Earlier quoted context omitted.
I'm very skeptical that this is a sufficient replacement. Relevant expertise matters. If all the top economics conferences adopted a code of ethics saying they can't study minimum wages or tax increases, that would be a serious constraint on our ability to understand the issues, even if think tanks and sociology conferences ignored those constraints. Perhaps equally importantly, I'm skeptical that you can limit ethic…
So let's examine our axioms: I'd, in some ways, expect Dr. Hanna to ascribe to the axiom that predictive policing is unethical. As such, applied research into "improving" predictive policing would also sit squarely in the unethical space. And I expect that's all you're going to get at an ML conference (outside of explicitly ethics/critique papers): "Our model predicts crime 4.2% better than competing models" However,…
One issue here is that the pool of domain experts depends on the different incentives faced by different researchers. If the top machine learning conferences all decide to reject papers about predictive policing, then this will reduce the incentive for machine learning researchers (who mostly want to publish in these conferences) to work in the area at all.
Maybe critics of predictive policing think this is good, or think that it's better for the field to restrict itself to the few computer science researchers who are prepared to seriously devote themselves to it? But there don't seem to be very many of them. And, as someone who thinks there is a lot of room for police departments to in general do a better job --- and I think most of the people arguing against predictive policing would believe the same! --- it's disappointing to me that the end result of this argument seems to be disincentivizing technical research on it.