As a black man, I'm concerned about the tendency for algorithms to exhibit what looks like racial bias. As a statistician, I'm naturally drawn to investigate why this happens But what is "bias"? Surprisingly, the answer depends on what you think it means to be "rational". 1/7 
We can think of bias as a type of irrational behavior. So broadly speaking, there are two ways one could define bias in algorithms and this arises from the two major definitions of rationality. These are epistemic rationality and instrumental rationality. 2/7
Epistemic rationality is defined as the part of rationality which involves achieving accurate beliefs about the world. Instrumental rationality is the art of choosing and implementing actions that steer the future toward outcomes that you want. 3/7
People who take an epistemic rationality approach tend to focus on what algorithms "think" about the world. They care about whether an algorithm uses race in its decision making and how. They might define lack of bias as colorblindness. 4/7
People who take an instrumental rationality approach tend to focus on the outcomes of an algorithm. They would ask, "Does the behavior of the algorithm impact races differently?" They would define bias as the tendency for an algorithm to create unfair social conditions. 5/7
As a statistician, I study how to reason logically using numbers. So, I'm very drawn to thinking about bias in terms of epistemic rationality. People who take a broader sociological view will tend to think of bias in terms of instrumental rationality and social impact. 6/7
I don't think either approach is wrong. Roughly speaking, the epistemic rationality approach is like science and the instrumental rationality approach is like engineering. When solving real-world problems, both approaches can be a valuable source of important insights. 7/7
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(Shout out to my mutual (omg!) @juliagalef for clueing me in to the epistemic rationality vs instrumental rationality distinction via her podcast)
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