3 ms·

Gelman's specific critique of Domingos is as follows: "And here’s the half that I think Domingos gets wrong: He’s too sanguine about existing algorithms being

Gelman's specific critique of Domingos is as follows: "And here’s the half that I think Domingos gets wrong: He’s too sanguine about existing algorithms being unbiased. I don’t know why he’s so confident that existing algorithms for credit-card scoring, parole consultation, shopping and media recommendations, etc., are unbiased and not capable of outside improvement. I respect his concern about political involvement in these processes—but the existing algorithms are human products and are already the result of political processes. Again, his concern that “progressives will blithely assign prejudices even to algorithms that transparently can’t have any,” is missing the point that the structure and inputs of these algorithms are the result of existing human choices."

jl2718 6y ago
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?

jl2718 6y ago

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?