Artificial Intelligence and Its Implications for Income Distribution and Unemployment

By Anton Korinek, Joseph E. Stiglitz. 2017

330 citations recorded by the source corpus.

Inequality is one of the main challenges posed by the proliferation of artificial intelligence (AI) and other forms of worker-replacing technological progress. This paper provides a taxonomy of the associated economic issues: First, we discuss the general conditions under which new technologies such as AI may lead to a Pareto improvement. Secondly, we delineate the two main channels through which inequality is affected – the surplus arising to innovators and redistributions arising from factor price changes. Third, we provide several simple economic models to describe how policy can counter these effects, even in the case of a “singularity” where machines come to dominate human labor. Under plausible conditions, non-distortionary taxation can be levied to compensate those who otherwise might lose. Fourth, we describe the two main channels through which technological progress may lead to technological unemployment – via efficiency wage effects and as a transitional phenomenon. Lastly, we speculate on how technologies to create super-human levels of intelligence may affect inequality and on how to save humanity from the Malthusian destiny that may ensue.

Subfields: Economics and Econometrics, AI Ethics, Fairness & Society, Economics & Operations Research

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Citations in the corpus

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (2023, background): “Their far-reaching consequences, which unfold over decades, are difficult to anticipate, particularly in relation to labor demand (Bessen, 2018; Korinek and Stiglitz, 2018; Acemoglu et al., 2020; Benzell et al., 2021).”
AI and Shared Prosperity (2021, background): “But some types of technological change lead to starkly diverging impacts on different factor owners [9].”
Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform (2018, neutral): “While so far much of the work on the economic eﬀects of AI has been theoretical (Acemoglu and Restrepo (2018), Aghion et al. (2017), Korinek and Stiglitz (2017), Sachs and Kotlikoﬀ (2012)), and notably Goldfarb and Treﬂer (2018) in the case of global trade, the introduction of improved MT on eBay…”
Digital technologies, innovation, and skills: emerging trajectories and challenges (2021, background): “, digital technologies will mainly lead to the elimination of some jobs (Acemoglu and Restrepo, 2019; Frey and Osborne, 2017; Korinek and Stiglitz, 2017); others are more optimistic, i.”
The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research (2020, background): “…(AI) has been labeled a "general purpose technology" due to its pervasive role across many different industries (Cockburn, Henderson, & Stern, 2018), and has substantial implications for innovation and socio-economic development (Cockburn et al., 2018; Goolsbee, 2018; Korinek & Stiglitz, 2017).”
Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution (2020, methodology): “None appear to be proposing the more substantial recommendations by Korinek and Stiglitz (2017) of nondistortionary taxation to redistribute innovators surpluses, nor do they pro - pose shortening the length of patents in order to enable the benefits of innovation to be more widely shared.”
Time and the Value of Data (2020, neutral): “…by several authors [e.g., Aghion et al. (2019),Agrawal et al. (2019),Bald-win (2019),Brynjolfsson et al. (2018),Cockburn et al. (2019),Cowgill and Tucker (2020),Jones and Tonetti (2020),Korinek and Stiglitz (2019),Milgrom and Tadelis (2019),Reimers and Shiller (2018),Tirole (2020),Varian (2019)].”