{"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/algorithmic-assurance-an-active-approach-to","title":"Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation","arxiv_id":null,"date":"2018-12-01","proceeding":"NeurIPS 2018 12","authors":["Shivapratap Gopakumar","Sunil Gupta","Santu Rana","Vu Nguyen","Svetha Venkatesh"],"abstract":"We introduce algorithmic assurance, the problem of testing whether\nmachine learning algorithms are conforming to their intended design\ngoal. We address this problem by proposing an efficient framework\nfor algorithmic testing. To provide assurance, we need to efficiently\ndiscover scenarios where an algorithm decision deviates maximally\nfrom its intended gold standard. We mathematically formulate this\ntask as an optimisation problem of an expensive, black-box function.\nWe use an active learning approach based on Bayesian optimisation\nto solve this optimisation problem. We extend this framework to algorithms\nwith vector-valued outputs by making appropriate modification in Bayesian\noptimisation via the EXP3 algorithm. We theoretically analyse our\nmethods for convergence. Using two real-world applications, we demonstrate\nthe efficiency of our methods. The significance of our problem formulation\nand initial solutions is that it will serve as the foundation in assuring\nhumans about machines making complex decisions.","url_abs":"http://papers.nips.cc/paper/7791-algorithmic-assurance-an-active-approach-to-algorithmic-testing-using-bayesian-optimisation","url_pdf":"http://papers.nips.cc/paper/7791-algorithmic-assurance-an-active-approach-to-algorithmic-testing-using-bayesian-optimisation.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":"algorithmic-assurance-an-active-approach-to","repo_url":"https://github.com/shivapratap/AlgorithmicAssurance_NIPS2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"bayesian-optimisation","task_name":"Bayesian Optimisation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}