Search Results for author:
Found 9 papers, 4 papers with code
Effectiveness of Anonymization in Double-Blind Review
Double-blind review relies on the authors' ability and willingness to effectively anonymize their submissions.
Digital Libraries General Literature Software Engineering
Fairness Testing: Testing Software for Discrimination
This paper defines software fairness and discrimination and develops a testing-based method for measuring if and how much software discriminates, focusing on causality in discriminatory behavior.
Offline Contextual Bandits with High Probability Fairness Guarantees
We present RobinHood, an offline contextual bandit algorithm designed to satisfy a broad family of fairness constraints.
Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models
To address bias in machine learning, data scientists need tools that help them understand the trade-offs between model quality and fairness in their specific data domains.
Blindspots in Python and Java APIs Result in Vulnerable Code
We find that using APIs with blindspots statistically significantly reduces the developers' ability to correctly reason about the APIs in both languages, but that the effect is more pronounced for Python.
Software Engineering Cryptography and Security
Fairness Guarantees under Demographic Shift
Recent studies have demonstrated that using machine learning for social applications can lead to injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes.
Enforcing Delayed-Impact Fairness Guarantees
Recent research has shown that seemingly fair machine learning models, when used to inform decisions that have an impact on peoples' lives or well-being (e. g., applications involving education, employment, and lending), can inadvertently increase social inequality in the long term.
Baldur: Whole-Proof Generation and Repair with Large Language Models
Recent work has developed methods to automate formal verification using proof assistants, such as Coq and Isabelle/HOL, e. g., by training a model to predict one proof step at a time, and using that model to search through the space of possible proofs.
Robust Image Watermarking using Stable Diffusion
Watermarking images is critical for tracking image provenance and claiming ownership.
