Conference Papers Year : 2024

Bias, Subjectivity and Norm in Large Language Models

Abstract

This article reevaluates the concept of bias in Large Language Models, highlighting the inherent and varying nature of these biases and the complexities involved in post hoc adjustments to meet legal and ethical standards. It argues for shifting the focus from seeking bias-free models to enhancing transparency in filtering processes, tailored to specific use cases, acknowledging that biases reflect societal values.
Fichier principal
Vignette du fichier
aequitas.pdf (564.04 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Licence

Dates and versions

hal-04838836 , version 1 (15-12-2024)

Licence

Identifiers

  • HAL Id : hal-04838836 , version 1

Cite

Thierry Poibeau. Bias, Subjectivity and Norm in Large Language Models. Aequitas (Fairness and Bias in AI), Oct 2024, Saint Jacques de Compostelle, Spain. ⟨hal-04838836⟩
11 View
11 Download

Share

More