While this is a nice summary of classifier trade-offs, I think you are entirely too dismissive of the role of history in the dataset, and if I didn’t know any better, I would walk away with the idea that fairness comes down to just choosing an optimal trade-off for a classifier. If you had read any of the technical response, you would have noticed that when controlling for “recidivism, criminal history, age and gender across races, black defendants were 45 percent more likely to get a higher score”. Controls are important because they let you get at the underlying causal model, which is more important for predicting a person’s recidivism than what statistical correlations will tell you. Choosing the right causal model is not an easy problem, but it is at the heart of what we mean when we conventionally talk about fairness.
While this is a nice summary of classifier trade-offs, I think you are entirely too dismissive of the role of history in the dataset, and if I didn’t know any better, I would walk away with the idea that fairness comes down to just choosing an optimal trade-off for a classifier. If you had read any of the technical response, you would have noticed that when controlling for “recidivism, criminal history, age and gender across races, black defendants were 45 percent more likely to get a higher score”. Controls are important because they let you get at the underlying causal model, which is more important for predicting a person’s recidivism than what statistical correlations will tell you. Choosing the right causal model is not an easy problem, but it is at the heart of what we mean when we conventionally talk about fairness.