Zitatform

Simons, Wouter, Alessandro Turrini & Lara Vivian (2024): Artificial Intelligence: Economic Impact, Opportunities, Challenges, Implications for Policy. (European economy. Discussion paper 210), Brüssel, 29 S.

Abstract

"This discussion paper presents the key features of Artificial Intelligence (AI), highlighting the main differences with respect to previous IT and digital technologies. It presents the most relevant facts about AI diffusion across EU countries, and discusses the main economic implications, focusing especially on its impact on productivity and labour markets. While AI presents a formidable opportunity, it also entails major challenges, with implications for policy. This paper focuses on policies to remove bottlenecks to AI development and adoption, regulatory policies, competition policy, policies to deal with labour market and distributive implications." (Author's abstract, IAB-Doku) ((en))

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Steinhau, Henry, Matthieu Binder, Merlin Münch & Lena Biskup (2024): Künstliche Intelligenz und Beschäftigte im Journalismus. (Working paper Forschungsförderung / Hans Böckler Stiftung 345), Düsseldorf, 55 S.

Abstract

"In dieser Kurzstudie untersuchen die Autor*innen den Einsatz von Künstlicher Intelligenz (KI) im Journalismus aus der Perspektive der Beschäftigten. Mithilfe von Expert*innenInterviews, der Auswertung wissenschaftlicher Literatur und einer Textanalyse von selbstverpflichtenden KI-Richtlinien und Positionspapieren erörtern sie wesentliche Fragen: Wie nehmen Journalist*innen die Verwendung von KI-gestützten Werkzeugen wahr? Welche Folgen erwarten sie für ihren Arbeitsalltag und ihr Berufsbild? Wie werden Betriebsräte einbezogen? Als Quintessenz formulieren sie sechs praxisorientierte Handlungsempfehlungen." (Autorenreferat, IAB-Doku)

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Suleyman, Mustafa (2024): The Coming Wave. Künstliche Intelligenz, Macht und das größte Dilemma des 21. Jahrhunderts. München: Beck, 377 S.

Abstract

"Die Menschheitsgeschichte kennt Innovationsschübe, die unaufhaltsam wie ein Tsunami alles verändern - die landwirtschaftliche Revolution, die Dampfmaschine, das Internet. Künstliche Intelligenz ist die nächste große Welle, die Coming Wave, die auf uns zurollt, und wir sind darauf nicht vorbereitet. Als Mitgründer von DeepMind weiß Mustafa Suleyman wie nur wenige andere, was die neuen Technologien können und was sie anzurichten vermögen. In seinem wegweisenden, vielgelobten Buch verortet der KI-Pionier die kommende Welle in der Geschichte der Menschheit, spielt die politischen und gesellschaftlichen Folgen der neuen Technologien durch, und stellt sich dem größten Dilemma des 21. Jahrhunderts: wie wir von ihnen profitieren, ohne die Kontrolle zu verlieren. Bald werden wir in unserem täglichen Leben von KI umgeben sein. Sie wird unseren Alltag organisieren, unsere Wirtschaft prägen, und sogar Kernaufgaben der Staatsverwaltung übernehmen. Als Mitgründer von DeepMind hat Mustafa Suleyman viele Jahre im Zentrum der KI-Revolution gearbeitet. Das kommende Jahrzehnt wird nach seiner Einschätzung von rasanten technologischen Sprüngen geprägt sein, von neuen technologischen Möglichkeiten, über deren Folgen und Risiken wir noch kein klares Bild haben. Eines aber wissen wir: Wir brauchen die KI, um die Herausforderungen zu meistern, vor denen wir stehen, etwa den Klimawandel. Gleichzeitig bergen die neuen Technologien Gefahren, wie sie von keiner vorherigen Fortschrittswelle ausgingen, bis hin zur Auflösung von Staaten und einer Verdrängung des Menschen. Was macht man mit einer Welle, die auf den Strand zurast und sich nicht aufhalten lässt? Man versucht sie zu kanalisieren. Genau das ist das Anliegen dieses Buches: Inmitten immer intensiver werdender geopolitischer Konflikte den schmalen Grat zu finden, auf dem wir die Früchte der Technologie ernten, ohne ihr zum Opfer zu fallen. Das ist die zentrale Aufgabe unserer Zeit. "Unsere Zukunft hängt von den neuen Technologien ab, ist gleichzeitig aber durch sie gefährdet." Was KI für die Zukunft der Menschheit bedeutet Alle, die heute leben, sind betroffen DAS Buch zu Risiken, Chancen und Folgen der neuen Technologien Mustafa Suleyman ist Mitbegründer von DeepMind und einer der Pioniere der KI-Industrie. Wie wir die Oberhand behalten: Mustafa Suleyman über die Kernfrage unseres Jahrhunderts Ein 12-Punkte-Plan für den Umgang mit KI" (Autorenreferat, IAB-Doku, © C.H. Beck)

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Tirabeni, Lia (2024): Bounded Well-Being: Designing Technologies for Workers' Well-Being in Corporate Programmes. In: Work, Employment and Society, Jg. 38, H. 6, S. 1506-1527. DOI:10.1177/09500170231203113

Abstract

"This article examines the relationship between workers’ well-being and digitalisation at work. It is based on the findings of a qualitative study carried out in a manufacturing company, and it focuses on the development of a wearable device for well-being. Using the analytical concepts of ‘translation’ and ‘inscription’ taken from Actor-Network Theory, it explores how digital technologies for well-being are designed in corporate programmes and shows how the final technology results from processes of inscription and translation performed by the actors involved in the design phase. The end device embodies a concept of well-being that has been called ‘bounded’ to emphasise how well-being at work is limited by organisational constraints. The article invites a rethinking of hedonic well-being at work as a precondition for eudaimonic well-being so that the human being is understood as a psychophysical unit that is part of a rich social context." (Author's abstract, IAB-Doku) ((en))

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Yilmaz, Erdem Dogukan & Christian Peukert (2024): Who Benefits from AI? Project-Level Evidence on Labor Demand, Operations and Profitability. (CESifo working paper 11321), München, 31 S.

