Influenciæ: A library for tracing the influence back to the data-points

Agustin Martin Picard (1, 2) , Lucas Hervier (1, 2) , Thomas Fel (3, 4, 2) , David Vigouroux (1, 2)

1 IRT Saint Exupéry - Institut de Recherche Technologique
2 ANITI - Artificial and Natural Intelligence Toulouse Institute
3 Carney Institute for Brain Science [Providence]
4 SNCF : Innovation & Recherche

In today’s AI-driven world, understanding model behavior is becoming more important than ever. While libraries abound for doing so via traditional XAI methods, the domain of influence-based techniques for data-centric explanations remains mostly underserved. To fill this void, we introduce Influenciæ, an open-source library that implements the state-of-the-art methods for estimating the influence of training points on the model, with a focus on efficiency and scalability to fit the needs and the recent trends in the field. Finally, we have thoroughly documented and included plenty of tutorials to make the library reachable to the public, in the hopes that it will bring these methods back into the spotlight.

Mots clés: Data-centric ai; XAI; Explainability; Influence Functions; Open-source toolbox

Domaines: Machine Learning [stat.ML]; Intelligence artificielle [cs.AI]

Agustin Martin Picard, Lucas Hervier, Thomas Fel, David Vigouroux. Influenciæ: A library for tracing the influence back to the data-points. 2023. ⟨hal-04284178⟩