Biological and artificial information processing systems form representations that they can
use to categorize, reason, plan, navigate, and make decisions. How can we measure the …

People are relying on AI agents to assist them with various tasks. The human must know
when to rely on the agent, collaborate with the agent, or ignore its suggestions. In this work …

In this paper, we address the concept of" alignment" in large language models (LLMs)
through the lens of post-structuralist socio-political theory, specifically examining its parallels …

People's decision-making abilities often fail to improve or may even erode when they rely on
AI for decision-support, even when the AI provides informative explanations. We argue this …

Language models are transforming the ways that their users engage with the world. Despite
impressive capabilities, over-consumption of language model outputs risks propagating …

In settings where users both need high accuracy and are time-pressured, such as doctors
working in emergency rooms, we want to provide AI assistance that both increases decision …

As AI assistance is increasingly infused into decision-making processes, we may seek to
optimize human-centric objectives beyond decision accuracy, such as skill improvement or …

Humans learn about the world, and how to act in the world, in many ways: from individually
conducting experiments to observing and reproducing others' behavior. Different learning …

Developing machine learning models worthy of decision-maker trust is crucial to using
models in practice. Algorithmic transparency tools, such as explainability and uncertainty …

AI systems are augmenting humans' capabilities in settings such as healthcare and
programming, forming human-AI teams. To enable more accurate and timely decisions, we …