Machine learning conferences often feature invited talks from practitioners of fields outside of but related to machine learning. I'd like to see an invited economist talk about current best guesses regarding how artificial intelligence is going to change the labor market.
The current economic environment is eerily reminiscent of the dystopian novel Player Piano, set in an America beset by massive unemployment and extreme income inequality between the wealthy engineer class and the manual labor class displaced by automation. In reality, GDP has returned to pre-recession levels although unemployment has not, leading some economists to formulate the zero marginal product worker hypothesis. The zero MP hypothesis presupposes that since the Great Recession has started ``there has been no major technological breakthrough in the meantime'', therefore when the workers were employed they had zero MP but no one noticed. However, as NPR points out, technology is eliminating skilled work. They give the example of the legal profession, which is doubly close to me: first because my wife is a lawyer who got laid off, and second because I consulted with an e-discovery firm that was interested in using the LDA capabilities in Vowpal Wabbit to improve their e-discovery efficiency. I would argue that there has been technological change since the beginning of the Great Recession (2007) in machine learning with the proliferation of knowledge coupled with open-source toolkits; in addition some of the technological change from the previous decade of machine learning (dramatic progress!) was presumably not yet applied because the economic good times were delaying the cost pressures. Therefore I suspect that workers have been displaced in the good old-fashioned manner, namely, being formally positive MP but no longer necessary due to technological change.
Overall I'm optimistic that technology and increased productivity will lead to a better standard of living for all. However the recent history of income inequality in America suggests that created wealth is not necessarily shared fairly across the population. Understanding who is likely to be the winners and losers in the labor market of the artificially intelligent future we are creating would be a great thing for the machine learning community.