FOUNDRY

The foundations of robustness and reliability in artificial intelligence

Preview

Develop the theoretical and methodological foundations of robustness and reliability needed to build and instill trust in AI technologies and systems.

Panayotis Mertikopoulos, Research Director at CNRS

The core vision of FOUNDRY is that robustness in AI – a desideratum which has eluded the field since its inception – cannot be achieved by blindly throwing more data and computing power to larger and larger models with exponentially growing energy requirements. Instead, we intend to rethink and develop the core theoretical and methodological foundations of robustness and reliability that are needed to build and instill trust in ML-powered technologies and systems from the ground up.

Keywords : Robustness, reliability, game theory, trust, fairness, privacy

Project website : In progress

Missions

Our researches


Achieving resilience to data-centric impediments

Develop algorithms and methodologies for overcoming shortfalls in the models’ training set (outliers, incomplete observations, label shifts, poisoning, etc.), as well as fortifying said models against impediments that arise at inference time.


Adapting to unmodeled phenomena and the environment

Develop the required theoretical and technical tools for AI systems that are able to adapt “on the fly” to non-stationary environments and gracefully interpolate from best- to worst-case guarantees .


Attaining robustness in the presence of concurrent aims and goals

Delineate how robustness criteria interact with standard performance metrics (e.g., a model’s predictive accuracy) and characterize the fundamental performance limits of ML models when the data are provided by selfishly-minded agents.

Consortium

CNRS, Université Paris-Dauphine, INRIA, Institut Mines Télécom, Ecole normale supérieure de Lyon, Université de Lille, ENSAE Paris, Ecole Polytechnique Palaiseau

Consortium location

Publication


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