How big data is unfair

As we're on the cusp of using machine learning for rendering basically all kinds of consequential decisions about human beings in domains such as education, employment, advertising, health care and policing, it is important to understand why machine learning is not, by default, fair or just in any meaningful way. This runs counter to the widespread misbelief that algorithmic decisions tend to be fair, because, y'know, math is about equations and not skin color. Examples of this misbelief are common and evident in a recent piece on data-driven crime fighting that appeared in the Financial Times, which Cathy O'Neil brought to my attention. Ironically, Gilian Tett is well known for reporting on the failure of such things as "multi-variable equations" in the wake of the financial crisis, but she is perplexingly quick to accept that multi-variable equations are neutral and therefore fair, because the "computer experts" (whatever that means) at the police station asserted them to be so. My goal is not to belabor this one example.