…les are all over the place, that children suffer, that workers do double shifts without sleep, etc. The problem isn’t the algorithm; it’s how it’s deployed, what maximization goals it uses, and who has the power to adjust the system. If we’re going to deploy these systems, we need to articulate clearly what values we believe are important and then be held accountable for building systems to those standards.
Personally I’m excited by the technical work that is happening in an area known as “fairness, accountability, and transparency in machine learning” (FATML). An example remedy in this space was proposed by a group of computer scientists who were bothered by how hiring algorithms learned the biases of the training data. They renormalized the training data so that protected categories like race and gender couldn’t be discerned through proxies. To do so, they relied heavily on legal frames in the United States that define equal opportunity in employment, making it very clear that when the terms of fairness are clearly defined, they can be computationally protected. This kind of remedy shows the elegant marriage of technology and policy to achieve agreed upon ends.
EM is presented as a more humane, productive and progressive means of social control. Companies such as iSecure Trac, Secure Alert, Pro Tech, GEO and Omnilink which manufacture ankle bracelets talk up the cost savings to their state clients.