{"type":"ScholarlyArticle","title":"Creating Dynamic Checklists via Bayesian Case-Based Reasoning: Towards Decent Working Conditions for All","authors":[{"name":"Eirik Lund Flogard","slug":"eirik-lund-flogard","given":"Eirik","family":"Lund  Flogard","affiliation":"Norwegian Labour Inspection Authority; Norwegian University of Science and Technology"},{"name":"Ole Jakob Mengshoel","slug":"ole-jakob-mengshoel","given":"Ole Jakob","family":"Mengshoel","affiliation":"Norwegian University of Science and Technology"},{"name":"Kerstin Bach","slug":"kerstin-bach","given":"Kerstin","family":"Bach","affiliation":"Norwegian University of Science and Technology"}],"abstract":"Every year there are 1.9 million deaths world-wide attributed to occupational health and safety risk factors. To address poor working conditions and fulfill UN's SDG 8, \"protect labour rights and promote safe working environments for all workers\", governmental agencies conduct labour inspections, using checklists to survey individual organisations for working environment violations. Recent research highlights the benefits of using machine learning for creating checklists. However, the current methods only create static checklists and do not adapt them to new information that surfaces during use. In contrast, we propose a new method called Context-aware Bayesian Case-Based Reasoning (CBCBR) that creates dynamic checklists. These checklists are continuously adapted as the inspections progress, based on how they are answered. Our evaluations show that CBCBR's dynamic checklists outperform static checklists created via the current state-of-the-art methods, increasing the expected number of working environment violations found in the labour inspections.","doi":"10.24963/ijcai.2022/709","url":"https://ijcai.org/proceedings/2022/709","pdf":"https://ijcai.org/proceedings/2022/0709.pdf","markdown":"https://ijcai.org/proceedings/2022/709.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/709","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Research Papers","track":"AI for Good","number":709,"pages":{"first":5108,"last":5114},"keywords":["Machine Learning: Recommender Systems","Knowledge Representation and Reasoning: Case-based Reasoning","Multidisciplinary Topics and Applications: Sustainable Development Goals","Machine Learning: Online Learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987127","length_sec":92,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985270","length_sec":720,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Aamodt and Plaza, 1994] Agnar Aamodt and Enric Plaza. 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