TITLE: Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers 

ABSTRACT: Machine learning datasets have elicited concerns about privacy, bias, and
unethical applications, leading to the retraction of prominent datasets such as
DukeMTMC, MS-Celeb-1M, and Tiny Images. In response, the machine learning
community has called for higher ethical standards in dataset creation. To help
inform these efforts, we studied three influential but ethically problematic
face and person recognition datasets -- Labeled Faces in the Wild (LFW),
MS-Celeb-1M, and DukeMTM -- by analyzing nearly 1000 papers that cite them. We
found that the creation of derivative datasets and models, broader
technological and social change, the lack of clarity of licenses, and dataset
management practices can introduce a wide range of ethical concerns. We
conclude by suggesting a distributed approach to harm mitigation that considers
the entire life cycle of a dataset.
Machine learning datasets have elicited concerns about privacy, bias, and
unethical applications, leading to the retraction of prominent datasets such as
DukeMTMC, MS-Celeb-1M, and Tiny Images. In response, the machine learning
community has called for higher ethical standards in dataset creation. To help
inform these efforts, we studied three influential but ethically problematic
face and person recognition datasets -- Labeled Faces in the Wild (LFW),
MS-Celeb-1M, and DukeMTM -- by analyzing nearly 1000 papers that cite them. We
found that the creation of derivative datasets and models, broader
technological and social change, the lack of clarity of licenses, and dataset
management practices can introduce a wide range of ethical concerns. We
conclude by suggesting a distributed approach to harm mitigation that considers
the entire life cycle of a dataset.
Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers
http://arxiv.org/abs/2108.02922v2