Abstract

"We examine how the adoption of digital automation technology affects labor demand, operations and profitability in the context of the logistics industry. Our data covers 9,300 digital automation projects in a multinational company involving service robots and machine learning-based software from 2019 to 2021, alongside fine-grained labor and operations data. To identify causal effects, we leverage exogenous variation from supply-chain disruptions and travel restrictions during COVID-19 and an import ban on information and communication technologies imposed by the Trump administration. We find that total labor cost increased after the adoption of digital automation technology, attributable to increased labor demand and more reliance on temporary workers. However, managerial hours declined, possibly due to increased efficiency. Furthermore, digital automation technology increased revenue and profit through a reduction in operational cost, improved utilization of warehouse space, and higher profit margins. However, the effects of digital automation technology are not homogeneous. We highlight substantial complementarities between hardware and software technologies. Management units that only use software technology experience only half the increase in revenue and profit." (Author's abstract, IAB-Doku) ((en))

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Zhao, Yong, Da Yin, Lili Wang & Yihua Yu (2024): The rise of artificial intelligence, the fall of human wellbeing? In: International Journal of Social Welfare, Jg. 33, H. 1, S. 75-105. DOI:10.1111/ijsw.12586

Abstract

"Concerns exist regarding the impact on our lives of the rise of artificial intelligence (AI). Using a large dataset of 137 countries over the period 2005–2018 from multiple sources, we estimate the causal effect of AI on individual-level subjective wellbeing. Our identification strategy is inferred from the gravity framework and uses merely the variation in exogenous drivers of a country's AI development. We find a significant negative effect of AI on an individual's wellbeing, in terms of current levels or expectations of future wellbeing. The results are robust to alternative measures of AI, identification strategies, and sampling. Moreover, we find evidence of significant heterogeneity in the impact of AI on individual wellbeing. Further, this dampening effect on individual wellbeing resulting from the use of AI is more prominent among young people, men, high-income groups, high-skilled groups, and manufacturing workers." (Author's abstract, IAB-Doku, Published by arrangement with John Wiley & Sons) ((en))

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Özgül, Pelin, Marie-Christine Fregin, Michael Stops, Simon Janssen & Mark Levels (2024): High-skilled Human Workers in Non-Routine Jobs are Susceptible to AI Automation but Wage Benefits Differ between Occupations. (arXiv papers 2404.06472), 55 S. DOI:10.48550/arXiv.2404.06472

Abstract

"Artificial Intelligence (AI) will change human work by taking over specific job tasks, but there is a debate which tasks are susceptible to automation, and whether AI will augment or replace workers and affect wages. By combining data on job tasks with a measure of AI susceptibility, we show that more highly skilled workers are more susceptible to AI automation, and that analytical non-routine tasks are at risk to be impacted by AI. Moreover, we observe that wage growth premiums for the lowest and the highest required skill level appear unrelated to AI susceptibility and that workers in occupations with many routine tasks saw higher wage growth if their work was more strongly susceptible to AI. Our findings imply that AI has the potential to affect human workers differently than canonical economic theories about the impact of technology on work these theories predict." (Author's abstract, IAB-Doku) ((en))

Abstract

"Die Organisation für wirtschaftliche Zusammenarbeit und Entwicklung (OECD) übergab am 11. Juni einen von ihr erstellten Bericht zum Stand der Entwicklung und Nutzung von Künstlicher Intelligenz (KI) in Deutschland an Vertreterinnen und Vertreter der Bundesregierung. In ihrem Bericht hebt die OECD die gute Ausgangsposition Deutschlands für KI hervor und bescheinigt der Bundesregierung, dass die KI-Strategie der Grundstein dafür war, dass sich Deutschland zu einem weltweit führenden Standort für KI – insbesondere im Bereich Forschung – entwickelt hat. Der in Deutschland gewählte menschenzentrierte Ansatz setzt dabei weltweit Maßstäbe für einen verantwortungsvollen Umgang mit KI." (Autorenreferat, IAB-Doku)

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(2024): Job Creation and Local Economic Development 2024. The Geography of Generative AI. (Job creation and local economic development [6]), Paris, 189 S. DOI:10.1787/83325127-en

Abstract

"Regions across the OECD face a range of labour market challenges and are undergoing a significant transformation. An ageing workforce, low labour productivity growth, persistent regional disparities, pervasive labor shortages, and new technologies will require both people and places to undergo transitions. This report, Job Creation and Local Economic Development 2024: The Geography of Generative AI, examines the health of regional and local labor markets, including through new estimates on regional labour shortages and their drivers. New tools and technologies, such as Generative AI, could help policymakers address these challenges and seize new opportunities for job creation and local economic growth. This report provides novel evidence of the geography of the impact of Generative AI on jobs across the OECD. It examines which places within countries and types of workers are most exposed to Generative AI and contrasts this with the labor market impact of past waves of technologies that drove automation. Finally, it discusses local and place-based actions and policies for seizing the benefits of Generative AI, such as boosting productivity, mitigating labor shortages and demographic change, as well as for mitigating risks of job displacement." (Author's abstract, IAB-Doku) ((en))

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(2024): Using AI in the workplace : Opportunities, risks and policy responses. (OECD artificial intelligence papers 11), Paris, 15 S. DOI:10.1787/73d417f9-en

Abstract

"AI can bring significant benefits to the workplace. In the OECD AI surveys of employers and workers, four in five workers say that AI improved their performance at work and three in five say that it increased their enjoyment of work. But the benefits of AI depend on addressing the associated risks. Taking the effect of AI into account, occupations at highest risk of automation account for about 27% of employment in OECD countries. Workers also express concerns around increased work intensity, the collection and use of data, and increasing inequality. To support the adoption of trustworthy AI in the workplace, this policy paper identifies the main risks that need to be addressed when using AI in the workplace. It identifies the main policy gaps and offers possible policy avenues specific to labour markets." (Author's abstract, IAB-Doku) ((en))

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(2024): Assessing potential future artificial intelligence risks, benefits and policy imperatives. (OECD Artificial Intelligence Papers 27), Paris, 68 S. DOI:10.1787/3f4e3dfb-en

Abstract

"The swift evolution of AI technologies calls for policymakers to consider and proactively manage AI-driven change. The OECD’s Expert Group on AI Futures was established to help meet this need and anticipate AI developments and their potential impacts. Informed by insights from the Expert Group, this report distils research and expert insights on prospective AI benefits, risks and policy imperatives. It identifies ten priority benefits, such as accelerated scientific progress, productivity gains and better sense-making and forecasting. It discusses ten priority risks, such as facilitation of increasingly sophisticated cyberattacks; manipulation, disinformation, fraud and resulting harms to democracy; concentration of power; incidents in critical systems and exacerbated inequality and poverty. Finally, it points to ten policy priorities, including establishing clearer liability rules, drawing AI “red lines”, investing in AI safety and ensuring adequate risk management procedures. The report reviews existing public policy and governance efforts and remaining gaps." (Author's abstract, IAB-Doku) ((en))

Zitatform

Acemoglu, Daron & Simon Johnson (2023): Rebalancing AI. In: Finance and development H. December, S. 26-29.

