Published January 1, 2023
| Version v1
Conference paper
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Enhancing Explainability in AI: A Personalized Food Recommender System Use Case
Creators
- 1. Ecole Natl Super Informat Alger ESI ex INI, Algiers, Algeria
- 2. Univ Luxembourg, AI Robolab, ICR, Esch Sur Alzette, Luxembourg
Description
As automated decision-making systems proliferate, accountability becomes crucial. Developers must ensure adherence to regulations and fairness. Explainable AI offers a remedy by crafting algorithms that provide precise outcomes and understandable explanations. This paper focuses on food recommender system interpretability for better health. Integrating explainable AI empowers users to make informed dietary decisions. The proposed framework generates natural language explanations for recommendations using the prompting technique, demonstrating superior performance and broad applicability across domains.
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