Machine Learning Heresy and the Church of Optimality:
As Machine Learning continues to grow in both usage and impact on people’s lives, there has been a growing concern around the ethics of using these systems. In application areas such as hiring selection, loan review, and even prison sentencing, ML is being used in ways that raise questions about the fairness of these algorithms. But what does it mean for an algorithm to be fair? An algorithm will consistently make the same decision when given the same data, leading some people to argue that building an optimal algorithm is inherently fair. Even in the case of using sensitive features like age, race and gender, if the data is predictive, aren’t we just modeling reality?
In this talk, I will argue that these questions do not let us off the hook in regards to the impact of the systems we build as Machine Learning engineers. I think it is important to question the nature of how ‘optimal’ a model can even be in the first place. Finally, I will discuss what kinds of organizational resistance engineers might run into, and how to deal with questionable ethical decisions for the sake of being ‘optimal’.