Abstract

"Optimistic forecasts regarding the growth implications of AI abound. AI adoption could boost productivity growth by 1.5 percentage points per year over a 10-year period and raise global GDP by 7 percent ($7 trillion in additional output), according to Goldman Sachs. Industry insiders offer even more excited estimates, including a supposed 10 percent chance of an “explosive growth” scenario, with global output rising more than 30 percent a year. All this techno-optimism draws on the “productivity bandwagon”: a deep-rooted belief that technological change— including automation—drives higher productivity, which raises net wages and generates shared prosperity. Such optimism is at odds with the historical record and seems particularly inappropriate for the current path of “just let AI happen,” which focuses primarily on automation (replacing people). We must recognize that there is no singular, inevitable path of development for new technology. And, assuming that the goal is to sustainably improve economic outcomes for more people, what policies would put AI development on the right path, with greater focus on enhancing what all workers can do?" (Text excerpt, IAB-Doku) ((en))

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Agarwal, Ayushi & Sreejith Alathur (2023): Metaverse revolution and the digital transformation: intersectional analysis of Industry 5.0. In: Transforming Government : People, Process and Policy, Jg. 17, H. 4, S. 688-707. DOI:10.1108/TG-03-2023-0036

Abstract

"Purpose: This study aims to investigate metaverse elements affecting digital transformation and examine how the metaverses ’ enabled digital transformation affects Industry 5.0. Design/methodology/approach This paper adopts intersectional research methodologies to understand how metaverse technologies facilitate digital transformation and contribute to Industry 5.0. The Metaverse literature is bibliometrically analyzed to identify the intersection of digital transformation and components of Industry 5.0. Findings The conceptualization of the metaverse, its ecosystem and its enabling technologies are consistent with the human-centric, resilient and sustainable vision of the industrial revolution. The findings show that scientific research into digital transformation contributes to refining potential conflicts and tensions that may arise at the intersection of the metaverse and Industry 5.0. Research limitations/implications Study have significant implications for digital transformation research, as transformation studies help to fine-tune emerging technologies such as the metaverse for the industrial revolution. Based on the findings, the authors have provided a threat model for the Sustainable Metaverse Revolution. Social implications The utility of metaverse technologies in industrial revolutions necessitates the formulation of business model policies that promote the metaverse-enabled digital transformation. Policy recommendations for integrated development approaches are also provided in this paper. Originality/value The metaverse-enabled digital transformation and its implications for the industrial revolution are less reported. The current study addresses the importance of such intersectional studies." (Author's abstract, IAB-Doku) ((en))

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Albanesi, Stefania, António Dias da Silva, Juan F. Jimeno, Ana Lamo & Alena Wabitsch (2023): New Technologies and Jobs in Europe. (IZA discussion paper / Forschungsinstitut zur Zukunft der Arbeit 16227), Bonn, 58 S.

Abstract

"We examine the link between labour market developments and new technologies such as artificial intelligence (AI) and software in 16 European countries over the period 2011- 2019. Using data for occupations at the 3-digit level in Europe, we find that on average employment shares have increased in occupations more exposed to AI. This is particularly the case for occupations with a relatively higher proportion of younger and skilled workers. This evidence is in line with the Skill Biased Technological Change theory. While there exists heterogeneity across countries, only very few countries show a decline in employment shares of occupations more exposed to AI-enabled automation. Country heterogeneity for this result seems to be linked to the pace of technology diffusion and education, but also to the level of product market regulation (competition) and employment protection laws. In contrast to the findings for employment, we find little evidence for a relationship between wages and potential exposures to new technologies." (Author's abstract, IAB-Doku) ((en))

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Avery, Mallory, Andreas Leibbrandt & Joseph Vecci (2023): Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech. (Discussion paper / Monash University, Department of Economics 2023-09), Clayton, 69 S.

Abstract

"The use of Artificial Intelligence (AI) in recruitment is rapidly increasing and drastically changing how people apply to jobs and how applications are reviewed. In this paper, we use two field experiments to study how AI in recruitment impacts gender diversity in the male-dominated technology sector, both overall and separately for labor supply and demand. We find that the use of AI in recruitment changes the gender distribution of potential hires, in some cases more than doubling the fraction of top applicants that are women. This change is generated by better outcomes for women in both supply and demand. On the supply side, we observe that the use of AI reduces the gender gap in application completion rates. Complementary survey evidence suggests that this is driven by female jobseekers believing that there is less bias in recruitment when assessed by AI instead of human evaluators. On the demand side, we find that providing evaluators with applicants' AI scores closes the gender gap in assessments that otherwise disadvantage female applicants. Finally, we show that the AI tool would have to be substantially biased against women to result in a lower level of gender diversity than found without AI." (Author's abstract, IAB-Doku) ((en))

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Becker, Marco, Carl Heinz Daube & Ernst Reinking (2023): Industrie 5.0. (EconStor Preprints 270296), Kiel, 12 S.

Abstract

"Spätestens seit der Etablierung von ChatGPT als eine für die breite Masse sowohl der Unternehmen als auch der Bevölkerung gleichermaßen interessante Anwendung der Künstlichen Intelligenz im November 2022 neigt sich die Epoche der Industrie 4.0 dem Ende entgegen. In diesem Working Paper werden die Grenzen der Industrie 4.0 aufgezeigt und die Potenziale der Industrie 5.0 analysiert." (Autorenreferat, IAB-Doku)

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Berg, Janine, Francis Green, Laura Nurski & David A. Spencer (2023): Risks to job quality from digital technologies: Are industrial relations in Europe ready for the challenge? In: European journal of industrial relations, Jg. 29, H. 4, S. 347-365. DOI:10.1177/09596801231178904

Abstract

"We examine job quality effects of new digital technologies, using the European frame of seven job quality domains: Pay, Working Time Quality, Prospects, Skills and Discretion, Work Intensity, Social Environment, and Physical Environment. Theoretical effects are ambivalent across all domains. The analysis of these effects confirms that digital technologies can both improve and harm job quality depending on how they are used. In light of this analysis and to think through the challenge of regulating digital technologies, we review emerging regulations across several European countries. Drawing on the principles of human-centred design, we argue that worker participation is important for securing good job quality outcomes, at both the innovation and adoption stages. We also consider the application of data protection legislation to the regulation of job quality. Overall, the paper extends debate about the future of work beyond employment and pay, on to a consideration of job quality more broadly." (Author's abstract, IAB-Doku) ((en))

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Bertschek, Irene (2023): Jetzt bloß nicht den Anschluss verlieren! – Status quo, Potenziale und Herausforderungen von Künstlicher Intelligenz. In: Wirtschaftsdienst, Jg. 103, H. 8, S. 518-520. DOI:10.2478/wd-2023-0149

Abstract

"Artificial Intelligence (AI) is likely to be the next general purpose technology. The U.S. and China are important players in the development of AI. Germany has a vibrant AI startup scene and is among the first third of EU countries in applying AI technologies. In order not to lose touch with international developments, Germany should work toward creating research- and innovation-friendly framework conditions. Appropriate measures include improving data availability, building AI expertise and enabling flexible regulation." (Author's abstract, IAB-Doku) ((en))

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Bonfiglioli, Alessandra, Rosario Crinò, Gino Gancia & Ioannis Papadakis (2023): Artificial Intelligence and Jobs: Evidence from US Commuting Zones. (CESifo working paper 10685), München, 41 S.

