Risks of AI Bias and Inequities in Learning

Abstract

As artificial intelligence (AI) systems become increasingly embedded within the education sector, there is growing concern about the issue of AI bias and its far-reaching implications for fairness, equity, and student outcomes. AI applications such as adaptive learning platforms, automated grading systems, student performance prediction models, and admissions algorithms are designed to enhance efficiency, personalize instruction, and support data-driven decision-making. However, these systems are only as unbiased as the data they are trained on and the assumptions embedded in their algorithms. If not carefully designed and audited, AI technologies can perpetuate, or even exacerbate, existing inequalities related to race, gender, socioeconomic status, language proficiency, and ability. This makes the conversation around AI bias in education not just a technical issue but a deeply ethical and social one. Bias can also be introduced during the design and implementation stages of AI development.

Ähnliche Arbeiten