25 citations · 46 across the 5 of their papers we have counts for
5 papers
Programmers Are Poor and Overconfident Judges of LLM-Generated Assertions
Zhanna Kaufman, Yuriy Brun, Adithya Murali +1
Code comprehension and code review are already critically important software engineering tasks, and the rising use of AI code generation tools is only increasing that importance. G…
Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models
Zhanna Kaufman, Madeline Endres, Cindy Xiong Bearfield +1
Systems relying on ML have become ubiquitous, but so has biased behavior within them. Research shows that bias significantly affects stakeholders' trust in systems and how they use…
QEDCartographer: Automating Formal Verification Using Reward-Free Reinforcement Learning
Alex Sanchez-Stern, Abhishek Varghese, Zhanna Kaufman +3
Formal verification is a promising method for producing reliable software, but the difficulty of manually writing verification proofs severely limits its utility in practice. Recen…
My Model is Unfair, Do People Even Care? Visual Design Affects Trust and Perceived Bias in Machine Learning
Aimen Gaba, Zhanna Kaufman, Jason Chueng +4
Machine learning technology has become ubiquitous, but, unfortunately, often exhibits bias. As a consequence, disparate stakeholders need to interact with and make informed decisio…
Passport: Improving Automated Formal Verification Using Identifiers
Alex Sanchez-Stern, Emily First, Timothy Zhou +3
Formally verifying system properties is one of the most effective ways of improving system quality, but its high manual effort requirements often render it prohibitively expensive.…