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Clara Kliman-Silver

Clara Kliman-Silver

Clara is a UX Researcher on the Material Design team with a focus on designer-developer workflows and artificial intelligence. Previously, she designed and researched for AI and healthcare companies, and she holds an Sc.B. in cognitive science from Brown University.

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    Exploring the future of design tooling: The role of artificial intelligence in tools for user experience professionals
    Tiffany Knearem
    Mohammed Kamal Khwaja
    Yuling Gao
    Frank Richard Bentley
    Clara Emma Kliman-Silver
    ACM CHI 2023 (2023)
    Preview abstract Recently, artificial intelligence (AI) has been introduced into a variety of consumer applications for creative work. Although AI-driven features in design tooling are nascent, there is growing interest in utilizing AI to support user experience (UX) workflows. In this case study, we surveyed industry UX professionals ("UXers") to understand how they perceive AI-driven assists in their tools, their concerns about accepting AI in design tools and which design-related workflows could be promising for future research. Our results suggest that UXers are overall positive about AI-driven features in design tools; looking to AI as a creative partner to iterate with and as an assistant with mundane tasks. We offer practical directions for the future of AI in UX tooling, but caution against developing tools that do not sufficiently address UXer's concerns around bias and trust. View details
    Adapting User Experience Research Methods for AI-Driven Experiences
    Clara Emma Kliman-Silver
    Oliver Siy
    Kira Awadalla
    Alison Lentz
    Gregorio Convertino
    Elizabeth Churchill
    ACM CHI 2020 (2020)
    Preview abstract This short paper describes how to adapt user experience research methods for artificial intelligence (AI)-driven applications. Presently, there is a dearth of guidance for conducting UX research on AI-driven experiences. We describe what makes this class of experiences unique, propose a preliminary foundational framework to categorize AI-driven experiences, and within the framework we show an example of methodological adaptations via a case study. View details
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