Education Technology’s Machine Learning Problem—and Responsibility

From Formula 1 to Yelp, industries across the board are seeking ways to apply machine learning to their work. Even academics and Goldman Sachs analysts tried using it to predict World Cup winners. (Those predictions proved very, very wrong.)

But how is machine learning playing out in education—and how does it impact not just students, educators and parents, but also the businesses building technology tools to support teaching and learning?

At the SF Edtech Meetup, hosted by EdSurge on July 10, four panelists gathered to discuss the challenges around deploying machine learning in the classroom and the boardroom. The speakers were Carlos Escapa (Senior Principal, AI/ML Business Development, Amazon Web Services), Vivienne Ming (Founder and CEO, Socos Labs), Matthew Ramirez (Director of Product Management, AI Writing Tools, Chegg) and Andrew Sutherland (CTO and co-founder, Quizlet).

The Data Problem

What makes machine learning work is data—but that data can be biased in problematic ways that can lead to misleading and disturbing outcomes. (Ming referenced the time when Google’s image recognition algorithms classified black people as gorillas.)

“When you’re doing this in advertising, who cares if you get some of it wrong,” Ming said. “When you’re doing it in diagnostics or in education or in hiring, you potentially just ruined someone’s life. You have a real moral obligation to understand why your system is making the recommendations it’s making.”

Education and human development is complex, Ming noted, and there’s still not a mass of training data that connects what activities four-year-olds do with their parents in the evenings to what their life outcomes will be. In complex data sets, particularly ones with lots of human data, she warned, the number of spurious correlations that don’t actually mean anything are often more common than evidence of reliable causal relationships.


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