Big Data And The Problem Of Bias In Higher Education

31May - by aiuniverse - 3 - In Uncategorized

Source:-forbes.com

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.

Decades of research on implicit bias show the limitations of human decision making across a number of settings. A recent report from the Ohio State University’s Kirwan Institute for the Study of Race and Ethnicity specifically cautions against the use of predictive analytics. The report asserts that there are potential cognitive and systemic racial biases that impact both the design of data models and the interpretation of their findings.

Thus, it’s fair to ask: Given the human element, can big data ever be bias-free in the context of diversity?

Probably not. But even with these risks, the need for predictive analytics and data-based models within higher education is clear. Many institutions are using big data models in an attempt to improve student outcomes in retention, graduation, engagement and career placement. The use of data in higher education is becoming a competitive advantage for institutions to meet annual enrollment, retention and revenue goals. Student data is gathered to craft models in order to predict their choices, actions and outcomes. In addition, decisions about student support services, programming and resources are being made based on this data.

While some cheer this development as much-needed progress that helps higher education to become more data-driven or evidence-based, others are raising a red caution flag. They point to a range of issues such as the accuracy, security and privacy of the data and the potential for a diversity bias against minoritized and underrepresented student groups.

The increased focus on student success is important given the rising cost of college and student debt. This has led some colleges and universities to use data analytics to predict the types of students who are more likely to need support from academic advising in the form of early intervention. Other institutions are using predictive models to offer adaptive learning tools that faculty can use to help identify and assist students who may need additional support in the classroom. Admissions managers are increasingly relying on predictive analytics to improve enrollment plans, target marketing efforts by student segments and provide customized scholarships and financial-need awards.

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