10.18710/7U6TZPFlogard, Eirik LundEirik LundFlogardNTNU – Norwegian University of Science and TechnologyLabour Inspection Checklist DatasetDataverseNO2022Computer and Information ScienceSocial SciencesLabour inspectionChecklistsMachine LearningNon complianceLong Tail ClassificationFlogard, Eirik LundEirik LundFlogardNorwegian Labour Inspection AuthorityMengshoel, Ole JakobOle JakobMengshoelNTNU – Norwegian University of Science and TechnologyNorwegian Labour Inspection AuthorityNTNU – Norwegian University of Science and TechnologyNTNU – Norwegian University of Science and Technology2022-09-292023-09-28https://papers.nips.cc/paper_files/paper/2022/hash/93e4d161bdd93d1dc0202b4044159edb-Abstract-Datasets_and_Benchmarks.htmlhttps://openreview.net/forum?id=93cqcWFpTex5654139985223118510text/plainapplication/pdftext/csv1.1CC0 1.0The dataset is intended to use for machine learning to promote United Nations' sustainable development goal 8, target 8: "Protect labour rights and promote safe and secure working environments for all workers, including migrant workers, in particular women migrants, and those in precarious employment". It consists of 63634 instances with past inspections conducted by NLIA between 01/01/2012 and 01/06/2019. Each instance in LICD is described via 575 features and two target variables. Each feature represents either organisational or financial information about the inspected organisation. The first target variable is an identifier for the checklist that were used to inspect the organisation. The variable can be used in a machine learning model to predict the optimal checklist for a specific target organisation. The second target variable denotes whether non-compliance was found at the inspection. The variable can be used to in a machine learning to predict non-compliance to working environment regulations among potential target organisations for inspections. The features, target variables and column names of the dataset have been translated from Norwegian to English.57.7631.7671.384.09