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It is taken that current research practices of improving one or two aspects in isolation are insufficient and research on Trustworthy AI must account for interactions between aspects and adopt a holistic view across all relevant axes at once.
This work incorporates fairness constraints directly into TFM training, enabling fair predictions in a single forward pass and proposes FairTFM, a scalable training strategy based on synthetic fairness tasks and a fairness-aware architecture using a gradient reversal layer, which encourages the model to learn represent...
The Conformal Relevance framework is introduced which uses in-context learning example curation and ensembling to create a score function which maintains coverage while improving conciseness with minimal manual input.
Causal foundation models are pretrained neural networks that estimate causal quantities, such as the average treatment effect, on entirely new datasets using in-context learning without requiring model updates.
LakeQuest is introduced, a human-validated benchmark of 9,846 QA pairs designed to evaluate the end-to-end retrieve-and-synthesize pipeline over realistic data lakes and exposes critical failure modes in modern QA systems.
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