Towards best practices for mitigating artificial intelligence implicit bias in shaping diversity, inclusion and equity in higher education

Artificial Intelligence (AI) strives to create intelligent machines with human-like abilities. However, like humans, AI can be prone to implicit biases due to flaws in data or algorithms. These biases may cause discriminatory outcomes and decrease trust in AI. Bias in higher education admission may limit access to opportunities and further social inequalities, often due to implicit biases in data processing and decision-making. Addressing and recognizing implicit biases in AI is essential to create equal access to higher education admission and opportunities for students. To combat AI implicit biases, it is necessary to monitor and assess their performance and train them using unbiased data and algorithms. This ensures that all students have equal access to higher education and the opportunities it provides them. While the recent studies reviewed the algorithmic approaches to reducing bias, this article focuses instead on exploring the current understanding of the impacts of AI implicit bias in higher education and its implications for admissions. Furthermore, it evaluates the interactions between AI technology and education, specifically in mitigating AI implicit bias algorithms that can be leveraged to achieve inclusive and equitable quality education and promote lifelong learning opportunities for all.