1. LEAFAGE: example-based and feature importance-based explanations for black-box ML models;Adhikari;Fuzzy Syst. Conf.,2019
2. Towards robust interpretability with self-explaining neural networks;Alvarez-Melis;Adv. Neural Inf. Process. Syst.,2018
3. Py-CIU: a Python library for explaining machine learning predictions using contextual importance and utility;Anjomshoae,2020
4. Visualizing the effects of predictor variables in black box supervised learning models;Apley;J. R. Stat. Soc. Ser. B Stat. Methodol.,2016
5. Explainable explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI;Barredo Arrieta;Inf. Fusion,2020