SOURCE-LINKED INTELLIGENCE

Guaranteed Uncertainty for InformeD DEcisions

CORDIS · observation · Publication date unknown

Guaranteed Uncertainty for InformeD DEcisions As machine learning (ML) is prevalent today to support decision-making, the European approach is to create an environment of trust for a fair AI through a unified regulatory framework, to ensure trust from both ‘‘people and companies’’. In line with this ambition, recent research efforts from the statistics and ML communities have been devoted to equipping ML models with provably valid tools for predictive uncertainty quantification (UQ), via methodological developments. We can now turn any point prediction into a guaranteed prediction set, or post-process estimated probabilities to get calibrated ones. Yet, a crucial question remains: what does this offer to the downstream pipeline? This project targets this gap by: A) characterizing the trade-offs between the two UQ holy grails, namely fairness (via conditional validity) and tight evaluation of the underlying model’s error (via sharpness)

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recordType

award

status

SIGNED

region

EU

value

338337.36

unit

EUR