Journal of Intelligent Communication

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Fairness, Bias, and Ethics in AI: Exploring the Factors Affecting Student Performance

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https://doi.org/10.54963/jic.v4i1.306
Omughelli , D., Gordon, N., & Al Jaber, T. (2024). Fairness, Bias, and Ethics in AI: Exploring the Factors Affecting Student Performance. Journal of Intelligent Communication, 3(2), 100–110. https://doi.org/10.54963/jic.v4i1.306
Volume 3 Issue 2 (2024): In Progress
Copyright (c) 2024 Doris Omughelli, Neil Gordon, Tareq Al Jaber
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Authors

The use of artificial intelligence (AI) as a data science tool for education has enormous potential for increasing student performance and course outcomes. However, the growing concern about fairness, bias, and ethics in AI systems requires a careful examination of these issues in an educational context. Using AI and predictive modelling tools, this paper explores the aspects influencing student performance and course success. The Open University Learning Analytics Dataset (OULAD) is analysed using several AI techniques (logistic regression and random forest) in this study to reveal insights about fairness, ethics, and potential biases. This dataset has been used by hundreds of studies to explore how educational data mining can provide information on students. However, potential bias or unfairness in that dataset could undermine the results and any conclusions made from them. To gain insights into the dataset's properties, this was analysed using a typical data science methodology, which included data collecting, cleaning, and exploratory data analysis using Python. By applying AI-based predictive models, this study aims to detect potential biases and their impact on student outcomes. Fairness and ethical considerations are central to the analysis as the representation of various demographic groups and any disparities are evaluated in course results. The goal is to provide useful insights on the proper use of AI in education, while also maintaining equitable and transparent decision-making procedures. The findings shed light on the complicated interplay between artificial intelligence, fairness, and ethics in the context of student performance and course success. As artificial intelligence continues to influence the educational landscape, this study will provide useful ideas for encouraging fairness and minimising biases, resulting in a more inclusive and equal learning environment.

Keywords:

artificial intelligence education fairness bias ethics predictive modelling

Author Biographies

Doris was an MSc postgraduate student at the University of Hull, within the centre of excellence in Data Science, Artificial Intellgence and Modelling (DAIM). Her dissertation topic built on prior work of Neil and Tareq, looking at fairness and bias, as well as wider interest in learning analytics.

Neil is a professor in Computer Science.at the University of Hull in England. Neil is also a National Teaching Fellow, and a Principal Fellow of AdvanceHE. Neil has produced several sector level reports for AdvanceHE, with significant ones on the way that technology enhanced learning can enable flexible pedagogy, on the role of assessment in education, and on computing education. Neil is chair of the British Computer Societies Ethics specialist group and is recognised for his work on ethics and AI. His research interests include applications of computer science to enable true technology enhanced learning, issues around sustainable development, as well as more discipline specific work on applications of computer algebra and formal methods. He has published over 150 research publications.
 
Tareq is a lecturer in Computer Science. His Ph.D. research was on designing and ranking mobile health apps. Since then, Tareq has been teaching and researching in Artificial Intelligence, teaching a variety of topics around machine learning in the centre of excellence in Data Science, Artificial Intelligence and Modelling (DAIM).
 

Highlights

  • Provides some insights into the challenges of fair use of learning analytics
  • Shows some of the effective AI techniques for analysing educational data
  • Reveals the challenges of fair and unbiassed data in machine learning
  • Gives recommendations to researchers and institutions on analysing educational data and in developing inclusive education

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