Veränderungen der Arbeitswelt durch Künstliche Intelligenz
Anwendungsmöglichkeiten und Auswirkungen des Einsatzes künstlicher Intelligenz auf den Arbeitsmarkt werden breit diskutiert. Welche Folgen für Beschäftigung, Löhne und Qualifikationsanforderungen sind zu erwarten? Birgt die Nutzung automatisierter Entscheidungssysteme (z.B. für die Personalauswahl) ein Diskriminierungsrisiko? Wie wirkt sich der Einsatz von künstlicher Intelligenz auf die Arbeitsqualität aus?
Dieses Themendossier stellt Literatur zum Stand der Forschung zusammen.
Im Filter „Autorenschaft“ können Sie auf IAB-(Mit-)Autorenschaft eingrenzen.
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Literaturhinweis
Technological Change and the Upskilling of European Workers (2025)
Zitatform
McGuinness, Seamus, Paul Redmond, Konstantinos Pouliakas, Lorcan Kelly & Luke Brosnan (2025): Technological Change and the Upskilling of European Workers. (IZA discussion paper / Forschungsinstitut zur Zukunft der Arbeit 17753), Bonn, 22 S.
Abstract
"Using the second wave of the European Skills and Jobs survey, this paper measures the relationship between technological change that automates or augments workers' job tasks and their participation in work-related training. We find that 58 per cent of European employees experienced no change in the need to learn new technologies in their jobs during the 2020-21 period. Of those exposed to new digital technology, 14 per cent did not experience any change in job tasks, 10 per cent reported that new tasks had been created while 5 per cent only saw some of their tasks being displaced by new technology. The remaining 13 per cent simultaneously experienced both task displacement and task creation. Our analysis shows that employees in jobs impacted by new digital technologies are more likely to have to react to unpredictable situations, thus demonstrating a positive link between technologically driven task disruption and job complexity. We show a strong linear relationship between technologically driven job task disruption and the need for job-related training, with training requirements increasing the greater the impact of new technologies on task content." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
AI innovation and the labor share in European regions (2025)
Zitatform
Minniti, Antonio, Klaus Prettner & Francesco Venturini (2025): AI innovation and the labor share in European regions. In: European Economic Review, Jg. 177. DOI:10.1016/j.euroecorev.2025.105043
Abstract
"This paper examines how the development of Artificial Intelligence (AI) affects the distribution of income between capital and labor, and how these shifts contribute to regional income inequality. To investigate this issue, we analyze data from European regions dating back to 2000. We find that for every doubling of regional AI innovation, the labor share declines by 0.5% to 1.6%, potentially reducing it by 0.09 to 0.31 percentage points from an average of 52%, solely due to AI. This new technology has a particularly negative impact on high- and medium-skill workers, primarily through wage compression, while for low-skill workers, employment expansion induced by AI mildly offsets the associated wage decline. The effect of AI is not driven by other factors influencing regional development in Europe or by the concentration of the AI market." (Author's abstract, IAB-Doku, © 2025 The Authors. Published by Elsevier B.V.) ((en))
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Literaturhinweis
Are artificial intelligence skills a reward or a gamble? Deconstructing the AI wage premium in Europe (2025)
Zitatform
Pouliakas, Konstantinos, Giulia Santangelo & Paul Dupire (2025): Are artificial intelligence skills a reward or a gamble? Deconstructing the AI wage premium in Europe. In: Eurasian business review, S. 1-38. DOI:10.1007/s40821-025-00302-0
Abstract
"Understanding the labor market impact of new, autonomous digital technologies, particularly generative or other forms of artificial intelligence (AI), is currently at the top of the research and policy agenda. Many initial studies, though not all, have shown that there is a wage premium to mostly technical AI skills in labor markets. Such evidence tends to draw on data from web-based sources and typically fails to provide insight into the mechanisms underlying the AI wage gap. This paper utilizes representative adult workforce data from 29 European countries, the second European skills and jobs survey, to examine wage differentials of the AI programmer workforce. The latter is uniquely identified as part of the workforce that writes computer programs using AI algorithms. The analysis shows that, on average, AI programmers enjoy a significant wage premium relative to a comparably educated or skilled workforce, such as programmers who do not yet write code using AI at work. Wage decomposition analysis further illustrates that there is a large unexplained component of such wage differential. Part of AI programmers’ larger wage variability can however be attributed to higher job-skill requirements, a propensity for remote work and a greater performance-based component in wage schedules. This indicates differences in the job design and performance management of the AI workforce." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Sociotechnical imaginaries of social inequality in the design and use of AI recruitment technology (2025)
