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 am developing a science of human-AI collaboration to help people become smarter with AI. I elicit what people need from AI, measure how well AI systems meet those needs, and engineer systems that meet them measurably better. For instance, I showed that people who use interpretability to supervise models overrely on their predictions. In contrast, when we engineered a system that uses interpretability to teach users decision-making strategies, their performance improved. Many other properties that we need from AI to make us smarter may not even have names yet. I search for them across policy, health, finance, and other high-stakes areas where people cannot or should not fully defer to AI outputs.

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]
• 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]