Abstracts
Abstract
This study uses folk theories of the Spotify music recommender system to inform the principles of human-centered explainable AI (HCXAI). The results show that folk theories can reinforce, challenge, and augment these principles facilitating the development of more transparent and explainable recommender systems for the non-expert, lay public.
Keywords:
- Folk theories,
- human-centered explainable artificial intelligence (HCXAI),
- recommender systems,
- explanations
Résumé
Cette étude utilise les théories populaires du système de recommandation de musique de Spotify pour éclairer les principes de l'IA explicable centrée sur l'humain (HCXAI). Les résultats montrent que les théories populaires peuvent renforcer, remettre en question et augmenter ces principes, facilitant le développement de systèmes de recommandation plus transparents et explicables pour le public non initié et non expert.
Mots-clés :
- Théories populaires,
- intelligence artificielle explicable centrée sur l'humain (HCXAI),
- systèmes de recommandation,
- explications
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Appendices
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