Zitatform
Sartori, Laura & Clementine Collett (2025): Sociotechnical imaginaries of social inequality in the design and use of AI recruitment technology. In: European Societies, S. 1-34. DOI:10.1162/euso_a_00035
Abstract
"Through interviewing 12 companies in Italy which either design (vendors) or use (clients) AI recruitment technology systems, we explore how these companies perceive their systems to interact with issues of social inequality and how these perceptions, in practice, carry societal impacts. Three sociotechnical imaginaries (Jasanoff and Kim, 2015) were consistently embedded within these companies’ visions of this intersection: the third eye, the river, and the car bonnet. Through critically analyzing these imaginaries, we find that they exhibit an overriding desire for productivity and talent capture from clients, and a consequential de-prioritization of addressing social inequality and scrutinizing the ways it could be reproduced from both vendors and clients. It demonstrates that the current ‘desired’ futures, shown by the sociotechnical imaginaries which vendors and clients share for AI-tec-tech are really leading us towards an ‘undesirable’ future of hiring which continues to perpetuate social inequality. This study contributes one of the first pieces of empirical work to simultaneously assess the perceptions of AI-rec-tech vendors ’ and clients’ surrounding social inequality, to shed light on the priorities for design and the motivations for usage, and to reflect upon how this impacts society. This is a significant and original contribution to the evolving body of literature on AI-rec-tech in sociology, critical data studies, and communications." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
KI-Nähe im Job zahlt sich aus (2025)
Seele, Stefanie; Stettes, Oliver;Zitatform
Seele, Stefanie & Oliver Stettes (2025): KI-Nähe im Job zahlt sich aus. (IW-Kurzberichte / Institut der Deutschen Wirtschaft Köln 2025,45), Köln, 3 S.
Abstract
"Beschäftigte, deren Aufgaben eine Nähe zu den Anwendungspotenzialen von Künstlicher Intelligenz (KI) aufweisen, erhalten höhere Tagesentgelte als Beschäftigte in KI-fernen Tätigkeiten. Sie wechseln zudem seltener den Betrieb und haben seltener längere Arbeitslosigkeitsperioden. Die Sorge vor einer zunehmenden Verbreitung von KI im Arbeitsalltag scheint bisher unbegründet." (Autorenreferat, IAB-Doku)
Weiterführende Informationen
Data product DOI: 10.5164/IAB.SIAB7521.de.en.v1 -
Literaturhinweis
Innovation and employment in the era of artificial intelligence: In the face of AI revolution, concerns about possible technological unemployment should be aware of the complex and mixed employment impacts of technological change. (2025)
Zitatform
Vivarelli, Marco & Guillermo Arenas Diaz (2025): Innovation and employment in the era of artificial intelligence. In the face of AI revolution, concerns about possible technological unemployment should be aware of the complex and mixed employment impacts of technological change. (IZA world of labor 154,2), Bonn, o. S. DOI:10.15185/izawol.154.v2
Abstract
"The relationship between technology and employment has always been a source of concern, at least since the first industrial revolution. However, while process innovation can be job-destroying (provided that its direct labor-saving effect is not compensated through market mechanisms), product innovation can imply the emergence of new firms, new sectors, and thus new jobs (provided that its welfare effect dominates the crowding out of old products). Nowadays, the topic is even more relevant because the world economy is undergoing a new technological revolution centred on automation and the diffusion of Artificial Intelligence (AI)." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Wenn Ihr Job von KI betroffen ist, kann das eine große Chance sein: Gastbeitrag (2025)
Zitatform
Weber, Enzo (2025): Wenn Ihr Job von KI betroffen ist, kann das eine große Chance sein. Gastbeitrag. In: Frankfurter Allgemeine Zeitung H. 12.02.2025 Frankfurt am Main.
