Building trust in AI
decisions

The TRUST Framework is an operational backbone turning Responsible AI into a measurable, actionable reality. By focusing on Transparency, Robustness, Unbiased outcomes, Security, and Testing, the TRUST Framework provides a rigorous standard for evaluating and building AI systems that inspire confidence and ensure long-term sustainability, offering a clear pathway to implement and validate Responsible AI across all parts of the ecosystem.

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Recent Publications

Decoupling Inference from State Updates in Low-Latency Feature Engines via Probabilistic Thinning

Augusto Peres, Iker Perez, Pedro Valdeira, Guilherme Jardim, Ana Sofia Gomes, Hugo Ferreira, Pedro Bizarro

PDF | arXiv

The Balance between Nuance and Clarity: Decluttering Tabular Sequential Graphs to Counter Money Laundering

Salomé Esteves, Rita Costa, Louise Fallon (Mastercard), Pedro Bizarro

arXiv | Supplemental Material (examples) | Supplemental Material (Algorithm Definitions)

Uncertainty-Aware Systems for Human-AI Collaboration

Vasco Pearson, Jean V. Alves, Jacopo Bono, Mário A. T. Figueiredo, Pedro Bizarro

Published at Transactions on Machine Learning Research

PDF | GitHub