ABSTRACT
According to a 2018 Pew Research Center survey, 40% of people believe that algorithmic decision-making can be objective, free from the biases that plague human decision-making. The algorithms are just math, the data on which they operate are just facts; at no point in explaining their operation do we need to make reference to human values whatsoever. This chapter traces the historical progression of a pursuit of objectivity in scientific inquiry, and explore how it applies to the domain of machine learning. Computer scientists are likely familiar with many of the points made already, though perhaps not under the guise of Kuhnian theory. Model builders undoubtably recognize that there are many different, yet acceptable ways to build predictive models. Algorithmic design decisions about how to manage error therefore inherently involve values.
