Enhancing Explanaibility in AI: Food Recommender System Use Case
Melissa Tessa, Sarah Abchiche, Yves Claude Ferstler, Igor Tchappi, Karima Benatchba, Amro Najjar · 2023
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.