1 citations · 2 across the 4 of their papers we have counts for
4 papers
Responsiveness Verification: Will Predictions Change? How Much? How Often?
Harry Cheon, Meredith Stewart, Bogdan Kulynych +2
Machine learning models are often used in applications where their inputs change due to routine interactions, strategic manipulation, or noise. In such settings, models can undermi…
Prediction without Preclusion: Recourse Verification with Reachable Sets
Avni Kothari, Bogdan Kulynych, Tsui-Wei Weng +1
Machine learning models are often used to decide who receives a loan, a job interview, or a public benefit. Models in such settings use features without considering their actionabi…
Exploring Data Pipelines through the Process Lens: a Reference Model forComputer Vision
Agathe Balayn, Bogdan Kulynych, Seda Guerses
Researchers have identified datasets used for training computer vision (CV) models as an important source of hazardous outcomes, and continue to examine popular CV datasets to expo…
Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks
Kendra Albert, Maggie Delano, Bogdan Kulynych +1
Attacks from adversarial machine learning (ML) have the potential to be used "for good": they can be used to run counter to the existing power structures within ML, creating breath…