{"type":"ScholarlyArticle","title":"Interpretability and Fairness in Machine Learning: A Formal Methods Approach","authors":[{"name":"Bishwamittra Ghosh","slug":"bishwamittra-ghosh","given":"Bishwamittra","family":"Ghosh","affiliation":"School of Computing , National University of Singapore"}],"abstract":"The last decades have witnessed significant progress in machine learning with a host of applications of algorithmic decision-making in different safety-critical domains, such as medical, law, education, and transportation. In high-stake domains, machine learning predictions have far-reaching consequences on the end-users. With the aim of applying machine learning for societal goods, there have been increasing efforts to regulate machine learning by imposing interpretability, fairness, robustness, etc. in predictions. Towards responsible and trustworthy machine learning, we propose two research themes in our dissertation research: interpretability and fairness of machine learning classifiers. In particular, we design algorithms to learn interpretable rule-based classifiers, formally verify fairness, and explain the sources of unfairness. Prior approaches to these problems are often limited by scalability, accuracy, or both. To overcome these limitations, we closely integrate automated reasoning, formal methods, and statistics with fairness and interpretability to develop scalable and accurate solutions.","doi":"10.24963/ijcai.2023/816","url":"https://ijcai.org/proceedings/2023/816","pdf":"https://ijcai.org/proceedings/2023/0816.pdf","markdown":"https://ijcai.org/proceedings/2023/816.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/816","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":816,"pages":{"first":7083,"last":7084},"keywords":["AI Ethics, Trust, Fairness: ETF: Fairness and diversity","General: General","AI Ethics, Trust, Fairness: ETF: Explainability and interpretability"],"videos":[],"references":[{"position":1,"raw":"[Barocas et al., 2017] Solon Barocas, Moritz Hardt, and Arvind Narayanan. Fairness in machine learning. NIPS Tutorial, 1, 2017.","title":"Fairness in machine learning","authors":"Solon Barocas, Moritz Hardt, Arvind Narayanan","year":"2017","venue":"NIPS Tutorial","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ghosh and Meel, 2019] Bishwamittra Ghosh and Kuldeep S. Meel. IMLI: An incremental framework for MaxSAT-based learning of interpretable classification rules. In AIES, 2019.","title":"IMLI: An incremental framework for MaxSAT-based learning of interpretable classification rules","authors":"Meel Ghosh, Bishwamittra Ghosh, Kuldeep S Meel","year":"2019","venue":"AIES","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ghosh et al., 2020] Bishwamittra Ghosh, Dmitry Malioutov, and Kuldeep S. Meel. Classification rules in relaxed logical form. In ECAI, 2020.","title":"Classification rules in relaxed logical form","authors":"Ghosh","year":"2020","venue":"ECAI","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Ghosh et al., 2021] Bishwamittra Ghosh, Debabrota Basu, and Kuldeep S. Meel. Justicia: A stochastic SAT approach to formally verify fairness. In AAAI, 2021.","title":"Justicia: A stochastic SAT approach to formally verify fairness","authors":"Ghosh","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Ghosh et al., 2022a] Bishwamittra Ghosh, Debabrota Basu, and Kuldeep S. Meel. Algorithmic fairness verification with graphical models. In AAAI, 2022.","title":"Algorithmic fairness verification with graphical models","authors":"Ghosh","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Ghosh et al., 2022b] Bishwamittra Ghosh, Dmitry Malioutov, and Kuldeep S. Meel. Efficient learning of interpretable classification rules. In JAIR, 2022.","title":"Efficient learning of interpretable classification rules","authors":"Ghosh","year":"2022","venue":"JAIR","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Ghosh et al., 2023] Bishwamittra Ghosh, Debabrota Basu, and Kuldeep S. Meel. \"How biased are your features?\": Computing fairness influence functions with global sensi- tivity analysis. In FAccT, 2023.","title":"How biased are your features?\": Computing fairness influence functions with global sensitivity analysis","authors":"Ghosh","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Rudin et al., 2022] Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, and Chudi Zhong. Interpretable machine learning: Fundamental prin- ciples and 10 grand challenges. Statistic Surveys, 16:1-85, 2022.","title":"Interpretable machine learning: Fundamental principles and 10 grand challenges","authors":"Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, Chudi Zhong","year":"2022","venue":"Statistic Surveys","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}