CAREER: Achieving Explainable Artificial Intelligence (AI) through Human-AI Interaction

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

The ability to explain and justify the decisions made by Artificial Intelligence (AI) is of critical importance to technology-driven innovation and broad societal adoption of AI. To explain AI means to not only describe how the AI makes its decisions and what it bases those decisions on, but also which decisions the AI is good at making and which ones it is not. Such explanations enable people to decide whether they should trust AI or not. Making AI explainable is increasingly important as AI-based technology gets deployed into many high-stakes decision-making scenarios, such as in government, judiciary, healthcare, and across many industries. However, most existing approaches to explain AI focus on computer science-savvy AI creators rather than end-users, such as other domain experts using AI in decision-making, policy makers regulating AI, and consumers interacting with AI-based systems. Explaining AI to end-users could help them understand what decisions the AI is making and why, and how those decisions impact them and society more broadly. The goal of this project is to democratize AI explanations by delivering them to end-users through carefully designed model- and domain-agnostic human-AI interactions without sacrificing the performance of AI. This project grounds the process of seeking and deriving AI explanations in sensemaking theory. In this, the end-user updates their mental model about how the AI works in two steps: 1) foraging for evidence of the relationship between different AI inputs and outputs, and 2) assigning meaning to the evidence they obtain to form and test hypotheses about possible AI explanations. The project will contribute new scientific knowledge about empirically-validated mechanisms that deliver meaningful, comprehensive, and accurate explanations about the AI to end-users. The findings from this project will expand the breadth of existing methods and tools that enable AI testing, end-user advocacy, public education, and investigative journalism about AI. This project will also generate design guidelines that will help increase access to future AI-based technology for a broad audience of end-users. 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.