Search Results for author:
Found 8 papers, 1 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
Legal Risks of Adversarial Machine Learning Research
Adversarial Machine Learning is booming with ML researchers increasingly targeting commercial ML systems such as those used in Facebook, Tesla, Microsoft, IBM, Google to demonstrate vulnerabilities.
Politics of Adversarial Machine Learning
In this paper, we draw on insights from science and technology studies, anthropology, and human rights literature, to inform how defenses against adversarial attacks can be used to suppress dissent and limit attempts to investigate machine learning systems.
Failure Modes in Machine Learning Systems
In the last two years, more than 200 papers have been written on how machine learning (ML) systems can fail because of adversarial attacks on the algorithms and data; this number balloons if we were to incorporate papers covering non-adversarial failure modes.
Law and Adversarial Machine Learning
When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond?
