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on Artificial Intelligence |
| By: | Werner, Tobias |
| JEL: | C90 D83 L13 L41 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc23:277573&r=ain |
| By: | Benjamin Schneider; Hillary Vipond |
| Abstract: | Debates about the future of work frequently reference past instances of transformative innovation to preface analysis of how automation and artificial intelligence could reshape society and the economy. However, technological shifts in history are rarely considered in depth or used to improve predictions and planning for the coming decades. In this paper we show that a deeper understanding of history can expand knowledge of possibilities and pitfalls for employment in the future. We open by demonstrating that evidence from historical events has been used to inform responses to present-day challenges. We argue that history provides the only way to analyze the long-term impacts of technological change, and that the scale of the First Industrial Revolution may make it the only precedent for emerging transformations. Next, we present an overview of the current debates around the potential effects of impending labor-replacing innovation. We then summarize existing historical research on the causes and consequences of technological change and identify areas in which salient historical findings are overlooked. We close by proposing further research into past technological shocks that can enhance our understanding of work and employment in an automated future. |
| Keywords: | technological change, innovation, automation, future of work, technological unemployment, labor displacement |
| JEL: | J23 J64 J81 N31 N33 N71 N73 O31 O33 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10766&r=ain |
| By: | von Maydell, Richard |
| JEL: | L11 L13 L52 O33 D21 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc23:277654&r=ain |
| By: | Khaled AlAjmi; Jose Deodoro; Mr. Ashraf Khan; Kei Moriya |
| Abstract: | Using the 2010, 2015, and 2020/2021 datasets of the IMF’s Central Bank Legislation Database (CBLD), we explore artificial intelligence (AI) and machine learning (ML) approaches to analyzing patterns in central bank legislation. Our findings highlight that: (i) a simple Naïve Bayes algorithm can link CBLD search categories with a significant and increasing level of accuracy to specific articles and phrases in articles in laws (i.e., predict search classification); (ii) specific patterns or themes emerge across central bank legislation (most notably, on central bank governance, central bank policy and operations, and central bank stakeholders and transparency); and (iii) other AI/ML approaches yield interesting results, meriting further research. |
| Keywords: | central bank legislation; central banking; artificial intelligence; machine learning; Bayesian algorithm; Boolean algorithm; central bank governance; law and economics |
| Date: | 2023–11–17 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2023/241&r=ain |