40 citations · 69 across the 7 of their papers we have counts for
8 papers
Sex Trouble: Common pitfalls in incorporating sex/gender in medical machine learning and how to avoid them
Kendra Albert, Maggie Delano
False assumptions about sex and gender are deeply embedded in the medical system, including that they are binary, static, and concordant. Machine learning researchers must understa…
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…
"This Whole Thing Smacks of Gender": Algorithmic Exclusion in Bioimpedance-based Body Composition Analysis
Kendra Albert, Maggie Delano
Smart weight scales offer bioimpedance-based body composition analysis as a supplement to pure body weight measurement. Companies such as Withings and Fitbit tout composition analy…
Ethical Testing in the Real World: Evaluating Physical Testing of Adversarial Machine Learning
Kendra Albert, Maggie Delano, Jonathon Penney +2
This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems…
Legal Risks of Adversarial Machine Learning Research
Ram Shankar Siva Kumar, Jonathon Penney, Bruce Schneier +1
Adversarial Machine Learning is booming with ML researchers increasingly targeting commercial ML systems such as those used in Facebook, Tesla, Microsoft, IBM, Google to demonstrat…
Politics of Adversarial Machine Learning
Kendra Albert, Jonathon Penney, Bruce Schneier +1
In addition to their security properties, adversarial machine-learning attacks and defenses have political dimensions. They enable or foreclose certain options for both the subject…