{"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/interpretable-active-learning","title":"Interpretable Active Learning","arxiv_id":"1708.00049","date":"2017-07-31","proceeding":null,"authors":["Richard L. Phillips","Kyu Hyun Chang","Sorelle A. Friedler"],"abstract":"Active learning has long been a topic of study in machine learning. However,\nas increasingly complex and opaque models have become standard practice, the\nprocess of active learning, too, has become more opaque. There has been little\ninvestigation into interpreting what specific trends and patterns an active\nlearning strategy may be exploring. This work expands on the Local\nInterpretable Model-agnostic Explanations framework (LIME) to provide\nexplanations for active learning recommendations. We demonstrate how LIME can\nbe used to generate locally faithful explanations for an active learning\nstrategy, and how these explanations can be used to understand how different\nmodels and datasets explore a problem space over time. In order to quantify the\nper-subgroup differences in how an active learning strategy queries spatial\nregions, we introduce a notion of uncertainty bias (based on disparate impact)\nto measure the discrepancy in the confidence for a model's predictions between\none subgroup and another. Using the uncertainty bias measure, we show that our\nquery explanations accurately reflect the subgroup focus of the active learning\nqueries, allowing for an interpretable explanation of what is being learned as\npoints with similar sources of uncertainty have their uncertainty bias\nresolved. We demonstrate that this technique can be applied to track\nuncertainty bias over user-defined clusters or automatically generated clusters\nbased on the source of uncertainty.","url_abs":"http://arxiv.org/abs/1708.00049v2","url_pdf":"http://arxiv.org/pdf/1708.00049v2.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":"interpretable-active-learning","repo_url":"https://github.com/rlphilli/InterpretableActiveLearning","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"}],"methods":[{"method_slug":"lime","method_name":"LIME"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.00049","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}