Abstract
"Künstliche Intelligenz ersetzt immer mehr Arbeitsplätze. Sie dringt in mehr und mehr Tätigkeitsbereiche vor. Mittlerweile sind auch viele hoch qualifizierte Jobs betroffen – Ärzte, Rechtsanwältinnen, Journalisten und viele andere. Wir müssen uns diesem Wandel anpassen." (Autorenreferat, IAB-Doku, © Frankfurter Allgemeine Zeitung)
Weiterführende Informationen
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Literaturhinweis
Does automation replace experts or augment expertise? The answer is yes (Interview) (2025)
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Winters, Jutta & Jonathan P. Latner; David Autor (interviewte Person) (2025): Does automation replace experts or augment expertise? The answer is yes (Interview). In: IAB-Forum H. 09.01.2025. DOI:10.48720/IAB.FOO.20250109.01
Abstract
"David Autor, Professor of economics at the Massachusetts Institute of Technology (MIT), gives a Special Lecture at the IAB on 15 January 2025. In this accompanying interview, he discusses the impact of Artificial Intelligence on wages and employment, outlines the crucial role of expertise and gives insights on policy-approaches for supporting workers in rapidly changing labor markets." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Erprobung neuer Methoden der Mustererkennung bei der Statistik der BA: Vorhaben im Rahmen des BMAS-Datenlabors (2025)
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(2025): Erprobung neuer Methoden der Mustererkennung bei der Statistik der BA. Vorhaben im Rahmen des BMAS-Datenlabors. (Grundlagen: Methodenbericht / Bundesagentur für Arbeit), Nürnberg, 81 S.
Abstract
"Die Statistik der BA betrachtet Methoden der Künstlichen Intelligenz, insbesondere des Maschinellen Lernens, als sinnvolle Ergänzung ihres Methodenspektrums zur Weiterentwicklung in konkreten Aufgabenbereichen. Einsatzfelder bestehen vor allem dort, wo lernfähige Algorithmen die automatische Verarbeitung großer Datenmengen mit dem Ziel der Mustererkennung ermöglichen. Vielversprechend erscheint der Einsatz dieser Methoden unter anderem in zwei Bereichen: Bei der Unterstützung der bestehenden Validierung von Daten im Zuge der Stichtagsverarbeitung sowie bei der Zuordnung von Beschäftigungsbetrieben zu Wirtschaftszweigen. Machbarkeitsanalysen für beide Anwendungen wurden durch das BMAS-Datenlabor gefördert und aus Mitteln der Europäischen Union „NextGenerationEU“, die im Rahmen des Deutschen Aufbau- und Resilienzplans (DARP) bereitgestellt wurden, finanziert. Das entsprechende Vorhaben der Statistik der BA, das „Datenlabor Statistik“, hatte eine Laufzeit von November 2022 bis Dezember 2024. Die beiden genannten Einsatzfelder bildeten zwei Module eines Gesamtvorhabens. Im Modul „Validierung“ wurden neue Methoden zur Validierung großer Mengen statistischer Daten konzipiert, exemplarisch eingesetzt und bewertet, mit deren Hilfe sich im Vergleich zum aktuellen Verfahren erheblich mehr Daten hinsichtlich auffälliger Konstellationen prüfen lassen. Die als erfolgversprechend bewerteten neuen Ansätze adressieren bisher weniger beachtete Bereiche der Plausibilitätsprüfung: fachstatistikübergreifende Analysen, die Berücksichtigung von Merkmalskombinationen sowie Analysen auf tieferer regionaler Ebene. Ein Ansatz für die beiden erstgenannten Bereiche wurde unmittelbar nach dem Datenlabor in einen testweisen Produktivbetrieb und in weitere Analysen überführt. Die geprüften Ansätze zur Erkennung fehlerhafter schleichender Veränderungen sowie für Einzelfallprüfungen auf tieferen Schichten der Verarbeitung haben sich als weniger aussichtsreich herausgestellt; hier sind weitere Analysen nötig. Im Modul „Wirtschaftszweige“ wurde vor allem ein Lösungsansatz als erfolgversprechend und praktisch umsetzbar bewertet: Die Zuordnung von Beschäftigungsbetrieben zu Wirtschaftszweigen auf Basis eines speziell trainierten großen Sprachmodells, das Freitext-Beschreibungen zu Betriebszwecken verarbeitet. Dieser Ansatz soll nun in der Praxis umgesetzt werden. Wertvolle Erkenntnisse wurden in diesem Modul zudem für Lösungsansätze auf Basis der folgenden Datenquellen gewonnen: Stellenanzeigen, Betriebsbezeichnungen und Informationen zu den Tätigkeiten von Beschäftigten. Zu den übergreifenden Ergebnissen des Datenlabors der Statistik zählen der Wissensgewinn durch methodischen Austausch innerhalb und außerhalb der BA, das Erschließen bisher nicht genutzter technischer Architekturen sowie leistungsfähiger Hardware und Entwicklungswerkzeuge." (Autorenreferat, IAB-Doku)
