Search Results for author:
Found 4 papers, 0 papers with code
Sex Trouble: Common pitfalls in incorporating sex/gender in medical machine learning and how to avoid them
False assumptions about sex and gender are deeply embedded in the medical system, including that they are binary, static, and concordant.
Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks
In this paper, we review the broader impact statements that adversarial ML researchers wrote as part of their NeurIPS 2020 papers and assess the assumptions that authors have about the goals of their work.
"This Whole Thing Smacks of Gender": Algorithmic Exclusion in Bioimpedance-based Body Composition Analysis
Smart weight scales offer bioimpedance-based body composition analysis as a supplement to pure body weight measurement.
Computers and Society
Ethical Testing in the Real World: Evaluating Physical Testing of Adversarial Machine Learning
This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems involving human subjects.
Computers and Society
