&gt; Or the exact opposite: that the algorithms simply reflect results caused by bias, a far more likely relationship given what we know of history.<p>Unsupported assertion<p>&gt; But we also know that a snapshot of current society is often little more than a reflection of social forces that has made the society what it is at this particular point in time, and treating them as immutable (or simply placing faith in their persistence) is nothing more than a political policy that helps perpetuate them.<p>This is anti-inductive. Knowledge can only be contingent based on past experience. This is John Stuart Mill stuff, empiricism.<p>&gt; Treating past correlations as future predictors in a dynamic system such as society is programming conservatism into your algorithm.<p>Yes. All anyone has to go one in making predictions is past data. If something changes radically in the environment then the model may break. This does not mean we should just make shit up for some of our sample data&#x2F;training set.<p>&gt; Acting on them is a political policy which strives to maintain the status quo -- there&#x27;s nothing wrong about it per se, if that&#x27;s what you want, but we need to be aware of this fact.<p>A model of the world can be more or less accurate. That is apolitical. It can be biased. This can be a political matter. So reduce or eliminate the bias.<p>&gt; Alternatively, we could program the algorithms to predict or even assist with social change.<p>If you want to enact social change go for it but stop pretending that this has anything to do with statistics, modeling, prediction or math, just go straight to the politics.