Abstract

"We study the effect of Artificial Intelligence (AI) on employment across US commuting zones over the period 2000-2020. A simple model shows that AI can automate jobs or complement workers, and illustrates how to estimate its effect by exploiting variation in a novel measure of local exposure to AI: job growth in AI-related professions built from detailed occupational data. Using a shift-share instrument that combines industry-level AI adoption with local industry employment, we estimate robust negative effects of AI exposure on employment across commuting zones and time. We find that AI's impact is different from other capital and technologies, and that it works through services more than manufacturing. Moreover, the employment effect is especially negative for low-skill and production workers, while it turns positive for workers at the top of the wage distribution. These results are consistent with the view that AI has contributed to the automation of jobs and to widen inequality." (Author's abstract, IAB-Doku) ((en))

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Broecke, Stijn, Marguerita Lane & Morgan Williams (2023): The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers. (OECD social, employment and migration working papers 288), Paris, 156 S. DOI:10.1787/ea0a0fe1-en

Abstract

"New OECD surveys of employers and workers in the manufacturing and finance sectors of seven countries shed new light on the impact that Artificial Intelligence has on the workplace —an under-researched area to date due to lack of data. The findings suggest that both workers and their employers are generally very positive about the impact of AI on performance and working conditions. However, there are also concerns, including about job loss—an issue that should be closely monitored. The surveys also indicate that, while many workers trust their employers when it comes to the implementation of AI in the workplace, more can be done to improve trust. In particular, the surveys show that both training and worker consultation are associated with better outcomes for workers." (Author's abstract, IAB-Doku) ((en))

Zitatform

Brynjolfsson, Erik, Danielle Li & Lindsey R. Raymond (2023): Generative AI at Work. (NBER working paper / National Bureau of Economic Research 31161), Cambridge, Mass, 56 S.

Abstract

"We study the staggered introduction of a generative AI-based conversational assistant using data from 5,179 customer support agents. Access to the tool increases productivity, as measured by issues resolved per hour, by 14 percent on average, with the greatest impact on novice and low-skilled workers, and minimal impact on experienced and highly skilled workers. We provide suggestive evidence that the AI model disseminates the potentially tacit knowledge of more able workers and helps newer workers move down the experience curve. In addition, we show that AI assistance improves customer sentiment, reduces requests for managerial intervention, and improves employee retention." (Author's abstract, IAB-Doku) ((en))

Zitatform

Brynjolfsson, Erik & Gabriel Unger (2023): The macroeconomics of artificial intelligence. In: Finance and development H. December, S. 20-25.

Abstract

"Economists have a poor track record of predicting the future. And Silicon Valley repeatedly cycles through hope and disappointment over the next big technology. So a healthy skepticism toward any pronouncements about how artificial intelligence will change the economy is justified. Nonetheless, there are good reasons to take seriously the growing potential of AI—systems that exhibit intelligent behavior, such as learning, reasoning, and problem-solving —to transform the economy, especially given the astonishing technica ladvances of the past year. AI may affect society in a number of areas besides the economy—including national security, politics, and culture. But in this article, we focus on the implications of AI on three broad areas of macroeconomic interest: productivity growth, the labor market, and industrial concentration. AI does not have a predetermined future. It can develop in very different directions. The particular future that emerges will be a consequence of many things, including technological and policy decisions made today. For each area, we present a fork in the road: two paths that lead to very different futures for AI and the economy. In each case, the bad future is the path of least resistance. Getting to the better future will require good policy—including • Creative policy experiments • A set of positive goals for what society wants from AI, not just negative outcomes to be avoided • Understanding that the technological possibilities of AI are deeply uncertain and rapidly evolving and that society must be flexible in evolving with them." (Author's abstract, IAB-Doku) ((en))

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Brühl, Volker (2023): Künstliche Intelligenz – wo stehen wir in Deutschland? In: Wirtschaftsdienst, Jg. 103, H. 8, S. 521-524. DOI:10.2478/wd-2023-0150

Abstract

"Artificial Intelligence (AI) is widely regarded as a technology which will impact the future competitiveness of the German economy. Looking at the research productivity of German scientists and universities in AI, we find that Germany definitively belongs to the top performers in AI research globally, although the United States and China are somewhat ahead. This is not surprising taking into account the sheer size of their talent pools. Furthermore, the majority of promising AI startups are based in the United States, while Germany is clearly underrepresented in the group of excellent AI startups given Germany’s excellence in AI research. Hence, it is obviously more challenging for Germany to translate research excellence into successful entrepreneurship." (Author's abstract, IAB-Doku) ((en))

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Buxmann, Peter & Anne Zöll (2023): Ökonomische Effekte von ChatGPT. In: Controlling & Management Review, Jg. 67, H. 5, S. 16-21. DOI:10.1007/s12176-023-1066-4

Abstract

Der Beitrag beleuchtet aus ökonomischer Perspektive die Auswirkungen des auf verschiedenen Algorithmen des maschinellen Lernens beruhenden Chatbots ChatGPT sowie die Potenziale einer Zusammenarbeit zwischen Menschen und Künstlicher Intelligenz. Die bei einer ökonomischen Bewertung der Nutzung digitaler Technologien unterschiedenen substitutiven Effekte (Kosten- sowie Zeiteinsparungen) und komplementären Effekte (Verbesserung der Qualität) lassen sich auch bei der Anwendung von ChatGPT und anderen Sprachmodellen feststellen. Referiert werden hierzu u.a. die Ergebnisse einer Studie des 'Massachusetts Institute of Technology (MIT), die die verschiedenen Anwendungsgebiete aufzeigt und die ökonomischen Vorteile quantifiziert. Entgegen der in der Öffentlichkeit geäußerten Befürchtung einer massiven Vernichtung von Arbeitsplätzen oder Horror-Szenarien durch unkontrollierbare Verselbständigungen der KI heben die Autoren eine andere Perspektive hervor: Die Zusammenarbeit zwischen Menschen und KI (Stichwort 'hybride Intelligenz') kann die Entscheidungsfindung (etwa Strategie- und Kaufentscheidungen) beschleunigen und durch den gegenseitigen Austausch und das gegenseitige Lernen voneinander verbessern. Noch ist nicht das ganze Potenzial dieser 'Basistechnologie des 21. Jahrhunderts' zu ermessen, beispielhaft können aber schon heute die Anwendungsbereiche medizinische Diagnostik, Texterstellung und Softwareentwicklung genannt werden. (IAB)

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Büchel, Jan, Jan Engler & Armin Mertens (2023): KI-Einsatzbereiche in Deutschland. Eine Analyse von KI-Stellenanzeigen : Gutachten im Projekt „Entwicklung und Messung der Digitalisierung der Wirtschaft am Standort Deutschland". Berlin, 31 S.

