AAAI 2024technical0 citations
Data Efficient Paradigms for Personalized Assessment of Black-Box Taskable AI Systems
Abstract
The vast diversity of internal designs of taskable black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. My dissertation focuses on developing paradigms that enable a user to assess and understand the limits of an AI system's safe operability. We develop a personalized AI assessment module that lets an AI system execute instruction sequences in simulators and answer queries about these executions. Our results show that such a primitive query-response interface is sufficient to efficiently derive a user-interpretable model of a system's capabilities.
BibTeX
@article{Verma_2024, title={Data Efficient Paradigms for Personalized Assessment of Black-Box Taskable AI Systems}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30414}, DOI={10.1609/aaai.v38i21.30414}, abstractNote={The vast diversity of internal designs of taskable black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. My dissertation focuses on developing paradigms that enable a user to assess and understand the limits of an AI system’s safe operability. We develop a personalized AI assessment module that lets an AI system execute instruction sequences in simulators and answer queries about these executions. Our results show that such a primitive query-response interface is sufficient to efficiently derive a user-interpretable model of a system’s capabilities.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Verma, Pulkit}, year={2024}, month={Mar.}, pages={23427-23428} }