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Literaturhinweis
D21-Digital-Index 2024/2025: Jährliches Lagebild zur Digitalen Gesellschaft (2025)
Zitatform
(2025): D21-Digital-Index 2024/2025. Jährliches Lagebild zur Digitalen Gesellschaft. (D21-Digital-Index), Berlin, 41 S.
Abstract
"Der D21-Digital-Index ist Deutschlands wichtigstes Lagebild zur Digitalisierung der Gesellschaft. Er zeigt, wie tief die digitale Transformation verschiedene Lebensbereiche durchdringt und wie gut Bürger*innen mit den Anforderungen des Wandels umgehen können. Gleichzeitig offenbart er Spaltungen und Herausforderungen: Wer profitiert, wer droht abgehängt zu werden? Der D21-Digital-Index ist mehr als eine Analyse: Er ist Basis für wirkungsvolles Handeln von Politik, Wirtschaft und Gesellschaft, um die Resilienz im digitalen Wandel zu stärken und gemeinsam eine inklusive digitale Zukunft zu gestalten, in der alle von den Chancen der Digitalisierung profitieren. Im Fokus steht in diesem Jahr der digitale Wandel als Treiber für weitere Transformationsprozesse, etwa bei der Wertschöpfung, beim Informations- und Kommunikationsverhalten oder beim ökologischen Wandel. Denn unsere Gesellschaft befindet sich inmitten eines tiefgreifenden Wandels. Der D21-Digital-Index begleitet sie in dieser Transformation seit vielen Jahren. Ein weiterer Schwerpunkt der Studie liegt auf der digitalen Bildung und den Kompetenzen, die für eine souveräne und kritische Nutzung digitaler Technologien erforderlich sind. Zudem wird untersucht, wie die Bevölkerung den Einsatz Künstlicher Intelligenz wahrnimmt: Welche Erwartungen, Chancen und Bedenken gibt es? Welche Faktoren beeinflussen die Akzeptanz neuer KI-Technologien im Alltag? Wie steht es um das Bewusstsein für die Transformationskraft von KI, insbesondere für die Beschäftigungschancen?" (Autorenreferat, IAB-Doku)
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Literaturhinweis
Learning from Ricardo and Thompson: Machinery and Labor in the Early Industrial Revolution, and in the Age of AI (2024)
Zitatform
Acemoglu, Daron & Simon Johnson (2024): Learning from Ricardo and Thompson: Machinery and Labor in the Early Industrial Revolution, and in the Age of AI. (NBER working paper / National Bureau of Economic Research 32416), Cambridge, Mass, 45 S. DOI:10.3386/w32416
Abstract
"David Ricardo initially believed machinery would help workers but revised his opinion, likely based on the impact of automation in the textile industry. Despite cotton textiles becoming one of the largest sectors in the British economy, real wages for cotton weavers did not rise for decades. As E.P. Thompson emphasized, automation forced workers into unhealthy factories with close surveillance and little autonomy. Automation can increase wages, but only when accompanied by new tasks that raise the marginal productivity of labor and/or when there is sufficient additional hiring in complementary sectors. Wages are unlikely to rise when workers cannot push for their share of productivity growth. Today, artificial intelligence may boost average productivity, but it also may replace many workers while degrading job quality for those who remain employed. As in Ricardo's time, the impact of automation on workers today is more complex than an automatic linkage from higher productivity to better wages." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Artificial Intelligence and the health workforce: Perspectives from medical associations on AI in health (2024)
Almyranti, Margarita; Eiszele, Samuel; Sutherland, Eric; Ash, Nachman;Zitatform
