Rik Adriaensen
@rik-a3.bsky.social
Joint work with @lucasvanpraet.bsky.social, @jessabekker.bsky.social, @robinmanhaeve.bsky.social, Pieter Delobelle, and Maaten Buyl. Find the preprint at:
arxiv.org/abs/2511.09768
arxiv.org/abs/2511.09768
ProbLog4Fairness: A Neurosymbolic Approach to Modeling and Mitigating Bias
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 algor...
arxiv.org
January 22, 2026 at 9:52 AM
Joint work with @lucasvanpraet.bsky.social, @jessabekker.bsky.social, @robinmanhaeve.bsky.social, Pieter Delobelle, and Maaten Buyl. Find the preprint at:
arxiv.org/abs/2511.09768
arxiv.org/abs/2511.09768
💡 ProbLog4Fairness bridges this gap. It shows how to declaratively specify causes of bias using probabilistic logic in a principled, flexible, and interpretable way. Neurosymbolic extensions allow integrating these assumptions in the training of a classifier, to learn fair models from biased data!
January 22, 2026 at 9:52 AM
💡 ProbLog4Fairness bridges this gap. It shows how to declaratively specify causes of bias using probabilistic logic in a principled, flexible, and interpretable way. Neurosymbolic extensions allow integrating these assumptions in the training of a classifier, to learn fair models from biased data!
🔍 How to integrate fairness assumptions into ML models?
In algorithmic fairness, many definitions of fairness exist, but they often contradict. Rather than choosing one definition, causal models reason about why bias arises in data. However, practitioners struggle to operationalize these models.
January 22, 2026 at 9:52 AM
🔍 How to integrate fairness assumptions into ML models?
In algorithmic fairness, many definitions of fairness exist, but they often contradict. Rather than choosing one definition, causal models reason about why bias arises in data. However, practitioners struggle to operationalize these models.
🔍 How to integrate fairness assumptions into ML models?
In algorithmic fairness, many definitions of fairness exist, but they often contradict. Rather than choosing one definition, causal models reason about why bias arises in data. However, practitioners struggle to operationalize these models.
January 22, 2026 at 9:48 AM
🔍 How to integrate fairness assumptions into ML models?
In algorithmic fairness, many definitions of fairness exist, but they often contradict. Rather than choosing one definition, causal models reason about why bias arises in data. However, practitioners struggle to operationalize these models.