{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predictive-inequity-in-object-detection","title":"Predictive Inequity in Object Detection","arxiv_id":"1902.11097","date":"2019-02-21","proceeding":null,"authors":["Benjamin Wilson","Judy Hoffman","Jamie Morgenstern"],"abstract":"In this work, we investigate whether state-of-the-art object detection\nsystems have equitable predictive performance on pedestrians with different\nskin tones. This work is motivated by many recent examples of ML and vision\nsystems displaying higher error rates for certain demographic groups than\nothers. We annotate an existing large scale dataset which contains pedestrians,\nBDD100K, with Fitzpatrick skin tones in ranges [1-3] or [4-6]. We then provide\nan in-depth comparative analysis of performance between these two skin tone\ngroupings, finding that neither time of day nor occlusion explain this\nbehavior, suggesting this disparity is not merely the result of pedestrians in\nthe 4-6 range appearing in more difficult scenes for detection. We investigate\nto what extent time of day, occlusion, and reweighting the supervised loss\nduring training affect this predictive bias.","url_abs":"http://arxiv.org/abs/1902.11097v1","url_pdf":"http://arxiv.org/pdf/1902.11097v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predictive-inequity-in-object-detection","repo_url":"https://github.com/benjaminrwilson/inequity-release","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1902.11097","atlas_url":"https://app.syntology.ai/?focus=1902.11097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.11097"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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