Julian Skirzyński

About

I am a Ph.D. candidate in Computer Science at UC San Diego, advised by Berk Ustun, and a member of ARIA, the NSF AI Research Institute on Interaction for AI Assistants. I work at the intersection of machine learning, human-computer interaction, and cognitive science. I study when the properties we build into AI actually help the people who use them, and how to account for the heuristics and biases people carry over to AI. To answer these questions, I work towards a science of human-AI collaboration: I measure how people work with AI, learn what they need, and engineer systems that meet those needs. For instance, I found that people overrely on interpretable models they are asked to supervise, yet the same interpretability, used to teach decision strategies, measurably improved their decisions. I pursue this science wherever people cannot or should not fully defer to AI outputs, e.g., in policy, health, and finance.

Before my Ph.D., I was a research scientist at the Max Planck Institute for Intelligent Systems, where I worked with Falk Lieder on interpretable reinforcement learning and interventions to improve human planning. I hold an M.S. in Computer Science from McGill University, and an M.S. in Cognitive Science and a B.S. in Mathematics and Cognitive Science from the University of Warsaw. You can access my full CV here and read my research statement here.

Recent news

• Aug 2026Attended the Summer Bootcamp on Trustworthy AI at Brown University.
• Jun 2026New preprint: Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models.
• Apr 2026Started a new project on the faithfulness of LLM justifications.
• Dec 2025Cognitive Bias Induction in LLMs accepted at IJCNLP 2025.
• Jun 2025Presented Discrimination Exposed? at ACM FAccT 2025 in Athens.

Selected Papers

• 
  Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models
  Julian Skirzyński, Harry Cheon, Shreyas Kadekodi, Meredith Stewart, Berk Ustun
  In Submission, 2026
  [Paper] [Code]
• 
  On the Value of Interpretability in Human Decision-Making
  Julian Skirzyński, Elena Glassman, Berk Ustun
  In Submission, 2025
  paper & code coming soon
• FAccT
  Discrimination Exposed? On the Reliability of Explanations for Discrimination Detection
  Julian Skirzyński, David Danks, Berk Ustun
  ACM Conference on Fairness, Accountability, and Transparency, 2025
  [Paper] [Code]
• IJCNLP-AACL

  
  Quantifying Cognitive Bias Induction in LLM-Generated Content
  Abeer Alessa, Param Somane, Akshaya Thenkarai Lakshminarasimhan, Julian Skirzyński, Julian McAuley, Jessica Maria Echterhoff
  IJCNLP-AACL, 2025
  [Paper]
• Computational Brain & Behavior

  
  Automatic discovery and description of human planning strategies
  Julian Skirzyński, Yash Raj Jain, Falk Lieder
  Computational Brain & Behavior, 2023
  [Paper] [Code]
• Computational Brain & Behavior

  
  Boosting Human Decision‑making with AI‑Generated Decision Aids
  Frederic Becker, Julian Skirzyński, Falk Lieder
  Computational Brain & Behavior, 2022
  [Paper] [Code]
• Machine Learning Journal

  
  Automatic discovery of interpretable planning strategies
  Julian Skirzyński, Frederic Becker, Falk Lieder
  Machine Learning Journal, 2021
  [Paper] [Code]