Ensuring Fairness in AI-Driven University Assessments

Abstract

The growing concern over bias in AI systems, particularly in high-impact areas like healthcare, hiring, criminal justice and education emphasizes the need for ethical implementations to ensure fairness. To address bias, indicators such as demographic parity, equalized odds, calibration, and disparate impact measurement are crucial in monitoring and reducing biased outcomes across different demographic groups. In a scenario of university students taking online courses, where the exams were evaluated by an AI system, raising concerns about potential bias in the evaluation process. The key problem identified is ensuring that AI-driven assessments fairly evaluate students from diverse backgrounds, without disproportionately favoring or penalizing certain groups. Bias detection and mitigation efforts are essential to foster trust, fairness, and consistency in AI-based evaluations.

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