{"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/the-algorithmic-automation-problem-prediction","title":"The Algorithmic Automation Problem: Prediction, Triage, and Human Effort","arxiv_id":"1903.12220","date":"2019-03-28","proceeding":null,"authors":["Maithra Raghu","Katy Blumer","Greg Corrado","Jon Kleinberg","Ziad Obermeyer","Sendhil Mullainathan"],"abstract":"In a wide array of areas, algorithms are matching and surpassing the\nperformance of human experts, leading to consideration of the roles of human\njudgment and algorithmic prediction in these domains. The discussion around\nthese developments, however, has implicitly equated the specific task of\nprediction with the general task of automation. We argue here that automation\nis broader than just a comparison of human versus algorithmic performance on a\ntask; it also involves the decision of which instances of the task to give to\nthe algorithm in the first place. We develop a general framework that poses\nthis latter decision as an optimization problem, and we show how basic\nheuristics for this optimization problem can lead to performance gains even on\nheavily-studied applications of AI in medicine. Our framework also serves to\nhighlight how effective automation depends crucially on estimating both\nalgorithmic and human error on an instance-by-instance basis, and our results\nshow how improvements in these error estimation problems can yield significant\ngains for automation as well.","url_abs":"http://arxiv.org/abs/1903.12220v1","url_pdf":"http://arxiv.org/pdf/1903.12220v1.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":"the-algorithmic-automation-problem-prediction","repo_url":"https://github.com/ptrckhmmr/learning-to-defer-with-limited-expert-predictions","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.12220","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}