all AI news
Multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers
March 22, 2024, 4:42 a.m. | Ignacy St\k{e}pka, Mateusz Lango, Jerzy Stefanowski
cs.LG updates on arXiv.org arxiv.org
Abstract: Counterfactuals are widely used to explain ML model predictions by providing alternative scenarios for obtaining the more desired predictions. They can be generated by a variety of methods that optimize different, sometimes conflicting, quality measures and produce quite different solutions. However, choosing the most appropriate explanation method and one of the generated counterfactuals is not an easy task. Instead of forcing the user to test many different explanation methods and analysing conflicting solutions, in this …
abstract arxiv cs.ai cs.lg ensemble generated predictions quality set type
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Senior Data Engineer
@ Cint | Gurgaon, India
Data Science (M/F), setor automóvel - Aveiro
@ Segula Technologies | Aveiro, Portugal