Navigating the ethical terrain of AI in higher education: Strategies for mitigating bias and promoting fairness
Abstract
Artificial intelligence (AI) and machine learning (ML) are transforming higher education by enhancing personalized learning and academic support, yet they pose significant ethical challenges, particularly in terms of inherent biases. This review critically examines the integration of AI in higher education, underscoring the dual aspects of its potential to innovate educational paradigms and the essential need to address ethical implications to avoid perpetuating existing inequalities. The researchers employed a methodological approach that analyzed case studies and literature as primary data collection methods, focusing on strategies to mitigate biases through technical solutions, diverse datasets, and strict adherence to ethical guidelines. Their findings indicate that establishing an ethical AI environment in higher education is imperative and involves comprehensive efforts across policy regulation, governance, and education. The study emphasizes the significance of interdisciplinary collaboration in addressing the complexities of AI bias, highlighting how policy, regulation, governance, and education play pivotal roles in creating an ethical AI framework. Ultimately, the paper advocates for continuous vigilance and proactive strategies to ensure that AI contributes positively to educational settings, stressing the need for robust frameworks that integrate ethical considerations throughout the lifecycle of AI systems to ensure their responsible and equitable use.
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