An Ethical Highlighter for People-Centric Dataset Creation

Abstract

Important ethical concerns arising from computer vision datasets of people have been receiving significant attention, and a number of datasets have been withdrawn as a result. To meet the academic need for people-centric datasets, we propose an analytical framework to guide ethical evaluation of existing datasets and to serve future dataset creators in avoiding missteps. Our work is informed by a review and analysis of prior works and highlights where such ethical challenges arise.

arXiv commentPart of the Navigating the Broader Impacts of AI Research Workshop at NeurIPS 2020

References in corpus (3)

• The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards
• Large image datasets: A pyrrhic win for computer vision?
• Analyzing Who and What Appears in a Decade of US Cable TV News

Cited by in corpus (5)

• Computer Vision and Conflicting Values: Describing People with Automated Alt Text
• Survey on the Analysis and Modeling of Visual Kinship: A Decade in the Making
• Augmented Datasheets for Speech Datasets and Ethical Decision-Making
• SoK: Machine Learning Governance
• Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers