BLACK BOXES, STUDENT DATA & PLAYING MONEYBALL FOR EDUCATION
We’re rapidly entering a world of evidence-based decision making in public education. These decisions will be powered by vast amounts of data run through proprietary black boxes that parents will have no way of understanding. The approach is called Moneyballand the goal is to justify ration resources to students –while investors make a tidy profit.
One of the most difficult challenges I’ve had as a parent is convincing other parents that the endless collection of our kids’ data isn’t benign and technology isn’t inherently benevolent.
Big Data, Like Big Brother, Isn’t Your Friend
As adults, we’ve chosen to ignore this cold hard fact: that by using electronic devices, we are allowing ourselves to become a product. Von Shoshana Zuboff calls this evolution in big data mediated economics surveillance capitalism:
It’s now clear that this shift in the use of behavioral data was an historic turning point. Behavioral data that were once discarded or ignored were rediscovered as what I call behavioral surplus. Google’s dramatic success in “matching” ads to pages revealed the transformational value of this behavioral surplus as a means of generating revenue and ultimately turning investment into capital. Behavioral surplus was the game-changing zero-cost asset that could be diverted from service improvement toward a genuine market exchange. Key to this formula, however, is the fact that this new market exchange was not an exchange with users but rather with other companies who understood how to make money from bets on users’ future behavior. In this new context, users were no longer an end-in-themselves. Instead they became a means to profits in a new kind of marketplace in which users are neither buyers nor sellers nor products. Users are the source of free raw material that feeds a new kind of manufacturing process.
As adults we’re vaguely aware that certain choices we make will impact our credit report. The inputs seem arbitrary and frankly ridiculous. Unless, there’s a problem, THEN, the unfairness of the system quickly comes into focus.
How your credit report is determined is an example of a black box. Inputs go in, something happens inside the box, and then your credit report comes out. What happens inside the box? Who knows? It’s a proprietary predictive model.
What sorts of random digital bits could impacts your credit report? Things like what operating system you use, if you do your browsing using a desktop or cellphone, even what you decided to use as your email address.
This excerpt is from New Study Shows You Can Predict Credit Rating from Your Online Tech Fingerprint.
And these things evidently matter a lot in predicting default.When combining information from both variables (“Operating system” and “Email host”), default rates are even more dispersed.15 We observe the lowest default rate for Mac-users with a T-online email address. The default rate for this combination is 0.36%, which is lower than the average default rate in the 1st Continue reading:Black Boxes, Student Data & Playing Moneyball for Education – Seattle Education
