80 citations · 182 across the 20 of their papers we have counts for
6 papers · 1 filter
ProbLog4Fairness: A Neurosymbolic Approach to Modeling and Mitigating Bias
Rik Adriaensen, Lucas Van Praet, Jessa Bekker +3
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 dir…
Building and Measuring Trust between Large Language Models
Maarten Buyl, Yousra Fettach, Guillaume Bied +1
As large language models (LLMs) increasingly interact with each other, most notably in multi-agent setups, we may expect (and hope) that `trust' relationships develop between them,…
BiMi Sheets: Infosheets for bias mitigation methods
MaryBeth Defrance, Guillaume Bied, Maarten Buyl +2
Over the past 15 years, hundreds of bias mitigation methods have been proposed in the pursuit of fairness in machine learning (ML). However, algorithmic biases are domain-, task-,…
What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices
Sander Noels, Guillaume Bied, Maarten Buyl +4
Large Language Models (LLMs) are increasingly deployed as gateways to information, yet their content moderation practices remain underexplored. This work investigates the extent to…
Biased Heritage: How Datasets Shape Models in Facial Expression Recognition
Iris Dominguez-Catena, Daniel Paternain, Mikel Galar +3
In recent years, the rapid development of artificial intelligence (AI) systems has raised concerns about our ability to ensure their fairness, that is, how to avoid discrimination…
AI Alignment at Your Discretion
Maarten Buyl, Hadi Khalaf, Claudio Mayrink Verdun +3
In AI alignment, extensive latitude must be granted to annotators, either human or algorithmic, to judge which model outputs are `better' or `safer.' We refer to this latitude as a…