- Thursday, May 14, 2020 - 2:30pm
John Zimmerman, Co-Chair (HCII, CMU)
Aaron Steinfeld, Co-Chair (RI, CMU)
Carolyn Rosé (LTI & HCII, CMU)
Saleema Amershi (Microsoft Research AI)
From predictive medicine to autonomous driving, advances in Artificial Intelligence (AI) promise to improve people's lives and improve society. As AI systems migrated from research labs into the real world, new challenges emerged. For example, when and how should predictive models fit into physicians' decision-making workflow such that the predictions impact them appropriately? These are challenges of translation: translating AI from a remarkable technological achievement into a real-world, socio-technical system that serves human ends. My research focuses on this critical translation; on the user experience (UX) design of AI.
The prevalence of AI suggests that the UX design community has effective design methods and tools to excel at this translation. While this is true in many cases, some challenges persist. For example, designers struggle with accounting for AI systems' unpredictable errors, and these errors damage UX and even lead to undesirable societal impacts. UX designers routinely grapple with unanticipated system or human failures, with a focus on mitigating technologies unintended consequences. What makes AI different from other interactive technologies? -- A critical first step in systematically addressing the UX design challenges of AI is to articulate what makes AI so difficult to design in the first place.
This dissertation delineates whether, when, and how UX of AI is uniquely difficult to design. I synthesize prior UX and AI research, my own experience designing human-AI interactions, my studies of experienced AI innovation teams in the industry, and my observations from teaching human-AI interaction. I trace the nebulous UX design challenges of AI back to just two root challenges: uncertainty around AI's capabilities and the complexity of what systems might output. I present a framework that unravels their effects on design processes; namely "AI's design complexity framework". Using the framework, I identify four levels of AI systems. On each level, designers are likely to encounter a different subset of design challenges.
I further demonstrate the usefulness of this framework for UX research and practice through two case studies. In both cases, I engaged stakeholders in their real-world contexts and addressed a critical challenge in fitting AI into people's everyday lives. The first is the design of a clinical decision-support system that can effectively collaborate with doctors in making life-and-death decisions. It exemplifies level 1 systems (probabilistic systems) which current design methods can readily elicit, address, and evaluate their UX issues. The second project is an investigation of how Natural Language Generation systems might seamlessly serve the authors’ communicative intent. This illustrates level 4 systems (evolving, adaptive systems). It reveals the limits of UX design methods and processes widely in use today.
By teasing apart the challenges of routine UX design and those distinctively needed for AI, the framework helps UX researchers and design tool makers to address AI design challenges in a targeted fashion.
- Queenie Kravitz