Abstract

"Zwei Studien untersuchten bereits anhand von KI-Stellenanzeigen, wie die KI-Bedarfe in Deutschland einerseits regional verteilt sind (Büchel/Mertens, 2022) und andererseits welche Anforderungsprofile neue Beschäftigte mit KI-Kompetenzen erfüllen sollten (ebenda, 2021). Unklar bleibt bislang jedoch, für welche Zwecke ausschreibende Unternehmen KI-Kompetenzen primär benötigen und einsetzen möchten. Erkenntnisse darüber schaffen eine größere Transparenz darüber, wofür Unternehmen KI-Talente überhaupt einsetzen und an welchen Stellen im Unternehmen KI relevant ist. Damit ergänzt die vorliegende Analyse Studien zum Einsatz von KI in Unternehmen (Rammer, 2020) und zu KI-Gründungen in Deutschland (Rammer, 2022). Im Folgenden wird untersucht, welche Einsatzbereiche in aktuellen KI-Stellenanzeigen relevant sind, wie häufig sie jeweils auftreten und wie sich die Bedarfe in den einzelnen Einsatzbereichen im Zeitverlauf entwickeln. Dafür erläutert Abschnitt 2 zunächst das methodische Vorgehen, mit dem die Autoren selbst mithilfe von KI, beziehungsweise einer Kombination aus einem Machine-Learning-Modell und einem regelbasierten Ansatz, automatisiert KI-Einsatzbereiche in jeder der etwa 73.000 KI-Stellenanzeigen aus den ersten Quartalen der Jahre 2019 bis 2023 identifizieren konnten. Die KI-Einsatzbereiche, die für die ausschreibenden Unternehmen relevant sind, werden in Kapitel 3 analysiert. Es wird zudem untersucht, wie hoch die KIBedarfe pro Einsatzbereich sind sowie welche typischen Überschneidungen und regionalen Besonderheiten auftreten. Kapitel 4 gibt ein Fazit." (Autorenreferat, IAB-Doku)

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Calvino, Flavio & Luca Fontanelli (2023): A portrait of AI adopters across countries: Firm characteristics, assets' complementarities and productivity. (OECD science, technology and industry working papers 2023,02), Paris, 85 S. DOI:10.1787/0fb79bb9-en

Abstract

"This report analyses the use of artificial intelligence (AI) in firms across 11 countries. Based on harmonised statistical code (AI diffuse) applied to official firm-level surveys, it finds that the use of AI is prevalent in ICT and Professional Services and more widespread across large – and to some extent across young – firms. AI users tend to be more productive, especially the largest ones. Complementary assets, including ICT skills, high-speed digital infrastructure, and the use of other digital technologies, which are significantly related to the use of AI, appear to play a critical role in the productivity advantages of AI users." (Author's abstract, IAB-Doku) ((en))

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Cardon, Peter W., Kristen Getchell, Stephen Carradini, Carolin Fleischmann & James Stapp (2023): Generative AI in the Workplace: Employee Perspectives of ChatGPT Benefits and Organizational Policies. (SocArXiv papers), [Charlottesville, VA], 17 S. DOI:10.31235/osf.io/b3ezy

Abstract

"Key Findings and Conclusions : Many US workers in this sample are using ChatGPT for professional purposes. Roughly the following percentages have already used ChatGPT in the following ways: 42% for researching a topic or generating ideas. 32% for drafting messages. 26% for drafting longer documents, such as reports. 22% for editing text. Many US workers in this sample believe ChatGPT can help them become better communicators. This is particularly the case for executives and managers. Roughly two thirds of executives (67%) and managers (64%) believe generative AI can help them communicate more effectively. Early adopters of ChatGPT in this sample hold much different views of generative AI than do non-users of ChatGPT. Early adopters hold the following distinctive views: They are much more likely to think AI is good for society than non-users (64% to 22%) and believe it will make them more productive (82% for early adopters; 26% for non-users); however, they are also more likely to worry about the ethical implications of AI (68% to 55%) in the workplace and worry that their own job will be replaced by AI (41% to 20%). They are much more likely to think generative AI will support them in their work. About 85% of early adopters say that ChatGPT can help them generate ideas for work compared to about 50% of non-users. About 73% of early adopters say it can improve the quality of their work compared to 42% of non-users. About 74% of early adopters say it can help them communicate more effectively compared to 41% of non-users. Executives and managers are slightly more likely to be enthusiastic about the benefits. Employees in organizations with generative AI policies view these policies positively. Those who are aware of an organizational policy about generative AI generally believe it has supported more comfort in using ChatGPT for work, has improved trust, has improved efficiency, and has provided legal protections. Those who are early adopters are generally more positive about each of these benefits of organizational policy than those who are non-users of ChatGPT. Most early adopters of generative AI in organizations without generative AI policies want more guidance about ChatGPT use. Most early adopters believe an organizational policy would make them more comfortable using ChatGPT (61%), that it would increase trust (56%), and that it would improve efficiency (66%)." (Text excerpt, IAB-Doku) ((en))

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Carstensen, Tanja (2023): Digitalisierung der Arbeit – eine Zwischenbilanz aus Geschlechterperspektiven. In: WSI-Mitteilungen, Jg. 76, H. 5, S. 374-382. DOI:10.5771/0342-300X-2023-5-374

Abstract

"Die Digitalisierung der Arbeitswelt seit der Mitte der 2010er Jahre wurde früh mit weitreichenden Hoffnungen und Befürchtungen für Veränderungen in den Geschlechterverhältnissen diskutiert. Mittlerweile liegen diverse, ein breites Feld an Fragen umspannende empirische Studien vor. Nach einigen Vormerkungen zum Verhältnis von Gender und Technik resümiert der Beitrag die bisherigen Befunde entlang von fünf Themenfeldern, die sich als Schwerpunkte der Digitalisierungsforschung aus Geschlechterperspektiven herausgebildet haben: 1. Ortsflexibilisierung / Homeoffice, 2. Plattformen, 3. Automatisierung und neue Anforderungen, 4. Diskriminierung durch Algorithmen und KI und 5. mangelnde Diversität und (globale) Ungleichheiten in der Technikentwicklung. Die Autorin schließt mit einer Zwischenbilanz dieser bisher vorliegenden Befunde und benennt weiteren Forschungsbedarf." (Autorenreferat, IAB-Doku)

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Czarnitzki, Dirk, Gastón P. Fernández & Christian Rammer (2023): Artificial intelligence and firm-level productivity. In: Journal of Economic Behavior & Organization, Jg. 211, S. 188-205. DOI:10.1016/j.jebo.2023.05.008

Abstract

"Artificial Intelligence (AI) is often regarded as the next general-purpose technology with a rapid, penetrating, and far-reaching use over a broad number of industrial sectors. The main feature of new general-purpose technology is to enable new ways of production that may increase productivity. However, to date, only a few studies have investigated the likely productivity effects of AI at the firm-level, presumably due to limited data availability. We exploit unique survey data on firms' adoption of AI technology and estimate its productivity effects with a sample of German firms. We employ both a cross-sectional dataset and a panel database. To address the potential endogeneity of AI adoption, we also implement IV estimators. We find positive and significant associations between the use of AI and firm productivity. This finding holds for different measures of AI usage, i.e., an indicator variable of AI adoption, and the intensity with which firms use AI methods in their business processes." (Author's abstract, IAB-Doku, © 2023 Elsevier) ((en))

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Damioli, Giacomo, Vincent Van Roy, Daniel Vertesy & Marco Vivarelli (2023): AI technologies and employment: micro evidence from the supply side. In: Applied Economics Letters, Jg. 30, H. 6, S. 816-821. DOI:10.1080/13504851.2021.2024129

Abstract

"In this work we investigate the possible job-creation impact of artificial intelligence (AI) technologies, focusing on the supply side, where the development of these technologies can be conceived as product innovations in upstream sectors. The empirical analysis is based on a worldwide longitudinal sample (obtained by merging the EPO PATSTAT and BvD-ORBIS databases) of more than 3,500 front-runner companies that patented AI-related inventions over the period 2000–2016. Based on system GMM estimates of dynamic panel models, our results show a positive and significant impact of AI patent families on employment, supporting the labour-friendly nature of AI product innovation." (Author's abstract, IAB-Doku) ((en))

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Damioli, Giacomo, Vincent Van Roy, Daniel Vertesy & Marco Vivarelli (2023): The employment impact of AI technologies among AI innovators. (MSI discussion paper / KU Leuwen 2306), KU Leuven, Faculty of Economics and Business (FEB), Department of Management, Strategy and Innovation, Leuven 36 S.

