Bias and incentives are part of the lenses through which we look at problems and data. The following articles in the series focus on lenses, specifically on frameworks aka mental models. I think most biases boil down to the use of a single/limited framework to look at a problem, or the misapplication of a framework to a dataset. This includes both cognitive biases, data biases (the way we choose to collect data is itself dictated by a framework) and even psychological lenses that color our reserach experience (eg seeing neutral observed behavior x as an instance of something we look at as probelmatic, offensive, etc.)

Haven't fully thought through the role of incentives, it could be something specular