From black boxes to explainable matchmaking: transparency, privacy and governance in enterprise AI
Sabatino, Benedetta (A.A. 2024/2025) From black boxes to explainable matchmaking: transparency, privacy and governance in enterprise AI. Tesi di Laurea in Data privacy and security, Luiss Guido Carli, relatore Paolo Spagnoletti, pp. 107. [Master's Degree Thesis]
|
PDF (Full text)
Download (2MB) | Preview |
Abstract/Index
Explainable artificial intelligence: origins, evolution and interpretability techniques. Black box problem. Evolution of XAI techniques. Interpretability techniques: approaches and debates. Explainable AI in practice: case studies in healthcare, finance, HR and cybersecurity. Methodologies for B2B matchmaking in digital business ecosystems. Introduction to B2B matchmaking. Comparative analysis. Methodology. Introduction to the problem and methodological approach. The dataset. Model architecture and training: engineering a system for semantic compatibility. Evaluation methodologies: measuring effectiveness and ensuring transparency. Results and discussion. Validation in the cyber 4.0 context. Quantitative results and model performance. Explainability and transparency analysis (XAI).
References
Bibliografia: pp. 93-99.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91) |
| Chair: | Data privacy and security |
| Thesis Supervisor: | Spagnoletti, Paolo |
| Thesis Co-Supervisor: | Coppa, Emilio |
| Academic Year: | 2024/2025 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 03 Mar 2026 08:02 |
| Last Modified: | 03 Mar 2026 08:02 |
| URI: | https://tesi.luiss.it/id/eprint/45024 |
Downloads
Downloads per month over past year
Repository Staff Only
 |
View Item |