Abstract

"This study supports the labour-friendly nature of product innovation among developers of artificial intelligence (AI) technologies. GMM-SYS estimates on a worldwide longitudinal dataset covering 3,500 companies that patented inventions related to AI technologies over the period 2000-2016 show a positive and significant impact of AI patent families on employment. The effect is small in magnitude and limited to service sectors and younger firms, which are front-runners of the AI revolution. We also detect some evidence of increasing returns suggesting that innovative companies more focused on AI technologies are those obtaining larger impacts in terms of job creation." (Author's abstract, IAB-Doku) ((en))

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De Stefano, Valerio & Simon Taes (2023): Algorithmic management and collective bargaining. In: Transfer, Jg. 29, H. 1, S. 21-36. DOI:10.1177/10242589221141055

Abstract

"Dieser Artikel befasst sich mit den Herausforderungen, die durch die Einführung von Management durch Algorithmen und durch künstliche Intelligenz in der Arbeitswelt entstehen. Dabei geht es in erster Linie um die Risiken, die neue Managementtechnologien für grundlegende Rechte und Prinzipien wie Nichtdiskriminierung, Vereinigungsfreiheit und das Recht auf Privatsphäre darstellen. Der Artikel argumentiert, dass Tarifverhandlungen das am besten geeignete Regulierungsinstrument sind, um auf diese Herausforderungen zu reagieren, und dass die aktuellen Rechtsetzungsinitiativen der EU die Rolle von Tarifverhandlungen in diesem Bereich nicht gebührend anerkennen. Der Artikel gibt ebenfalls eine Übersicht über die derzeit laufenden Initiativen von Gewerkschaftsbewegungen in Europa, um das Management durch Algorithmen einzuhegen." (Autorenreferat, IAB-Doku)

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Deng, Liuchun, Steffen Müller, Verena Plümpe & Jens Stegmaier (2023): Robots, Occupations, and Worker Age: A Production-unit Analysis of Employment. (IWH-Diskussionspapiere 2023,05), Halle, 45 S.

Abstract

"Wir analysieren die Auswirkungen der Einführung von Robotern auf die Zusammensetzung der Beschäftigung anhand neuer Mikrodaten über den Einsatz von Robotern in deutschen Betrieben des verarbeitenden Gewerbes in Verbindung mit weiteren Daten. Unser theoretisches Modell sagt positive Beschäftigungseffekte für die am wenigsten routineintensiven Berufe und für junge Arbeitnehmer voraus, wobei letztere sich besser an den Wandel anpassen können. Eine Event-Study zur Einführung von Robotern findet hierfür Evidenz. Wir finden für keine Berufs- oder Altersgruppe negative Beschäftigungseffekte, aber die Fluktuation unter gering qualifizierten Arbeitnehmern steigt stark an. Wir kommen zu dem Schluss, dass der Verdrängungseffekt von Robotern berufsabhängig, aber altersneutral ist, während der Wiedereinstellungseffekt altersabhängig ist und vor allem jungen Arbeitnehmern zugute kommt." (Autorenreferat, IAB-Doku)

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Dicks, Alexander, Martin Ehlert, Insa Grüttgen, Benjamin Schulz & Basha Vicari (2023): ChatGPT, cobots and the like. How new automation technologies are transforming the working world. In: WZB-Mitteilungen H. 180, 2023-05-10.

Abstract

"Artificial intelligence and automation are currently being debated fiercely. How can these new technologies and applications support people in their work? Will jobs be replaced by AI? Fear of job loss due to digitalization and of loss of autonomy is a widespread concern. The aim of the study presented here is to find out how widespread digital assistance systems are, who uses them and how this affects different groups of employees." (Author's abstract, IAB-Doku) ((en))

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Dicks, Alexander, Martin Ehlert, Insa Grüttgen, Benjamin Schulz & Basha Vicari (2023): ChatGPT, Cobots & Co. Wie neue Automatisierungstechnologien die Arbeitswelt verändern. In: WZB-Mitteilungen H. 180, 2023-05-10.

Abstract

"Künstliche Intelligenz und Automatisierung werden zur Zeit heftig diskutiert. Wie können diese neuen Technologien und Anwendungen Menschen bei ihrer Arbeit unterstützen? Werden Arbeitsplätze durch KI ersetzt? Die Angst vor Arbeitsplatzverlust durch Digitalisierung und vor Fremdbestimmung ist eine weit verbreitete Sorge. Ziel der hier vorgestellten Studie ist es herauszufinden, wie weit digitale Assistenzsysteme verbreitet sind, wer sie nutzt und wie sich das auf verschiedene Beschäftigtengruppen auswirkt." (Autorenreferat, IAB-Doku)

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Engberg, Erik, Michael Koch, Magnus Lodefalk & Sarah Schroeder (2023): Artificial Intelligence, Tasks, Skills and Wages: Worker-Level Evidence from Germany. (Ratio working paper 371), Stockholm, 55 S.

Abstract

"This paper documents novel facts on within-occupation task and skill changes over the past two decades in Germany. In a second step, it reveals a distinct relationship between occupational work content and exposure to artificial intelligence (AI) and automation (robots). Workers in occupations with high AI exposure, perform different activities and face different skill requirements, compared to workers in occupations ex- posed to robots. In a third step, the study uses individual labor market biographies to investigate the impact on wages between 2010 and 2017. Results indicate a wage growth premium in occupations more exposed to AI, contrasting with a wage growth discount in occupations exposed to robots. Finally, the study further explores the dynamic in- fluence of AI exposure on individual wages over time, uncovering positive associations with wages, with nuanced variations across occupational groups." (Author's abstract, IAB-Doku) ((en))

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Engels, Barbara (2023): Künstliche Intelligenz in der deutschen Wirtschaft: Ohne Digitalisierung und Daten geht nichts. In: Wirtschaftsdienst, Jg. 103, H. 8, S. 525-529. DOI:10.2478/wd-2023-0151

Abstract

"Artificial Intelligence (AI) holds immense potential for enhancing prosperity. However, the adoption of AI in German businesses remains limited, with only 19% of companies utilizing AI in 2022. The successful implementation of AI relies on two key prerequisites: a company’s digitalisation and data economy readiness. The Digitalisation Index reveals slow progress in digitalisation across sectors, indicating a need for increased efforts. Additionally, companies must enhance their data economy readiness to efficiently utilize data for AI applications. Failing to tap into the potential of AI may result in significant competitive disadvantages in the future." (Author's abstract, IAB-Doku) ((en))

