HCII PhD Thesis Proposal: Anna Kawakami
When
-
Where
NSH 3305
Description
Thesis title: Designing for the Future of Socially Complex Work With and For Workers
Committee members:
Kenneth Holstein (co-chair), Carnegie Mellon University
Haiyi Zhu (co-chair), Carnegie Mellon University
Jodi Forlizzi, Carnegie Mellon University
Alexandra Chouldechova, Microsoft Research
Krzysztof Gajos, Harvard University
Zoom: https://cmu.zoom.us/j/91406880344?pwd=PkWJdhaJINUsqRRLS8dBZdZ7yobJPB.1 (Meeting ID: 914 0688 0344, Password: 140149)
Abstract:
AI systems are increasingly used to assist socially complex forms of human work, centered on reasoning about and responding to the needs, behaviors, and intentions of other people. For example, AI-based tools are designed to support care workers in providing socio-emotional support or to support social service workers in making decisions about how best to allocate scarce resources. However, in practice, such systems often fail to meaningfully augment worker abilities. How can we design towards more positive futures for AI and workers in socially complex work, where AI deployments truly complement and enhance worker expertise and capabilities? In my research, I explore this question in close collaboration with organizations and workers in feminized labor contexts such as social work and education, where worker expertise has historically been devalued and misunderstood.
In the first thread of my research, I conduct field observations, design research, experimental studies, and case study analyses to advance our understanding of why AI for socially complex work often fails to meaningfully support workers. Through a series of studies, I explore this question across multiple levels, investigating worker-level, organization-level, and societal-level challenges, and I identify opportunities for future work to address these challenges. In the second thread of my research, I explore the design of new processes and tools to begin addressing these opportunities. First, I explore whether the design of new training interfaces might help social workers learn to use AI tools more critically. Second, I collaborate with public sector agencies and community groups across the US to develop a new toolkit to support multi-stakeholder deliberations around proposed AI projects. Finally, in my proposed research, I will expand on a collaboration with a local school social work organization to design and test a new approach that engages workers in the design of AI measurement instruments (e.g., benchmarks and metrics) to ensure that AI evaluations are meaningful, interpretable, and valid from the perspective of workers who will be using them.