{"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/fairput-a-light-framework-for-machine","title":"FairPut: A Light Framework for Machine Learning Fairness with LightGBM","arxiv_id":null,"date":"2020-10-22","proceeding":"The Alan Turing Institute 2020 10","authors":["Derek Snow"],"abstract":"This is a holistic framework to approach fair prediction outputs at the individual and group level. This framework includes quantitative monotonic measures, residual explanations, benchmark competition, adversarial attacks, disparate error analysis, model agnostic pre-and post-processing, reasoning codes, counterfactuals, contrastive explanations, and prototypical examples. A number novel techniques are proposed in this framework, each of which could benefit from future examination.","url_abs":"https://privpapers.ssrn.com/sol3/papers.cfm?abstract_id=3619715","url_pdf":"https://poseidon01.ssrn.com/delivery.php?ID=359103099101004112117106004068114065023050001054093024010084017068124127077090079027031029003043109007047089110088074110065013052052059034038026114080027093118078118046054062026029072078127124119115118089102075083110127001094079088110007006114026024008&EXT=pdf&INDEX=TRUE","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":"fairput-a-light-framework-for-machine","repo_url":"https://github.com/firmai/ml-fairness-framework","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"fairness","task_name":"Fairness"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}