I-Corps: A Software Platform to Customize, Inspect and Improve Artificial Intelligence (AI) Systems

University Of Maryland, College Park, College Park MD

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is the development of a software platform to make Artificial Intelligence (AI) models more reliable. Artificial intelligence is rapidly becoming a part of everyday businesses and organizations. However, key concerns in using AI systems are their lack of reliability and explainability, and their lack of transparency with respect to internal workings where output inferences and predictions are not interpretable. This makes the process of developing AI models and inspecting and mitigating their failure modes time-consuming and challenging. The proposed technology is designed to automate developing, inspecting and improving AI models using another AI system that uses human feedback in its optimization. Understanding and mitigating reliability issues of AI models may mitigate the risks of their deployment in practice. In addition, these efforts may democratize the reliable use of AI systems by non-experts and increase human trust in these systems. This I-Corps project is based on the development of an automated and unified software platform that provides multi-modal interpretability and reliability analysis and monitoring tools to design, train, inspect, and improve Artificial Intelligence (AI) systems. The proposed technology is designed to automatically uncover and address hidden reliability issues within AI models employing the user’s unique data. It simplifies the complex process of identifying and mitigating potential reliability risks and explainability challenges, which may help to ensure AI models deliver trustworthy and accurate results. In addition, users may compare hundreds of AI models and select the ones with the maximum efficiency and reliability for their specific applications. It also interactively incorporates user feedback in its optimization to improve reliability and explainability of AI models while reliability becomes transparent and manageable, empowering users to make informed decisions with increased confidence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.