Human-AI Interaction | Natural Language Processing | Computational Social Science

I study how people work with AI, so it makes work better to do, not just faster.

I’m a senior applied scientist at the IDEAS Research group at Microsoft, led by Scott Counts.

I study human-AI collaboration in work that requires expertise and judgment. My research examines how people communicate their needs and interact with AI, how LLMs behave and can be steered, and how using AI changes people’s work. I use interaction traces to make this collaboration visible over time, examining how people steer AI, evaluate its outputs, and recover from failures. I ask when AI expands people’s capabilities, when it creates additional work, and how people can stay in control.

I did my PhD in the Minnesota NLP group with Prof. Dongyeop Kang, co-advised by Prof. Jaideep Srivastava, and interned as a doctoral student at Microsoft Research and 3M R&D.

Research focus

01

Human-AI collaboration in expert workflows

Understand people's goals, work contexts, and collaboration with AI to design assistance that fits their needs.

02

Characterizing and steering LLM behavior

Characterize how language models reason and generate, and how inputs and steering shape their behavior.

03

Evaluating consequences of AI

Examine the benefits, burdens, and demands of AI use on people, with attention to oversight and meaningful control.

Coming soon

Explore Recent publications full list

Each circle is one paper. Hover for details; click to open it.

Publications

2026

preprint

Babysitting AI Agents: A User-Centered Taxonomy and Study of Agentic Delegation Failures

S. Mittal, S. Kim, S. Counts, J. Neville, Debarati Das*, P. Laban*

CHI 2027, under submission

workshop

COGENT: a modular cognitive architecture for agentic user simulation

Y. Chi, A. Davies, Debarati Das, L. Ai, S. Counts

USERSIM at NeurIPS

preprint

Not Just My Style: Toward Contextual Personalization in AI-Assisted Workplace Communication

Debarati Das, S. Mysore, C. St.Hill, D. Kang, B. Sarrafzadeh

TIIS

journal

Thinking Like A Lawyer In The Age Of Generative AI: Cognitive Limits On AI Adoption Among Lawyers

Daniel Schwarcz, Debarati Das, Dongyeop Kang, Brett H. McDonnell

Journal of Institutional and Theoretical Economics

Paper

2025

conference

Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wild

Sheshera Mysore, Debarati Das, Hancheng Cao, Bahareh Sarrafzadeh

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing

Paper
conference

LawFlow: Collecting and Simulating Lawyers' Thought Processes on Business Formation Case Studies

Debarati Das, Khanh Chi Le, Ritik Sachin Parkar, Karin De Langis, Brendan Madson, Chad M. Berryman, Robin M. Willis, Daniel H. Moses, Brett McDonnell, Daniel Schwarcz, Dongyeop Kang

Second Conference on Language Modeling (COLM)

Paper

2024

preprint

Under the surface: Tracking the artifactuality of llm-generated data

Debarati Das, Karin De Langis, Anna Martin-Boyle, Jaehyung Kim, Minhwa Lee, Zae Myung Kim, Shirley Anugrah Hayati, Risako Owan, Bin Hu, Ritik Parkar, others

arXiv preprint arXiv:2401.14698

Paper
conference

Which Modality should I use-Text, Motif, or Image?: Understanding Graphs with Large Language Models

Debarati Das, Ishaan Gupta, Jaideep Srivastava, Dongyeop Kang

Findings of the Association for Computational Linguistics: NAACL 2024

Paper

2023

conference

Balancing the Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer

Debarati Das, David Ma, Dongyeop Kang

Findings of the Association for Computational Linguistics: ACL 2023

Paper