{"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/right-for-the-wrong-scientific-reasons","title":"Making deep neural networks right for the right scientific reasons by interacting with their explanations","arxiv_id":"2001.05371","date":"2020-01-15","proceeding":null,"authors":["Patrick Schramowski","Wolfgang Stammer","Stefano Teso","Anna Brugger","Xiaoting Shao","Hans-Georg Luigs","Anne-Katrin Mahlein","Kristian Kersting"],"abstract":"Deep neural networks have shown excellent performances in many real-world applications. Unfortunately, they may show \"Clever Hans\"-like behavior -- making use of confounding factors within datasets -- to achieve high performance. In this work, we introduce the novel learning setting of \"explanatory interactive learning\" (XIL) and illustrate its benefits on a plant phenotyping research task. XIL adds the scientist into the training loop such that she interactively revises the original model via providing feedback on its explanations. Our experimental results demonstrate that XIL can help avoiding Clever Hans moments in machine learning and encourages (or discourages, if appropriate) trust into the underlying model.","url_abs":"https://arxiv.org/abs/2001.05371v4","url_pdf":"https://arxiv.org/pdf/2001.05371v4.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":"right-for-the-wrong-scientific-reasons","repo_url":"https://github.com/ml-research/xil","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"plant-phenotyping","task_name":"Plant Phenotyping"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2001.05371","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.05371"}},"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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