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Unbiasing Higher Education (And Beyond)

by Sirley Carballo · Nov 18, 2021

It's clear that artificial intelligence (AI) systems are all around us. From the Alexa on your desk to Siri in your pocket or even your institution's CRM, more and more organizations are leveraging the use of AI to simplify and maximize the way we engage with the world. But because AI is only as smart as the data it's fed by humans, it's up to us to combat the pitfalls of our natural biases when we create these AI systems.

In a new Forbes feature, Element451's focus on bias was elevated as a way to rethink disability and bias in AI. Systems like Amazon's Alexa are created to recognize and imitate common speech patterns, yet this constitutes a bias since not everyone speaks the same way, especially those with speech impediments and disabilities.

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Through the use of AI technology, we are accelerating or taking a steps that could take 10, 20, 30 different steps and reduce that. So in essence, technology becomes an enabler and it becomes a kind of a facilitator.”
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Similar issues arise when we look at the ways university admissions have traditionally segmented and grouped students together by traits such as race or gender. By eliminating this type of categorization in the admissions CRM, schools are better able to focus on the intent of the student, not on preconceived assumptions created from years of historic modeling admissions and enrollment.

Although the use of AI technology in higher education has primarily focused on maximizing admissions processes and increasing hyper-personalization, it seems reasonable that the next focus would be on disability and accessibility.

No matter the reason behind developing new AI systems, the responsibility of unbiasing AI lies with us as we create technologies that incorporate diverse data to serve diverse people.

To read the full article, click here.


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