| Titre : | Ethics and Trustworthiness of Algorithmic Decision-Making Systems |
| Auteurs : | OUissem Touameur, Auteur ; Fouzi Harrag, Directeur de thèse |
| Type de document : | document électronique |
| Editeur : | Sétif : Universite ferhat abbas faculté des sciences de l’ingénieur département d’informatique, 2026 |
| ISBN/ISSN/EAN : | E-TH/2566 |
| Format : | 1 vol. (139 f.) |
| Note générale : | Bibliogr. |
| Langues: | Français |
| Catégories : | |
| Résumé : |
The rapid adoption of artificial intelligence (AI) in high-stakes domains such as rec- ommender systems, healthcare, and disaster management has amplified the need for trustworthy systems. However, many AI models remain limited by opaque decision- making, biased or noisy data, and the lack of explicit mechanisms to model and quantify trust. This thesis addresses these limitations by integrating trust at three complementary levels—data, model, and prediction—through the combined use of knowledge graphs (KGs) and graph neural networks (GNNs). At the data and model levels, the thesis introduces a taxonomy of trust dimensions, including accuracy, reliability, provenance, fairness, robustness, and explainability, and demonstrates how structured knowledge and graph-based learning enhance transparency and relational reasoning. Building on this foundation, the first major contribution is GUITARES, a trust-aware recommender system based on graph attention networks. GUITARES integrates item confidence derived from external knowledge graphs, inferred user–user trust relationships, and structural learning over user–item graphs. Experimental evaluation shows that GUITARES achieves an RMSE of 0.80, outperforming state-of-the- art baselines while maintaining scalability and robustness. The second major contribution focuses on trust in predictions. The core framework, GraphSkinUQ, is proposed for skin cancer classification, combining CNN feature embed- dings, graph-based relational modeling, and uncertainty quantification to assess predictive confidence. GraphSkinUQ achieves 91% accuracy, with predictive uncertainty between 10% and 11%, a Brier score of 13%, an Expected Calibration Error (ECE) of 6%, and a ROC-AUC of 94%, demonstrating strong performance and well-calibrated confidence esti- mates. This predictive-trust framework is then extended to disaster management through the TDC-GCN model, which adapts the same principles—CNN features, graph convolu- tional learning, and Monte Carlo dropout—to disaster image classification. TDC-GCN achieves 97% accuracy with an entropy-based uncertainty measure of 30%, confirming its effectiveness in high-stakes scenarios. Overall, results across recommendation, medical imaging, and disaster analysis demon- strate that embedding trust mechanisms—from structured data modeling to uncertainty- aware predictions—significantly improves both performance and reliability. This thesis contributes to the development of AI systems that are not only accurate, but also transpar- ent, robust, and trustworthy. |
| Côte titre : | E-TH/2566 |
| En ligne : | http://dspace.univ-setif.dz:8888/jspui/retrieve/13133/final%20thesis.pdf |
Exemplaires (1)
| Cote | Support | Localisation | Disponibilité |
|---|---|---|---|
| E-TH/2566 | Thèse | Bibliothèque centrale | Disponible |
Accueil
