Explaining Algorithm Aversion with Metacognitive Bandits. - Aakriti Kumar; Trisha Patel; Aaron S. Benjamin; Mark Steyvers (CogSci, 2021)

Meta-Learning of Dynamic Policy Adjustments in Inhibitory Control Tasks. - Soumya Chatterjee 002; Aakriti Kumar; Pradeep Shenoy (CogSci, 2022)

An Empirical Investigation of Reliance on AI-Assistance in a Noisy-Image Classification Task. - Heliodoro Tejeda Lemus; Aakriti Kumar; Mark Steyvers (HHAI, 2022)

Capturing Humans' Mental Models of AI: An Item Response Theory Approach. - Markelle Kelly; Aakriti Kumar; Padhraic Smyth; Mark Steyvers (FAccT, 2023)

How Displaying AI Confidence Affects Reliance and Hybrid Human-AI Performance. - Heliodoro Tejeda Lemus; Aakriti Kumar; Mark Steyvers (HHAI, 2023)

When Do Drivers Intervene In Autonomous Driving? - Aakriti Kumar; Kumar Akash; Shashank Mehrotra; Teruhisa Misu; Mark Steyvers (HRI (Companion), 2023)

The Calibration Gap between Model and Human Confidence in Large Language Models. - Mark Steyvers; Heliodoro Tejeda Lemus; Aakriti Kumar; Catarina G. Belém; Sheer Karny; Xinyue Hu; Lukas William Mayer; Padhraic Smyth (CoRR, 2024)

Human Mental Models of Self, Others, and AI Agents - Aakriti Kumar (2024)

Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images. - Negar Kamali; Karyn Nakamura; Aakriti Kumar; Angelos Chatzimparmpas; Jessica Hullman; Matthew Groh (CHI, 2025)

What large language models know and what people think they know. - Mark Steyvers; Heliodoro Tejeda Lemus; Aakriti Kumar; Catarina G. Belém; Sheer Karny; Xinyue Hu; Lukas William Mayer; Padhraic Smyth (Nat. Mac. Intell., 2025)

When Large Language Models are Reliable for Judging Empathic Communication. - Aakriti Kumar; Nalin Poungpeth; Diyi Yang; Erina Farrell; Bruce Lambert; Matthew Groh (CoRR, 2025)