AI and economics
https://spectrum.ieee.org/deep-learning-computational-cost
https://spectrum.ieee.org/rodney-brooks-ai/particle-5
Google tackles the black box problem with Explainable AI
The main question is to do these things called counterfactuals, where the neural network asks itself, for example, 'Suppose I hadn't been able to look at the shirt colour of the person walking into the store, would that have changed my estimate of how quickly they were walking?' By doing many counterfactuals, it gradually builds up a picture of what it is and isn't paying attention to when it's making a prediction.
Artificial intelligence and behavioral economics Colin F. Camerer
The first idea is that AI can be used in the search for new “behavioral”-type variables that affect choice. Two examples are given from experimental data on bargaining and on risky choice.
The second idea is that some common limits on human prediction might be understood as the kinds of errors made by poor implementations of machine learning. That is, people are thinking as if they are executing machine learning algorithms but are doing a mediocre job of it.
The half idea— it’s short-- is that it is important to study how AI technology used in firms and by other institutions can bot overcome and exploit human limits. The fullest understanding of this tech-human interaction will require new knowledge from behavioral economics about attention and perceived fairness.
Neglected Open Questions in the Economics of Artificial Intelligence by Tyler Cowen