ProbLog4Fairness: A Neurosymbolic Approach to Modeling and Mitigating Bias
Rik Adriaensen, Lucas Van Praet, Jessa Bekker, Robin Manhaeve, Pieter Delobelle, Maarten Buyl
Abstract
Operationalizing definitions of fairness is difficult in practice, as multiple definitions can be incompatible while each being arguably desirable. Instead, it may be easier to directly describe algorithmic bias through ad-hoc assumptions specific to a particular real-world task, e.g., based on background information on systemic biases in its context. Such assumptions can, in turn, be used to mitigate this bias during training. Yet, a framework for incorporating such assumptions that is simultaneously principled, flexible, and interpretable is currently lacking. Our approach is to formalize bias assumptions as programs in ProbLog, a probabilistic logic programming language that allows for the description of probabilistic causal relationships through logic. Neurosymbolic extensions of ProbLog then allow for easy integration of these assumptions in a neural network
BibTeX
@inproceedings{aaai2026_problog4fairness,
title = {ProbLog4Fairness: A Neurosymbolic Approach to Modeling and Mitigating Bias},
author = {Rik Adriaensen and Lucas Van Praet and Jessa Bekker and Robin Manhaeve and Pieter Delobelle and Maarten Buyl},
booktitle = {AAAI 2026},
year = {2026}
}