Best practices of mitigating educational Artificial Intelligence implicit bias in shaping diversity, inclusion, and equity

Artificial Intelligence (A.I.) 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. Biases in education may limit access to opportunities and further social inequalities, often due to implicit bias in data processing and decision-making. Addressing and recognizing implicit biases in AI is essential to create equal access to higher education and opportunities for students. To combat AI biases, it's necessary to monitor and assess their performance and train them using unbiased data and algorithms to ensure that all students have equal access to higher education and the opportunities it provides them.

Presented at the Abington College Undergraduate Research Activities (ACURA) Poster Fair