The Blame Problem in Evaluating Local Explanations, and How to Tackle it

5 October 2023

Amir Hossein Akhavan Rahnama

ELM

FAtt

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Papers citing "The Blame Problem in Evaluating Local Explanations, and How to Tackle it"

7 / 7 papers shown

XAI-Units: Benchmarking Explainability Methods with Unit TestsConference on Fairness, Accountability and Transparency (FAccT), 2025

Jun Rui Lee

Sadegh Emami

Michael David Hollins

Timothy C. H. Wong

Carlos Ignacio Villalobos Sánchez

Francesca Toni

Dekai Zhang

Adam Dejl

242

01 Jun 2025

Time series saliency maps: explaining models across multiple domains

Christodoulos Kechris

Jonathan Dan

David Atienza

AI4TS FAtt

380

19 May 2025

Explanations Go Linear: Post-hoc Explainability for Tabular Data with Interpretable Meta-Encoding

1.2K

29 Apr 2025

Hidden Conflicts in Neural Networks and Their Implications for ExplainabilityConference on Fairness, Accountability and Transparency (FAccT), 2023

434

31 Oct 2023

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective

Himabindu Lakkaraju

951

257

03 Feb 2022

A Unified Approach to Interpreting Model Predictions

Scott M. Lundberg

Su-In Lee

FAtt

5.2K

32,979

22 May 2017

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

2.7K

21,359

16 Feb 2016