Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Adesola, Adedeji Edward; Ojo, Olayinka Anthony; Aboutorabi, Negin; Iwendi, Celestine; Sharma, Vandana Title A name given to the resource Predicting Job Risk from Artificial Intelligence in London Using Supervised Machine Learning Models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTBIG68706.2025.11323513" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323513</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032917186?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032917186?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Adesola A.E., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Ojo O.A., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Aboutorabi N., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Iwendi C., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India Description An account of the resource This study investigates the risk of job automation in London due to artificial intelligence (AI), applying supervised machine learning techniques to identify occupations most at risk. Leveraging a dataset encompassing job-specific features such as primary tasks, industry domains, and associated AI models, the research develops two predictive models. A Random Forest Classifier is used to categorize jobs as low, medium, or high automation risk, while a Linear Regression model estimates the proportion of each occupation's workload likely to be automated. The Random Forest model achieved a high accuracy rate of 97% in classifying job risk, indicating strong predictive capability. Meanwhile, the regression model explained 85% of the variance in the AI workload ratio, highlighting a significant relationship between job attributes and automation potential. These results suggest that job characteristics are reliable indicators of AI impact, particularly in routine, repetitive, and low-skilled roles that are more easily codified and replicated by algorithms. The findings align with broader economic theories such as creative destruction and technological waves, suggesting that AI not only displaces certain roles but also drives structural transformation within the labor market. By focusing on London, this study provides a localized understanding of how AI is reshaping employment patterns. It underscores the growing urgency for strategic workforce re-skilling and adaptive policy frameworks to mitigate negative outcomes and maximize opportunities presented by AI. Ultimately, this research contributes valuable insights into the interaction between AI technologies and employment, helping policymakers, employers, and educators anticipate change and prepare for a more resilient, inclusive labor market. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Automation Risk; Employment Forecasting; Labor Market; Machine Learning; Random Forest Classification Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157981-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online