Almyranti, Margarita, Eric Sutherland, Nachman Ash & Samuel Eiszele (2024): Artificial Intelligence and the health workforce. Perspectives from medical associations on AI in health. (OECD Artificial Intelligence Papers 28), Paris, 53 S. DOI:10.1787/9a31d8af-en
Abstract
"Healthcare has progressed through advancements in medicine, leading to improved global life expectancy. Nevertheless, the sector grapples with increasing challenges such as heightened demand, soaring costs, and an overburdened workforce. Factors contributing to health workforce strain include ageing populations, increasing burden from non-communicable and chronic diseases, healthcare providers' burnout, and evolving patient expectations. Artificial Intelligence (AI) could potentially transform healthcare by alleviating some of these pressures. But AI in health poses risks to health providers through potential workforce disruption – with changing roles requiring adapted skills with some functions subject to automation. Striking a balance between innovation and safeguards is imperative." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
KI für die Fachkräftesicherung nutzen: Lösungsansätze für Automatisierung, Teilhabe und Wissenstransfer (2024)
André, Elisabeth; Suchy, Oliver; Steil, Jochen; Bittner, Eva; Wilkens, Uta ; Heister, Michael; Bullinger-Hoffmann, Angelika; Huchler, Norbert ; Schmidt, Christoph M.; Peissner, Matthias; Stich, Andrea; Prasuhn, Pierre;Zitatform
André, Elisabeth, Angelika Bullinger-Hoffmann, Eva Bittner, Michael Heister, Norbert Huchler, Matthias Peissner, Pierre Prasuhn, Christoph M. Schmidt, Jochen Steil, Andrea Stich, Oliver Suchy & Uta Wilkens (2024): KI für die Fachkräftesicherung nutzen. Lösungsansätze für Automatisierung, Teilhabe und Wissenstransfer. München, 41 S. DOI:10.48669/pls_2024-2
Abstract
"Ob im Handwerk, Medizin oder der Verwaltung – in fast allen Branchen kommen Fachkräfteengpässe auf uns zu oder sind bereits spürbar. Zunehmend verstärkt durch demografische Entwicklungen wie den bevorstehenden Renteneintritt der Babyboomer. Um die Fachkräftebasis von morgen zu sichern, kann auch Künstliche Intelligenz (KI) als technologischer Baustein – vor allem in den Bereichen Automatisierung und KI-basierte Assistenz – eine bedeutende Rolle spielen. Das Whitepaper gibt einen Überblick, wie KI-Technologien gezielt zur Fachkräftesicherung beitragen können, um die Wettbewerbsfähigkeit zu stärken und Arbeitsplätze in Deutschland zu sichern." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Artificial Intelligence – Gender-Specific Differences in Perception, Understanding, and Training Interest (2024)
Zitatform
Armutat, Sascha, Malte Wattenberg & Nina Mauritz (2024): Artificial Intelligence – Gender-Specific Differences in Perception, Understanding, and Training Interest. In: C.-P. Marti Ballester (ed.) (2024): Proceedings of the 7th International Conference on Gender Research, S. 36-43. DOI:10.34190/icgr.7.1.2163
Abstract
"In light of the growing importance of Artificial Intelligence (AI) in science, business, and society, broad acceptance is crucial. However, recent studies indicate a significant underrepresentation of women in the emerging AI-driven professions of the future job market. This hampers the innovation potential of technologies due to the lack of diverse perspectives in development. Gender-specific differences also manifest in the perception of AI: Men tend to view AI applications more positively, rate their own AI competencies higher, and have more trust in the technology compared to women. However, both genders agree on the critical importance of the comprehensibility of AI decisions and are equally willing to pursue further education in the field of AI. This study aimed to investigate gender-relevant aspects in the perception and understanding of AI, as well as the need for further education and opportunities for communication and exchange on the topic of AI. To achieve this, focus groups with female students were conducted in May 2023. The analysis of the conversation data and materials used was carried out using an inductive coding method. Overall, women perceive knowledge as the key to generating more interest in