{"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/global-explanations-of-neural-networks","title":"Global Explanations of Neural Networks: Mapping the Landscape of Predictions","arxiv_id":"1902.02384","date":"2019-02-06","proceeding":null,"authors":["Mark Ibrahim","Melissa Louie","Ceena Modarres","John Paisley"],"abstract":"A barrier to the wider adoption of neural networks is their lack of\ninterpretability. While local explanation methods exist for one prediction,\nmost global attributions still reduce neural network decisions to a single set\nof features. In response, we present an approach for generating global\nattributions called GAM, which explains the landscape of neural network\npredictions across subpopulations. GAM augments global explanations with the\nproportion of samples that each attribution best explains and specifies which\nsamples are described by each attribution. Global explanations also have\ntunable granularity to detect more or fewer subpopulations. We demonstrate that\nGAM's global explanations 1) yield the known feature importances of simulated\ndata, 2) match feature weights of interpretable statistical models on real\ndata, and 3) are intuitive to practitioners through user studies. With more\ntransparent predictions, GAM can help ensure neural network decisions are\ngenerated for the right reasons.","url_abs":"http://arxiv.org/abs/1902.02384v1","url_pdf":"http://arxiv.org/pdf/1902.02384v1.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":"global-explanations-of-neural-networks","repo_url":"https://github.com/capitalone/global-attribution-mapping","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[{"method_slug":"gam","method_name":"GAM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1902.02384","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.02384"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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