"How Good Is Your Explanation?": Towards a Standardised Evaluation Approach for Diverse XAI Methods on Multiple Dimensions of Explainability

Author:

Bhattacharya Aditya1ORCID,Verbert Katrien1ORCID

Affiliation:

1. Computer Science, KU Leuven, Belgium

Funder

FWO

Publisher

ACM

Reference20 articles.

1. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

2. Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, and Himabindu Lakkaraju. 2022. OpenXAI: Towards a Transparent Evaluation of Model Explanations. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. https://openreview.net/forum?id=MU2495w47rz

3. Aditya Bhattacharya. 2022. Applied Machine Learning Explainability Techniques. In Applied Machine Learning Explainability Techniques. Packt Publishing, Birmingham, UK. https://www.packtpub.com/product/applied-machine-learning-explainability-techniques/9781803246154

4. Towards Directive Explanations: Crafting Explainable AI Systems for Actionable Human-AI Interactions

5. Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations

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