AI. However, they also identify obstacles such as discrimination, gender stereotypes, and a lack of gender equality. Additionally, they desire more practical examples, improved communication regarding the advantages and disadvantages of AI, as well as more democratic and transparent decision-making processes. The paper emphasizes that an inclusive educational environment requires awareness and education for women, along with measures against discriminatory barriers and stereotypes. Furthermore, it suggests the early involvement of women in the development of AI applications and the establishment of clear rules to ensure gender equality in the workplace. These study findings provide valuable support to companies in the gender-specific planning of awareness and training processes for introducing AI." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech (2024)
Zitatform
Avery, Mallory, Andreas Leibbrandt & Joseph Vecci (2024): Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech. (CESifo working paper 10996), München, 70 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 recruitment tools can impact 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 anticipated bias is a driver of increased female application completion 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))
Ähnliche Treffer
auch erschienen als: Monash Economics Working Papers, 2023-09 -
Literaturhinweis
The impact of Robots on Labour market transitions in Europe (2024)
Zitatform
Bachmann, Ronald, Myrielle Gonschor, Piotr Lewandowski & Karol Madoń (2024): The impact of Robots on Labour market transitions in Europe. In: Structural Change and Economic Dynamics, Jg. 70, S. 422-441. DOI:10.1016/j.strueco.2024.05.005
Abstract
"Dieses Papier untersucht die Auswirkungen von Robotern auf Arbeitsmarkttransitionen in 16 europäischen Ländern. Generell reduzieren Roboter Übergänge von der Beschäftigung in die Arbeitslosigkeit und erhöhen die Wahrscheinlichkeit, einen neuen Job zu finden. Arbeitskosten sind eine wichtige Erklärung für die beobachteten Unterschiede zwischen Ländern: In Ländern mit niedrigeren Arbeitskosten zeigt sich ein stärkerer Effekt auf Einstellungen und Trennungen. Diese Auswirkungen sind bei Arbeitskräften in Berufen mit manuellen oder kognitiven Routineaufgaben besonders ausgeprägt, bei Berufen mit nicht-routine kognitiven Aufgaben hingegen vernachlässigbar. Für junge und ältere Arbeitskräfte in Ländern mit niedrigeren Arbeitskosten wirken sich Roboter positiv auf Übergänge aus. Unsere Ergebnisse deuten darauf hin, dass die Einführung von Robotern in den meisten europäischen Ländern zu einem Anstieg der Beschäftigung und einem Rückgang der Arbeitslosigkeit geführt hat, vor allem durch einen Rückgang der Übergänge in die Arbeitslosigkeit." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Navigating career stages in the age of artificial intelligence: A systematic interdisciplinary review and agenda for future research (2024)
Bankins, Sarah ; Jooss, Stefan ; Restubog, Simon Lloyd D. ; Ocampo, Anna Carmella ; Shoss, Mindy; Marrone, Mauricio ;Zitatform
Bankins, Sarah, Stefan Jooss, Simon Lloyd D. Restubog, Mauricio Marrone, Anna Carmella Ocampo & Mindy Shoss (2024): Navigating career stages in the age of artificial intelligence: A systematic interdisciplinary review and agenda for future research. In: Journal of vocational behavior, Jg. 153. DOI:10.1016/j.jvb.2024.104011
Abstract
"As artificial intelligence (AI) use expands within organizations, its influence is increasingly permeating careers and vocational domains. However, there is a notable lack of structured insights regarding AI's role in shaping individual career paths across career stages. To address this gap, we undertook a systematic literature review of 104 empirical articles, aiming to synthesize the scholarship on AI in the context of careers. Drawing upon career stage theory, we examine the implications of AI on careers, identify key barriers and enablers of AI use in this area, and reveal how the utilization of AI impacts individuals' career competencies. In doing so, we illustrate how AI actively shapes individuals' career trajectories and we dissect these effects both within and across various career stages to situate AI within the broader context of careers research. Adopting a sustainable career lens, we conclude by outlining a future research agenda that advocates for the design and adoption of AI systems that promote sustainable and equitable careers." (Author's abstract, IAB-Doku, © 2024 Elsevier) ((en))