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Fenwick, Ali, Gabor Molnar & Piper Frangos (2023): Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace. In: Frontiers in artificial intelligence, Jg. 6. DOI:10.3389/frai.2023.1272823

Abstract

"The functions of human resource management (HRM) have changed radically in the past 20 years due to market and technological forces, becoming more cross-functional and data-driven. In the age of AI, the role of HRM professionals in organizations continues to evolve. Artificial intelligence (AI) is transforming many HRM functions and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. A growing body of evidence highlights the benefits AI brings to the field of HRM. Despite the increased interest in AI-HRM scholarship, focus on human-AI interaction at work and AI-based technologies for HRM is limited and fragmented. Moreover, the lack of human considerations in HRM tech design and deployment can hamper AI digital transformation efforts. This paper provides a contemporary and forward-looking perspective to the strategic and human-centric role HRM plays within organizations as AI becomes more integrated in the workplace. Spanning three distinct phases of AI-HRM integration (technocratic, integrated, and fully-embedded), it examines the technical, human, and ethical challenges at each phase and provides suggestions on how to overcome them using a human-centric approach. Our paper highlights the importance of the evolving role of HRM in the AI-driven organization and provides a roadmap on how to bring humans and machines closer together in the workplace." (Author's abstract, IAB-Doku) ((en))

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Frank, Morgan, Yong-Yeol Ahn & Esteban Moro (2023): AI exposure predicts unemployment risk. (arXiv papers), 35 S.

Abstract

"Is artificial intelligence (AI) disrupting jobs and creating unemployment? Despite many attempts to quantify occupations' exposure to AI, inconsistent validation obfuscates the relative benefits of each approach. A lack of disaggregated labor outcome data, including unemployment data, further exacerbates the issue. Here, we assess which models of AI exposure predict job separations and unemployment risk using new occupation-level unemployment data by occupation from each US state's unemployment insurance office spanning 2010 through 2020. Although these AI exposure scores have been used by governments and industry, we find that individual AI exposure models are not predictive of unemployment rates, unemployment risk, or job separation rates. However, an ensemble of those models exhibits substantial predictive power suggesting that competing models may capture different aspects of AI exposure that collectively account for AI's variable impact across occupations, regions, and time. Our results also call for dynamic, context-aware, and validated methods for assessing AI exposure." (Author's abstract, IAB-Doku) ((en))

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Fregin, Marie-Christine & Michael Stops (2023): Wer mit KI-Technologien erfolgreich sein will, sollte die Wirkungen valide abschätzen können. In: Ifo-Schnelldienst, Jg. 76, H. 8, S. 12-15., 2023-08-16.

Abstract

"Marie-Christine Fregin, Universität Maastricht, und Michael Stops, Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg, zeigen, dass die KI bisher insgesamt wenig quantitative Beschäftigungseffekte verursacht hat, da KI-Systeme in der deutschen Wirtschaft noch recht wenig verbreitet sind. Zudem müssten Beschäftigte bei der Einführung neuer Systeme oftmals neue Tätigkeiten ausführen und teilweise erlernen; andererseits sei erwartbar, dass bestimmte Tätigkeiten, die bisher den Beschäftigten vorbehalten waren, von der KI unterstützt und manchmal sogar übernommen werden könnten. Unternehmen sollten wissen, wie der Erfolg einer Technologieeinführung zu messen sei." (Textauszug, IAB-Doku)

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Fregin, Marie-Christine, Theresa Koch, Verena Malfertheiner, Pelin Özgül & Michael Stops (2023): Automatisierungspotenziale von beruflichen Tätigkeiten: Künstliche Intelligenz und Software – Beschäftigte sind unterschiedlich betroffen. (IAB-Kurzbericht 21/2023), Nürnberg, 8 S. DOI:10.48720/IAB.KB.2321

Abstract

"Künstliche Intelligenz (KI) und Software-Systeme ohne KI (Software) können die Ausübung verschiedenster Tätigkeiten beeinflussen. So könnten Tätigkeiten von Hochqualifizierten teilweise von KI übernommen werden, während ein Teil der Tätigkeiten in Berufen mit mittleren oder geringen Qualifikationsanforderungen eher durch den Einsatz von Software betroffen sein könnte. Ganze Berufe mit ihren vielfältigen Tätigkeiten können die Technologien aber nicht übernehmen - auch nicht dort, wo Fachkräfte dringend benötigt werden." (Autorenreferat, IAB-Doku)

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Fung, Esabella (2023): A machine learning approach for assessing labor supply to the online labor market. (MPRA paper / University Library of Munich 118844), München, 28 S.

Abstract

"The online labor market, comprised of companies such as Upwork, Amazon Mechanical Turk, and their freelancer workforce, has expanded worldwide over the past 15 years and has changed the labor market landscape. Although qualitative studies have been done to identify factors related to the global supply to the online labor market, few data modeling studies have been conducted to quantify the importance of these factors in this area. This study applied tree-based supervised learning techniques, decision tree regression, random forest, and gradient boosting, to systematically evaluate the online labor supply with 70 features related to climate, population, economics, education, health, language, and technology adoption. To provide machine learning explainability, SHAP, based on the Shapley values, was introduced to identify features with high marginal contributions. The top 5 contributing features indicate the tight integration of technology adoption, language, and human migration patterns with the online labor market supply." (Author's abstract, IAB-Doku) ((en))

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Garnitz, Johanna & Daria Schaller (2023): ChatGPT, Chatbots und mehr – wie wird künstliche Intelligenz in den HR-Abteilungen von Unternehmen genutzt? In: Ifo-Schnelldienst, Jg. 76, H. 9, S. 65-68.

Abstract

"Das ifo Institut befragt im Auftrag von Randstad Deutschland quartalsweise deutsche HR-Abteilungen zu personalpolitisch relevanten Themen. Das aktuelle Schwerpunktthema befasst sich mit dem Einsatz von Künstlicher Intelligenz, insbesondere in den HR-Abteilungen. Derzeit nutzen ca. 5% der befragten Unternehmen Künstliche Intelligenz im HR-Bereich, geplant haben dies weitere 25% der Unternehmen. Ein Viertel der Unternehmen ergreift Maßnahmen für den (geplanten) Einsatz von KI, und zwar am häufigsten in Form von Arbeits- und Expertengruppen (53%), gefolgt von Fortbildungen (43%). 86% der Teilnehmenden sind hinsichtlich des Einsatzes von KI skeptisch. Trotzdem sehen sie Potenzial für KI im Personalbereich, besonders im Bereich der Automatisierung von Personalprozessen, in der Rekrutierung und im Bewerbermanagement." (Autorenreferat, IAB-Doku)

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Geuss, Raymond (2023): Über die Arbeit. Ein Essay. Hamburg: Hamburger Edition, 198 S.

