UC Santa Cruz

Artificially intelligent systems play an increasingly large role in our everyday lives. These intelligent systems make decisions big and small—from who gets a mortgage to what articles we read online. However, these systems are often designed out of convenience; they are built using data that is available, they do not explain the decisions they make, and sometimes they are simply inaccurate. At best, these problems result in systems that are difficult to use; at worst, these problems result in reinforcing societal biases at scale. Rather than designing systems from a standpoint of convenience, my work centers the human experience in the design of intelligent systems. I show how to work with users to build maximally accurate systems. I demonstrate that common intelligent systems are biased and not equally usable by everyone but also that this can be fixed through careful data collection. I show what users need when a system explains itself and how we can build our systems to explain the right thing at the right time. Finally, I move this work out of the lab and into the wild through a field study with expert users of an explainable AI system. Together, I use these studies to make recommendations that ensure we meet the needs of our human users in the intelligent systems that we build.