In conventional software development, the boundary between user experience (UX) design and engineering is well defined: designers create specifications based on end-user needs, then engineers build to those specifications. However, AI application design poses a challenge to this "separation of concerns." Emerging Human-AI guidelines show that human-centered design extends beyond the user interface and into the design of AI sub-components and training data, thus 'puncturing' this separation. In this talk, I will share insights about collaboration challenges at the AI-UX boundary and discuss approaches to operationalize the vision for human-centered AI. Based on studies with industry practitioners, I will describe how "leaky" abstractions afford collaboration across expertise boundaries and discuss the critical role of end-user data in generating both AI and UX design specifications. Finally, I will present an approach for prototyping AI-powered interfaces for diverse users and use contexts by directly incorporating end-user data and machine learning models within UX workflows.
Overview
Syllabus
Introduction.
Human-Centered Software Design Workflow.
Human Centered AIX.
USER INTERFACE.
Design-Engineering Boundary INTRODUCES KNOWLEDGE BLINDNESS.
Design-Engineering Boundary RESTRICTS COLLABORATION.
"Al-First" Design Workflow.
Leaky Abstractions ALLEVIATE KNOWLEDGE BLINDNESS.
Leaky Abstractions PROMOTES DESIGN COLLABORATION.
Leaky Abstractions SUPPORTS AIX EVALUATION.
What should Al do for Humans?.
A Process Model for Co-Creating Al Experiences.
DESIGN SPACE FOR MIP.
Expertise & Collaboration.
Taught by
Stanford Online
