Persuasive Explanation of Reasoning Inferences on Dietary Data

By Ivan Donadello, M. Dragoni, C. Eccher. 2019

20 citations recorded by the source corpus.

Subfields: Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety, Robustness & Interpretability

1. Adrien Bennetot
2. Elena Hernández-Pereira
3. Eduardo Mosqueira-Rey
4. Á. Fernández-Leal
5. José Bobes-Bascarán
6. David Alonso-Ríos
7. Natalia Díaz Rodríguez
8. Raja Chatila

Citations in the corpus

• Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “A successful use case is on dietary recommendations, where explanations are extracted from the reasoning behind (non-deep but KB-based) models [301] .”
• Human-in-the-loop machine learning: a state of the art (2022, background): “A successful use case is on dietary recommendations, where explanations are extracted from the reasoning behind (non-deep but KB-based) models (Donadello et al. 2019).”
• Towards Explainable Neural-Symbolic Visual Reasoning (2019, methodology): “The use of a Knowledge Base (KB) to learn and reason with symbolic representation has the advantage of promoting the production of explanations while making a prediction [Donadello et al., 2019].”