offs compared to randomly acquired data and conventional BED (arXiv:2606.23515v1; published 2026-06-22).

Abstract

Frameworks for ensuring fairness in machine learning typically focus on learning fair models from existing data. But this endeavor is often undermined by biases already present in that data. We therefore look to modify the data acquisition process itself to help gather fairer data that is inherently more suitable for training fair predictors. To this end, we introduce FairBED, which provides novel formulations for quantifying the fairness of datasets themselves based on the idea that fair datasets should be uninformative about sensitive attributes. We then use this to construct practical fairness-aware Bayesian experimental design (BED) objectives that maximize expected information gain about the target quantity of interest while minimizing expected information gain about sensitive attributes. We further derive a theoretical link between FairBED and demographic parity, and show empirically that models trained on data gathered using FairBED provide improved fairness-accuracy trade-offs compared to randomly acquired data and conventional BED.