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Literaturhinweis
AI, Automation and Taxation (2024)
Zitatform
Bastani, Spencer & Daniel Waldenström (2024): AI, Automation and Taxation. (IZA policy paper / Forschungsinstitut zur Zukunft der Arbeit 212), Bonn, 19 S.
Abstract
"This paper examines the implications of Artificial Intelligence (AI) and automation for the taxation of labor and capital in advanced economies. It synthesizes empirical evidence on worker displacement, productivity, and income inequality, as well as theoretical frameworks for optimal taxation. Implications for tax policy are discussed, focusing on the level of capital taxes and the progressivity of labor taxes. While there may be a need to adjust the level of capital taxes and the structure of labor income taxation, there are potential drawbacks of overly progressive taxation and universal basic income schemes that could undermine work incentives, economic growth, and long-term household welfare. Some of the challenges posed by AI and automation may also be better addressed through regulatory measures rather than tax policy." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Robots and firms' labour search: The role of temporary work agencies (2024)
Beneito, Pilar; Wilemme, Guillaume; Vicente-Chirivella, Oscar; Garcia-Vega, Maria;Zitatform
Beneito, Pilar, Maria Garcia-Vega, Oscar Vicente-Chirivella & Guillaume Wilemme (2024): Robots and firms' labour search: The role of temporary work agencies. (Research paper / Nottingham Centre for Research on Globalisation and Economic Policy 2024,02), Nottingham, 55 S.
Abstract
"We study the impact of industrial robots on the use of labor intermediaries or temporary work agencies (TWAs) and firm productivity. We develop a theoretical framework where new technologies increase the need for quality match workers. TWAs help firms to search for workers who better match their technologies. The model predicts that using robots increases TWA use, which increases robots' productivity. We test the model implications with panel data of Spanish firms from 1997 to 2016 with information on robot adoption and TWA use. Using staggered difference-in-difference (DiD) estimations, we estimate the causal effects of robot adoption on TWAs. We find robot adopters increase the probability of TWA use compared to non-adopters. We also find that firms that combine robots with TWAs achieve higher productivity than those who adopt robots without TWAs." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
The Rapid Adoption of Generative AI (2024)
Zitatform
Bick, Alexander, Adam Blandin & David J. Deming (2024): The Rapid Adoption of Generative AI. (NBER working paper / National Bureau of Economic Research 32966), Cambridge, Mass, 44 S. DOI:10.3386/w32966
Abstract
"Generative Artificial Intelligence (AI) is a potentially important new technology, but its impact on the economy depends on the speed and intensity of adoption. This paper reports results from the first nationally representative U.S. survey of generative AI adoption at work and at home. In August 2024, 39 percent of the U.S. population age 18-64 used generative AI. More than 24 percent of workers used it at least once in the week prior to being surveyed, and nearly one in nine used it every workday. Historical data on usage and mass-market product launches suggest that U.S. adoption of generative AI has been faster than adoption of the personal computer and the internet. Generative AI is a general purpose technology, in the sense that it is used in a wide range of occupations and job tasks at work and at home." (Author's abstract, IAB-Doku) ((en))