Giving Compass' Take:

• The author relays the discussion of the panel at the SF Edtech Meetup earlier this week. The panelists talked about how machine learning runs the risk of having data bias which can really harm a student's education. 

• The panelists urged edtech leaders to be responsible with AI use in education and advised them to incorporate educators and teachers in the planning process to strengthen education technology. Will this strategy be effective for reducing data bias? 

• Read about startup education technology incubators that are located inside schools so that teachers can help with implementing the tools. 

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).

What makes machine learning work is data—but that data can be biased in problematic ways that can lead to misleading and disturbing outcomes.

Data must be representative of the population it’s trying to serve, and there must be sufficient data from that population. “It’s very easy to make a mistake creating a model where you’re trying to generalize a solution to a problem, and you actually have insufficient data from some of the demographics that you’re trying to address,” he said.

So what can education companies do to reduce biases in their tools? Ramirez advised that a company wanting to build out its machine learning system should figure out a “ground source of truth” that guides how a company is building its tools.

So what can education companies do to reduce biases in their tools?

Ramirez advised that a company wanting to build out its machine learning system should figure out a “ground source of truth” that guides how a company is building its tools. There’s human effort involved as well. Ming recommended that companies hire domain experts who can build the machine learning system.

Escapa thinks educators should ask AI companies what data they use to generate their models, how they curate that data, how often they update those models and how they know their data doesn’t have bias.

Read the full article about machine learning in education by Tina Nazerian at EdSurge