VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AI

arXiv:2403.05174v1 Announce Type: new
Abstract: Trustworthy AI is crucial to the widespread adoption of AI in high-stakes applications with fairness, robustness, and accuracy being some of the key trustworthiness metrics. In this work, we propose a controllable framework for data-centric trustworthy AI (DCTAI)- VTruST, that allows users to control the trade-offs between the different trustworthiness metrics of the constructed training datasets. A key challenge in implementing an efficient DCTAI framework is to design an online value-function-based training data subset selection …

abstract accuracy adoption applications arxiv control cs.lg data data-centric fairness framework function key metrics robustness the key trustworthy trustworthy ai type value work