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Big data has been a buzz word for a few years and the trend of its expansion into other fields is by no means slowing down. As always, where there is a “gold rush” of a new concept lawyers do not fall very far behind, even if it is about numbers—something lawyers do not particularly claim a forte in. 

In November 2018, more than 20,000 Google employees staged a collaborative walkout after the New York Times reported that the company protected three executives accused of sexual harassment[1].  While the focus of this blog has little do with labor rights or sexual harassment, mobilization of this magnitude does highlight another problem in the technology industry—its history of exclusion.  The industry’s uncontended ‘bro culture’ is not only imploding in the workplace, it is directly linked to the ethical problems concerning algorithmic discrimination.

As I started graduate school, I received the opportunity to become part of a research group working on the prevention of mother-to-child transmission of HIV. Quickly I discovered that being part of a research group requires a lot of independent research. But that independent research is not quite so independent. Instead my main collaborator when it came to code review and continuation of work, was myself. However, it was not myself at the time the analysis was conducted, it was myself two weeks later trying to build on my previous work.

Data, Prediction and Law is an innovative course  that has been offered by the Berkeley Legal Studies undergraduate program for the past two years. This is one example of the Data Science Education Program’s “data-enabled courses” that build on concepts introduced in Data 8: Foundations of Data Science and other core courses.