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1. Motivations for explainable artificial intelligence
Over the past decade, there has been a clear conceptual trend in artificial intelligence literature. It is interesting to note the latent need for interpretable AI models over time (which is intuitively true, as interpretability is a requirement in many fields). However, it was not until 2017-2018 that interest in techniques for explaining AI models became widespread in the scientific and R&D community (figure 1 ).
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Motivations for explainable artificial intelligence
Conceptual landscape of explicability
Bibliography
- (1) - WANG (J.) et al - Learning Credible Models. - In : Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (juill. 2018). arXiv : 1711.03190, p. 2417-2426. doi : 10.1145/3219819. 3220070. URL : http://arxiv.org/...
In the Laws section
Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL ESTABLISHING HARMONIZED RULES FOR ARTIFICIAL INTELLIGENCE (ARTIFICIAL INTELLIGENCE LEGISLATION) AND AMENDING CERTAIN UNION LEGISLATIVE ACTS. Brussels, 21.4.2021.
Websites
Royal Society – Projects
https://royalsociety.org/topics-policy/projects
IBM-Explainable AI
https://www.ibm.com/fr-fr/watson/explainable-ai
Kaggle
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