Biases in ML Part 3A: Risks and Mitigation - Understanding Statistical Fairness in Higher Education AI

As AI continues to revolutionize educational methodologies and administrative operations, it brings the challenge of ensuring that these technologies are free from biases and are equitable in their function. This guide introduces the concepts of statistical fairness in the context of higher education AI, examining how biases can manifest the algorithmic outputs and impact decision-making processes.

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