ItemConference Paper

Predicting Job Risk from Artificial Intelligence in London Using Supervised Machine Learning Models

Title
Predicting Job Risk from Artificial Intelligence in London Using Supervised Machine Learning Models
Creator
Adesola, Adedeji Edward; Ojo, Olayinka Anthony; Aboutorabi, Negin; Iwendi, Celestine; Sharma, Vandana
Description
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.
Source
2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Artificial Intelligence; Automation Risk; Employment Forecasting; Labor Market; Machine Learning; Random Forest Classification
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833157981-4;
Format
online
Language
English
Type
Conference paper

Citation

Adesola, Adedeji Edward; Ojo, Olayinka Anthony; Aboutorabi, Negin; Iwendi, Celestine; Sharma, Vandana, “Predicting Job Risk from Artificial Intelligence in London Using Supervised Machine Learning Models,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26114.

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