https://arxiv.org/abs/2108.02922 close this message Donate to arXiv Please join the Simons Foundation and our generous member organizations in supporting arXiv during our giving campaign September 23-27. 100% of your contribution will fund improvements and new initiatives to benefit arXiv's global scientific community. DONATE [secure site, no need to create account] Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation and member institutions. arXiv.org > cs > arXiv:2108.02922 [ ] Help | Advanced Search [All fields ] Search arXiv Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Machine Learning arXiv:2108.02922 (cs) [Submitted on 6 Aug 2021] Title:Mitigating dataset harms requires stewardship: Lessons from 1000 papers Authors:Kenny Peng, Arunesh Mathur, Arvind Narayanan Download PDF Abstract: Concerns about privacy, bias, and harmful applications have shone a light on the ethics of machine learning datasets, even leading to the retraction of prominent datasets including DukeMTMC, MS-Celeb-1M, TinyImages, and VGGFace2. In response, the machine learning community has called for higher ethical standards, transparency efforts, and technical fixes in the dataset creation process. The premise of our work is that these efforts can be more effective if informed by an understanding of how datasets are used in practice in the research community. We study three influential face and person recognition datasets - DukeMTMC, MS-Celeb-1M, and Labeled Faces in the Wild (LFW) - 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 that can mitigate these harms, making recommendations to dataset creators, conference program committees, dataset users, and the broader research community. Subjects: Machine Learning (cs.LG); Computers and Society (cs.CY) Cite as: arXiv:2108.02922 [cs.LG] (or arXiv:2108.02922v1 [cs.LG] for this version) Submission history From: Arunesh Mathur [view email] [v1] Fri, 6 Aug 2021 02:52:36 UTC (785 KB) Full-text links: Download: * PDF * Other formats [by-nc-sa-4] Current browse context: cs.LG < prev | next > new | recent | 2108 Change to browse by: cs cs.CY References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export bibtex citation Loading... Bibtex formatted citation x [loading... ] Data provided by: Bookmark BibSonomy logo Mendeley logo Reddit logo ScienceWISE logo (*) Bibliographic Tools Bibliographic and Citation Tools [ ] Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) [ ] Litmaps Toggle Litmaps (What is Litmaps?) ( ) Code & Data Code and Data Associated with this Article [ ] arXiv Links to Code Toggle arXiv Links to Code & Data (What is Links to Code & Data?) ( ) Related Papers Recommenders and Search Tools [ ] Connected Papers Toggle Connected Papers (What is Connected Papers?) [ ] Core recommender toggle CORE Recommender (What is CORE?) ( ) About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs and how to get involved. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) * About * Help * Click here to contact arXiv Contact * Click here to subscribe Subscribe * Copyright * Privacy Policy * Web Accessibility Assistance * arXiv Operational Status Get status notifications via email or slack