{"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/enhancing-the-accuracy-and-fairness-of-human","title":"Enhancing the Accuracy and Fairness of Human Decision Making","arxiv_id":"1805.10318","date":"2018-05-25","proceeding":"NeurIPS 2018 12","authors":["Isabel Valera","Adish Singla","Manuel Gomez Rodriguez"],"abstract":"Societies often rely on human experts to take a wide variety of decisions\naffecting their members, from jail-or-release decisions taken by judges and\nstop-and-frisk decisions taken by police officers to accept-or-reject decisions\ntaken by academics. In this context, each decision is taken by an expert who is\ntypically chosen uniformly at random from a pool of experts. However, these\ndecisions may be imperfect due to limited experience, implicit biases, or\nfaulty probabilistic reasoning. Can we improve the accuracy and fairness of the\noverall decision making process by optimizing the assignment between experts\nand decisions?\n  In this paper, we address the above problem from the perspective of\nsequential decision making and show that, for different fairness notions from\nthe literature, it reduces to a sequence of (constrained) weighted bipartite\nmatchings, which can be solved efficiently using algorithms with approximation\nguarantees. Moreover, these algorithms also benefit from posterior sampling to\nactively trade off exploitation---selecting expert assignments which lead to\naccurate and fair decisions---and exploration---selecting expert assignments to\nlearn about the experts' preferences and biases. We demonstrate the\neffectiveness of our algorithms on both synthetic and real-world data and show\nthat they can significantly improve both the accuracy and fairness of the\ndecisions taken by pools of experts.","url_abs":"http://arxiv.org/abs/1805.10318v1","url_pdf":"http://arxiv.org/pdf/1805.10318v1.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":"enhancing-the-accuracy-and-fairness-of-human","repo_url":"https://github.com/Networks-Learning/FairHumanDecisions","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"fairness","task_name":"Fairness"},{"task_slug":"sequential-decision-making","task_name":"Sequential Decision Making"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.10318","atlas_url":"https://app.syntology.ai/?focus=1805.10318","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}