Artificial Intelligence and Its Implications for Income Distribution and Unemployment

Abstract

We would like to thank our discussant Tyler Cowan as well as Jayant Ray and participants at the NBER conference for helpful comments. We also acknowledge research assistance from Haaris Mateen as well as financial support from the Institute for New Economic Thinking (INET) and the Rewriting the Rules project at the Roosevelt Institute, supported by the Ford and Open Society Foundations, and the Bernard and Irene Schwartz Foundation. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Deep Learning Meets Deep Democracy: Deliberative Governance and Responsible Innovation in Artificial Intelligence

“…Poorly designed AI may further generate feedback loops that reinforce inequalities, as in the case of predictive policing (Kaufmann, Egbert, & Leese, 2019), for example, or in predictions of creditworthiness that render it difficult for individuals to escape vicious cycles of poverty (O’Neill, 2014). Indirectly harmful outcomes of AIs can arise from the application of AI technologies more generally, often with long-term consequences, such as large-scale technological unemployment (Korinek & Stiglitz, 2018). Such outcomes can also take the form of so-called latent, secondary, and transformative effects (Mittelstadt et al, 2016) that occur when AI outcomes change the ways that people perceive situations, as, for example, in the case of profiling algorithms that powerfully ontologize the world in particular ways and trigger new patterns of behavior (Pasquale, 2015), though these effects are also evident in the ways that content curation and news recommendation algorithms lead to people being unwittingly socialized in “filter bubbles” (Berman & Katona, 2020).…”

The Impact of Artificial Intelligence on the Mental Health of Manufacturing Workers: The Mediating Role of Overtime Work and the Work Environment

“…Therefore, the impact of AI on employment is expected to be stronger than that of the previous technological revolutions ( 10 ). AI is not only expected to replace jobs in the labor market but also to change the ways in which all occupations complete their tasks, leading to massive technology-driven unemployment in the future ( 11 , 12 ). Some scholars have found that empirically, industrial robots have had a greater impact than capital and other technological advances on the U.S. labor market, and the use of industrial robots has had a stronger negative impact on the employment-to-population ratio and the wage level in some U.S. industries ( 13 ).…”

A mesterséges intelligencia elterjedésének geoökonómiai hatásai és Magyarország

“…Acemoglu & Restrepo (2018a;2018b), Arntz et al (2017), valamint Autor & Salomons (2018) az automatizáció munkaerő-megtakarító jellegére, Autor et al (2003), Autor & Dorn (2013) és Michaels et al (2014) a munkaerőpiaci polarizáció jelenségére hívta fel a figyelmet. Az MI-re való átállás globális hatásai kapcsán, a témával szélesebb geoökonómiai perspektívában, a fejlett és fejlődő országok, illetve különböző munkaerőpiaci rétegek közötti egyenlőtlenséget növelő dinamikákkal foglalkozott Korinek & Stiglitz (2017, Aghion et al (2017) és Varian (2019).…”