6 papers
The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Products
Hao-Ping Lee, Jessica He, David Piorkowski +3
Agentic AI systems act autonomously, use tools, adapt to context, and operate in complex real-world environments. However, these same characteristics can create or exacerbate produ…
Why Johnny Can't Use Agents: Industry Aspirations vs. User Realities with AI Agents
Pradyumna Shome, Sashreek Krishnan, Sauvik Das
There is growing imprecision about what "AI agents" are, what they can do, and how effectively they can be used by their intended users. We pose two key research questions: (i) How…
WhatIf: Interactive Exploration of LLM-Powered Social Simulations for Policy Reasoning
Yuxuan Li, Kyzyl Monteiro, Hirokazu Shirado +1
Policymakers in domains such as emergency management, public health, and urban planning must make decisions under deep uncertainty, where outcomes depend on how large populations i…
How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?
Yuxuan Li, Leyang Li, Hao-Ping Lee +1
A growing body of research assumes that large language model (LLM) agents can serve as proxies for how people form attitudes toward and behave in response to security and privacy (…
What Makes LLM Agent Simulations Useful for Policy Practice? An Iterative Design Study in Emergency Preparedness
Yuxuan Li, Sauvik Das, Hirokazu Shirado
Policymakers must often act under conditions of deep uncertainty, such as emergency response, where predicting the specific impacts of a policy apriori is implausible. Large Langua…
Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Models
Yuxuan Li, Hirokazu Shirado, Sauvik Das
While advances in fairness and alignment have helped mitigate overt biases exhibited by large language models (LLMs) when explicitly prompted, we hypothesize that these models may…