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Nevada is not going to be the last state to integrate big data and predictive analytics into its school funding formula, so we need to think critically about how best to deploy these tools. There is value in efforts to use these resources to better target high-need students, and education funding models can certainly take full advantage of the growing data resources available. However, if school fundingand by extension, the opportunities available to studentsare directly linked to the outputs of a machine learning model, then that model must be designed with transparency, equity, and accountability in mind from the start. The methodology must be in the public domain, so that they can be evaluated for how fairly and well they actually support high-need studentswhich should include students from low-income backgrounds as well as those with other needs and challenges. Nevadas new policy falls short on all these fronts, leaving too many students out of the equation.

See more here:
When Students Get Lost in the Algorithm: The Problems with Nevada's AI School Funding Experiment - New America