Abstract

"Ende der 1980er Jahre schloss nördlich von Philadelphia das Stahlwerk seine Tore, in dem Raymond Geuss’ Vater lange Zeit gearbeitet hatte. Sein Onkel, ein Landwirt in Indiana, brauchte bald einen zweiten Job, um seinen Lebensunterhalt bestreiten zu können. Auch anhand seiner Familiengeschichte zeigt der Philosoph in seinem neuen Buch, dass Arbeit, wie wir sie in westlichen Gesellschaften kannten, verschwindet. Automatisierung und Outsourcing haben einen tiefgreifenden gesellschaftlichen Wandel in Gang gesetzt, und Geuss führt seine Leserinnen und Leser durch diese Umbrüche bis zur die Gegenwart dominierenden Amazonisierung. Was ist Arbeit? Wie ist sie organisiert? Und wie wird Arbeit in Zukunft aussehen? In seinem hellsichtigen Essay verbindet Raymond Geuss philosophische Überlegungen mit ökonomischen und historischen Reflexionen. Auch mit der Arbeitsethik und dem Unbehagen an der Arbeit befasst er sich, das so alt ist wie die Arbeit selbst. Wir sollten uns, so Geuss, von den Pathologien unendlichen Wachstums befreien. Das bedeutet auch, Arbeit endlich nicht mehr als Konzept stetig steigender menschlicher Produktivkraft und Anstrengung zu begreifen." (Verlagsangaben, IAB-Doku)

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Giuntella, Osea, Johannes Koenig & Luca Stella (2023): Artificial Intelligence and Workers' Well-Being. (IZA discussion paper / Forschungsinstitut zur Zukunft der Arbeit 16485), Bonn, 43 S.

Abstract

"This study explores the relationship between artificial intelligence (AI) and workers' well-being and mental health using longitudinal survey data from Germany (2000-2020). We construct a measure of individual exposure to AI technology based on the occupation in which workers in our sample were first employed and explore an event study design and a difference-in-differences approach to compare AI-exposed and non-exposed workers. Before AI became widely available, there is no evidence of differential pre-trends in workers' well-being and concerns about their economic futures. Since 2015, however, with the increasing adoption of AI in firms across Germany, we find that AI-exposed workers have become less satisfied with their life and job and more concerned about job security and their personal economic situation. However, we find no evidence of a significant impact of AI on workers' mental health, anxiety, or depression." (Author's abstract, IAB-Doku) ((en))

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Gmyrek, Pawel, Janine Berg & David Bescond (2023): Generative AI and Jobs: a global analysis of potential effects on job quantity and quality. (ILO working paper / International Labour Organization 96), Geneva, 51 S. DOI:10.54394/fhem8239

Abstract

"This study assesses the potential global exposure of occupations to Generative AI, particularly GPT-4. It predicts that the overwhelming effect of the technology will be to augment occupations, rather than to automate them. The greatest impact is likely to be in high and upper-middle income countries due to a higher share of employment in clerical occupations. As clerical jobs are an important source of female employment, the effects are highly gendered. Insights from this study underline the need for proactive policies that focus on job quality, ensure fair transitions, and that are based on dialogue and adequate regulation." (Author's abstract, IAB-Doku) ((en))

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Goller, Daniel, Christian Gschwendt & Stefan C. Wolter (2023): "This Time It's Different" - Generative Artificial Intelligence and Occupational Choice. (IZA discussion paper / Forschungsinstitut zur Zukunft der Arbeit 16638), Bonn, 23 S.

Abstract

"In this paper, we show the causal influence of the launch of generative AI in the form of ChatGPT on the search behavior of young people for apprenticeship vacancies. There is a strong and long-lasting decline in the intensity of searches for vacancies, which suggests great uncertainty among the affected cohort. Analyses based on the classification of occupations according to tasks, type of cognitive requirements, and the expected risk of automation to date show significant differences in the extent to which specific occupations are affected. Occupations with a high proportion of cognitive tasks, with high demands on language skills, and those whose automation risk had previously been assessed by experts as lower are significantly more affected by the decline. However, no differences can be found with regard to the proportion of routine vs. non-routine tasks." (Author's abstract, IAB-Doku) ((en))

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González Ehlinger, Eugenia & Fabian Stephany (2023): Skills or Degree? The Rise of Skill-Based Hiring for AI and Green Jobs. (CESifo working paper 10817), München, 37 S.

Abstract

"For emerging professions, such as jobs in the field of Artificial Intelligence (AI) or sustainability (green), labor supply does not meet industry demand. In this scenario of labor shortages, our work aims to understand whether employers have started focusing on individual skills rather than on formal qualifications in their recruiting. By analyzing a large time series dataset of around one million online job vacancies between 2019 and 2022 from the UK and drawing on diverse literature on technological change and labor market signalling, we provide evidence that employers have started so-called “skill-based hiring” for AI and green roles, as more flexible hiring practices allow them to increase the available talent pool. In our observation period the demand for AI roles grew twice as much as average labor demand. At the same time, the mention of university education for AI roles declined by 23%, while AI roles advertise five times as many skills as job postings on average. Our analysis also shows that university degrees no longer show an educational premium for AI roles, while for green positions the educational premium persists. In contrast, AI skills have a wage premium of 16%, similar to having a PhD (17%). Our work recommends making use of alternative skill building formats such as apprenticeships, on-the-job training, MOOCs, vocational education and training, micro-certificates, and online bootcamps to use human capital to its full potential and to tackle talent shortages." (Author's abstract, IAB-Doku) ((en))

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Green, Andrew & Lucas Lamby (2023): The supply, demand and characteristics of the AI workforce across OECD countries. (OECD social, employment and migration working papers 287), Paris, 55 S. DOI:10.1787/bb17314a-en

Abstract

"This report provides representative, cross-country estimates of the artificial intelligence (AI) workforce across OECD countries. The AI workforce is defined as the subset of workers with skills in statistics, computer science and machine learning who could actively develop and maintain AI systems. For countries that wish to be at the forefront of AI development, understanding the AI workforce is crucial to building and nurturing a talent pipeline, and ensuring that those who create AI reflect the diversity of society. This report uses data from online job vacancies to measure the within-occupation intensity of AI skill demand. The within-occupation AI intensity is then weighted to employment by occupation in labour force surveys to provide estimates of the size and growth of the AI workforce over time." (Author's abstract, IAB-Doku) ((en))

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Guarascio, Dario, Jelena Reljic & Roman Stöllinger (2023): Artificial Intelligence and Employment: A Look into the Crystal Ball. (GLO discussion paper / Global Labor Organization 1333), Essen, 28 S.

Abstract

"This study provides evidence of the employment impact of AI exposure in European regions, addressing one of the many gaps in the emerging literature on AI's effects on employment in Europe. Building upon the occupation-based AI-exposure indicators proposed by Felten et al. (2018, 2019, 2021), which are mapped to the European occupational classification (ISCO), following Albanesi et al. (2023), we analyse the regional employment dynamics between 2011 and 2018. After controlling for a wide range of supply and demand factors, our findings indicate that, on average, AI exposure has a positive impact on regional employment. Put differently, European regions characterised by a relatively larger share of AI-exposed occupations display, all else being equal and once potential endogeneity concerns are mitigated, a more favourable employment tendency over the period 2011-2018. We also find evidence of a moderating effect of robot density on the AI-employment nexus, which however lacks a causal underpinning." (Author's abstract, IAB-Doku) ((en))