There’s no argument here. Domingos stated that there are algorithms that cannot have bias, and Gelman is claiming that there are algorithms that can have bias.

There’s no argument here. Domingos stated that there are algorithms that cannot have bias, and Gelman is claiming that there are algorithms that can have bias. These are both objectively true, but Gelman’s twists it into an ad-hominem. This reads like mendacious virtue signaling. Ironic too that he would be the one to levy accusations of unprofessorial behavior. It would be very hard to convince me that this was not motivated by cheap point-scoring for political reasons, perhaps as defense in anticipation of an attack upon himself. Domingos’s research is specific to naive algorithms learning from data in experimental realms. Gelman’s work often relates to expert-designed simulations which are actively used in economic policy, banking, insurance, and legal systems. From this perspective, it’s somewhat clear that Domingos’s approach has far less capacity for bias, and thus invites criticism of the older method. Learning from data versus designed simulation. This reads somewhat like a reply suggesting we should trust hand-engineered bias from the right hands rather than seek to eliminate it. But in this case, the hand’s argument is an ad-hominem, so we see that it’s a religious argument about social values rather than a theoretical argument about statistical bias. Maybe we should have that conversation. Yes, we can use algorithms that have no statistical bias. Yes, we can insert any bias we want into an algorithm. Now that that’s established, what should we do?