About

Welcome! We are a research group based at Dartmouth College's Department of Computer Science in beautiful Hanover, New Hampshire.


Our lab studies the first-mile and last-mile problems of AI systems (e.g. agents) in human environments.

The first mile is about understanding AI systems themselves. We draw on techniques from behavioral evaluation, interpretability (e.g. mechanistic), and representation learning to build better tools for reasoning about these systems.

The last mile is about deployment: how AI systems behave in real workflows, under real incentives, and in open-ended human settings. Our aim is to make these systems more robust and beneficial under the pressures of actual use.

We treat this as both a scientific and a design problem. Scientifically, we want to understand how human-AI systems work. From a design perspective, we want to shape how they interact with people in practice. We call this dual focus the science and art of human-AI systems.

Our work is domain-general, with current applied efforts in areas like creativity, music, healthcare, and decision-making.

Photo of Engineering and Computer Science Center at Dartmouth College

Keywords: Machine Learning, Human-Computer Interaction, Human Behavior, Creativity, Generative Models, Interactive Systems, Interpretability, Steerability, Human-AI Collaboration, Augmented Intelligence, Cognitive Tools, Human-Centered AI, Human-AI Co-Evolution, Human-in-the-Loop Systems

साहस • (sāhas) — stem noun: a Hindi word often translated as courage, intrepidity, principled boldness. The resolve to confront uncertainty with inner strength.

Research Pillars

We work across the stack of human-AI systems:

SAHAS Research Vision Diagram showing anchor points: The first mile of AI behavior, The last mile of AI integration, Closing the loop of human-AI co-adaptation. The points are overlayed on top of a diagram showing a cyclical relationship between humans and AI using arc shaped arrows.
1

The first mile of AI behavior

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What behavioral strategies and internal structure allow AI systems to do the work they do?

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2

The last mile of AI integration

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How can agents remain controllable and interpretable when embedded in real workflows?

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3

Closing the loop of human-AI co-adaptation

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How do AIs and humans co-adapt over time?

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