In my last post, I lamented a bit the potential for advanced technologies to reinforce existing biases at a quicker and larger scale, without either our awareness nor consent.

The issue of algorithmically detecting gender that I briefly mentioned is one that I’ve been struggling with this semester. Inferring gender can be crucial in revealing lags at publications and harmful disparities in behavior where diversity data is not provided, yet the act of classification can be morally and socially harmful.

Nathan Matias is a Ph.D student at the neighboring Civic Media Lab who has been a great ally in helping soften the steep learning curve of grad school and also in tackling gender/identity related projects. His master’s thesis work in Open Gender Tracker has led to many great posts on the Guardian’s Datablog.

Like a truly great friend, he not only answered my questions with regard to ethically detecting gender, he wrote a comprehensive blogpost about it. He highlights the practical realities of keeping work both computationally effective and ethically responsible. A must-read for anyone considering work in this area. Thank you Nathan!

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