A review of taxonomies of explainable artificial intelligence (XAI) methods

T Speith - Proceedings of the 2022 ACM conference on fairness …, 2022 - dl.acm.org
The recent surge in publications related to explainable artificial intelligence (XAI) has led to
an almost insurmountable wall if one wants to get started or stay up to date with XAI. For this …

A survey of algorithmic recourse: contrastive explanations and consequential recommendations

AH Karimi, G Barthe, B Schölkopf, I Valera - ACM Computing Surveys, 2022 - dl.acm.org
Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …

A survey on the explainability of supervised machine learning

N Burkart, MF Huber - Journal of Artificial Intelligence Research, 2021 - jair.org
Predictions obtained by, eg, artificial neural networks have a high accuracy but humans
often perceive the models as black boxes. Insights about the decision making are mostly …

Human-centered explainable ai (xai): From algorithms to user experiences

QV Liao, KR Varshney - arxiv preprint arxiv:2110.10790, 2021 - arxiv.org
In recent years, the field of explainable AI (XAI) has produced a vast collection of algorithms,
providing a useful toolbox for researchers and practitioners to build XAI applications. With …

A historical perspective of explainable Artificial Intelligence

R Confalonieri, L Coba, B Wagner… - … Reviews: Data Mining …, 2021 - Wiley Online Library
Abstract Explainability in Artificial Intelligence (AI) has been revived as a topic of active
research by the need of conveying safety and trust to users in the “how” and “why” of …

Explaining by removing: A unified framework for model explanation

I Covert, S Lundberg, SI Lee - Journal of Machine Learning Research, 2021 - jmlr.org
Researchers have proposed a wide variety of model explanation approaches, but it remains
unclear how most methods are related or when one method is preferable to another. We …