The explosive use of big data, predictive analytics and other modeling techniques to help understand and drive outcomes in all types of organizations has significantly increased over the past decade. Advocates of artificial intelligence enthusiastically tout the benefits of data to predict and, in some cases, alter key processes and outcomes.
Higher education institutions are no different. They are increasingly turning to predictive analytics to help understand and improve student success. While it is true that there is power in predictive analytics, they are no panacea — especially not within the context of diversity and inclusion. Concepts such as “AI” and “machine learning” are assumed to be neutral by definition, yet all predictive models are shaded by human judgment, which we know falls far short of being error-free.
Author: Audrey Murrell