{"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/contexts-matter-an-empirical-study-on","title":"Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems","arxiv_id":"2408.06102","date":"2024-08-12","proceeding":null,"authors":["Chengwen Du","Tao Chen"],"abstract":"Background: Fairness testing for deep learning systems has been becoming increasingly important. However, much work assumes perfect context and conditions from the other parts: well-tuned hyperparameters for accuracy; rectified bias in data, and mitigated bias in the labeling. Yet, these are often difficult to achieve in practice due to their resource-/labour-intensive nature. Aims: In this paper, we aim to understand how varying contexts affect fairness testing outcomes. Method:We conduct an extensive empirical study, which covers $10,800$ cases, to investigate how contexts can change the fairness testing result at the model level against the existing assumptions. We also study why the outcomes were observed from the lens of correlation/fitness landscape analysis. Results: Our results show that different context types and settings generally lead to a significant impact on the testing, which is mainly caused by the shifts of the fitness landscape under varying contexts. Conclusions: Our findings provide key insights for practitioners to evaluate the test generators and hint at future research directions.","url_abs":"https://arxiv.org/abs/2408.06102v1","url_pdf":"https://arxiv.org/pdf/2408.06102v1.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":"contexts-matter-an-empirical-study-on","repo_url":"https://github.com/ideas-labo/fes","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"fairness","task_name":"Fairness"}],"methods":[{"method_slug":"hint","method_name":"HINT"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}