5 papers
Position: AI Evaluations Should be Grounded on a Theory of Capability
Nathanael Jo, Ashia Wilson
Evaluations of generative models are now ubiquitous, and their outcomes critically shape public and scientific expectations of AI's capabilities. Yet skepticism about their reliabi…
Alignment has a Fantasia Problem
Nathanael Jo, Zoe De Simone, Mitchell Gordon +1
In accomplishing complex tasks, human cognition typically progresses from abstract to concrete (e.g., from brainstorming ideas to writing an essay). With the advent of highly capab…
Incentives shape how humans co-create with generative AI
Nathanael Jo, Manish Raghavan
Generative AI is quickly becoming an integral part of people's everyday workflows. Early evidence has shown that while generative AI can increase individual-level productivity, it…
The Subjectivity of Monoculture
Nathanael Jo, Nikhil Garg, Manish Raghavan
Machine learning models -- including large language models (LLMs) -- are often said to exhibit monoculture, where outputs agree strikingly often. But what does it actually mean for…
Homogeneous Algorithms Can Reduce Competition in Personalized Pricing
Nathanael Jo, Kathleen Creel, Ashia Wilson +1
Firms' algorithm development practices are often homogeneous. Whether firms train algorithms on similar data, aim at similar benchmarks, or rely on similar pre-trained models, the…