{"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/why-did-you-do-that-explaining-black-box","title":"\"Why did you do that?\": Explaining black box models with Inductive Synthesis","arxiv_id":"1904.09273","date":"2019-04-17","proceeding":null,"authors":["Görkem Paçacı","David Johnson","Steve McKeever","Andreas Hamfelt"],"abstract":"By their nature, the composition of black box models is opaque. This makes\nthe ability to generate explanations for the response to stimuli challenging.\nThe importance of explaining black box models has become increasingly important\ngiven the prevalence of AI and ML systems and the need to build legal and\nregulatory frameworks around them. Such explanations can also increase trust in\nthese uncertain systems. In our paper we present RICE, a method for generating\nexplanations of the behaviour of black box models by (1) probing a model to\nextract model output examples using sensitivity analysis; (2) applying\nCNPInduce, a method for inductive logic program synthesis, to generate logic\nprograms based on critical input-output pairs; and (3) interpreting the target\nprogram as a human-readable explanation. We demonstrate the application of our\nmethod by generating explanations of an artificial neural network trained to\nfollow simple traffic rules in a hypothetical self-driving car simulation. We\nconclude with a discussion on the scalability and usability of our approach and\nits potential applications to explanation-critical scenarios.","url_abs":"http://arxiv.org/abs/1904.09273v1","url_pdf":"http://arxiv.org/pdf/1904.09273v1.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":"why-did-you-do-that-explaining-black-box","repo_url":"https://github.com/UppsalaIM/rice","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"program-synthesis","task_name":"Program Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}