Leveraging Tropical Algebra to Assess Trustworthy AI
Year: 2024
Type: conference-paper
Abstract: Given the complexity of the application domain, the qualitative and quantifiable nature of the concepts involved, the wide heterogeneity and granularity of trustworthy attributes, and in some cases the non-comparability of the latter, assessing the trustworthiness of AI-based systems is a challenging process. In order to overcome these challenges, the Confiance.ai program proposes an innovative solution based on a Multi-Criteria Decision Aiding (MCDA) methodology. This approach involves several stages: framing trustworthiness as a set of well-defined attributes, exploring attributes to determine related Key Performance Indicators (KPI) or metrics, selecting evaluation protocols, and defining a method to aggregate multiple criteria to estimate an overall assessment of trust. This approach is illustrated by applying the RUM methodology (Robustness, Uncertainty, Monitoring) to ML context, while the focus on aggregation methods are based on Tropical Algebra.
Authors: Juliette Mattioli, Martin Gonzalez, Lucas Mattioli, Karla Quintero, Henri Sohier
Institutions: Thales (France), Institut de Recherche Technologique SystemX
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4403323919