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on Artificial Intelligence |
| By: | Kevin He (University of Pennsylvania); Ran Shorrer (Pennsylvania State University); Mengjia Xia (University of Pennsylvania) |
| Abstract: | We conduct an incentivized laboratory experiment to study people’s perception of generative artificial intelligence (GenAI) alignment in the context of economic decisionmaking. Using a panel of economic problems spanning the domains of risk, time preference, social preference, and strategic interactions, we ask human subjects to make choices for themselves and to predict the choices made by GenAI on behalf of a human user. We find that people overestimate the degree of alignment between GenAI’s choices and human choices. In every problem, human subjects’ average prediction about GenAI’s choice is substantially closer to the average human-subject choice than it is to the GenAI choice. At the individual level, different subjects’ predictions about GenAI’s choice in a given problem are highly correlated with their own choices in the same problem. We explore the implications of people overestimating GenAI alignment in a simple theoretical model. |
| Date: | 2025–04–06 |
| URL: | https://d.repec.org/n?u=RePEc:pen:papers:25-019 |
| By: | Normann, Hans-Theo; Martin, Simon; Püplichhuisen, Paul; Werner, Tobias |
| JEL: | C73 D43 L13 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:vfsc25:325405 |
| By: | Normann, Hans-Theo; Rulié, Nina; Stypa, Olaf; Werner, Tobias |
| JEL: | C90 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:vfsc25:325417 |
| By: | Walter, Johannes |
| JEL: | D83 D72 C90 C91 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:vfsc25:325453 |
| By: | Boyan Jovanovic; Peter L. Rousseau |
| Abstract: | We model several ways in which AI may improve decisions, raise the productivity of firms, and raise human capital growth. Each focuses on activities that involve problem solving, with solutions being guided by signals. If AI raises the accuracy of the signals, humans will then make better decisions — individually and in groups. |
| JEL: | O32 O33 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34295 |
| By: | Frédéric Marty (Université Côte d'Azur, GREDEG, CNRS, France); Thierry Warin (HEC Montréal; CIRANO, OBVIA, GPAI/CEIMIA) |
| Abstract: | Digital markets are increasingly dominated by entities that leverage technical specificities such as network effects, economies of scale, and scope, as well as significant advantages in data access and critical infrastructure, including computing power and cloud capacities. The advent of generative artificial intelligence (AI) marks a potential inflection point in this landscape. In this context, the primary barriers to entry are no longer merely data and open source foundation models but the availability of large, high-quality datasets and substantial computing power. This paper examines whether these barriers will entrench the dominant positions of Big Tech companies or if they will catalyze a reshuffling of competitive dynamics. By focusing on the dual challenges of data and computing power, this study identifies the key factors that will shape the future competitive landscape of the generative AI industry. This article contributes to the ongoing debate in industrial economics and strategic management regarding the potentially disruptive effects of generative AI on the market power of Big Tech firms. Can this technological shift recalibrate competitive dynamics, or will it ultimately serve to entrench existing power structures? At its core, the article seeks to interrogate a prevailing narrative - namely, the notion that innovation inherently sustains competitive processes, even in the face of short-term lock-in effects. |
| Keywords: | Generative AI, data-based advantage, digital ecosystems, Big Techs |
| JEL: | K21 L12 L13 L41 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2025-38 |
| By: | Alessandra Bonfiglioli; Rosario Crinò; Mattia Filomena; Gino Gancia |
| Abstract: | We study the environmental impact of artificial intelligence (AI) using a novel dataset that links measures of AI penetration, the location of data centers and power plants, and CO2 emissions across US commuting zones between 2002 and 2022. Our analysis yields four main findings. First, exploiting a shift–share identification strategy, we show that localities more exposed to AI experience relatively faster emissions growth. Second, decomposition results indicate that scale effects dominate, while changes in industrial composition exert at most a weak mitigating effect; at the same time, electricity generation becomes more carbon intensive. Third, AI penetration raises dependence on non-renewable electricity. Fourth, proximity to data centers is a key driver of this effect, as nearby power plants shift toward greater fossil fuel use. These findings suggest that, absent a rapid decarbonization of power generation, the diffusion of AI is likely to exacerbate environmental externalities through the energy demand of data centers. |
| Keywords: | artificial intelligence, data centers, environment, emissions, pollution |
| JEL: | O33 Q55 R11 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12158 |
| By: | Maydell, Richard; Firth, John |
| JEL: | O11 E24 I25 O33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:vfsc25:325460 |
| By: | Yusuke Aoki (Indeed. (E-mail: yaoki@indeed.com)); Joon Suk Park (Bank of Korea. (E-mail: parkjs@bok.or.kr)); Yuya Takada (Re Data Science Co., Ltd. and Specially Appointed Researcher, Indeed Recruit Partners Co., Ltd. (E-mail: yuyatakada@redata.co.jp)); Koji Takahashi (Bank of Japan. (E-mail: kouji.takahashi-2@boj.or.jp)) |
| Abstract: | This paper examines the relationship between individuals' expectations of job replacement by generative AI (GenAI) and their macroeconomic outlooks and behaviors. Using online surveys combined with randomized experiments conducted in the U.S. and Japan, we derive the following findings about the effects of expecting greater job replacement due to GenAI. First, in both the U.S. and Japan, respondents revise their beliefs after receiving information about GenAI's job replacement ratios. Second, in Japan, such an expectation leads to an increase in inflation expectations driven by a rise in investment. Third, it increases respondents' willingness to use GenAI in workplaces in Japan. Fourth, in the U.S., expectations of greater job replacement amplify concerns about weaker short-term labor demand and reduced skill requirements, particularly among more educated respondents. In addition, these respondents anticipate lower investment, while less educated respondents expect higher investment. |
| Keywords: | Generative Artificial intelligence, labor market, inflation, productivity. |
| JEL: | E24 E31 O30 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:ime:imedps:25-e-04 |
| By: | Gallego-Moll, Carlos; Carrasco-Ribelles, Lucía A.; Casajuana, Marc; Maynou, Laia; Arocena, Pablo; Violán, Concepción; Zabaleta-Del-Olmo, Edurne |
| Abstract: | Objectives: To broadly map the research landscape to identify trends, gaps, and opportunities in data sets, methodologies, outcomes, and reporting standards for artificial intelligence (AI)-based healthcare utilization prediction. Methods: We conducted a scoping review following the Joanna Briggs Institute methodology. We searched 3 major international databases (from inception to January 2025) for studies applying AI in predictive healthcare utilization. Extracted data were categorized into data sets characteristics, AI methods and performance metrics, predicted outcomes, and adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) + AI reporting guidelines. Results: Among 1116 records, 121 met inclusion criteria. Most were conducted in the United States (62%). No study incorporated all 6 relevant variable groups: demographic, socioeconomic, health status, perceived need, provider characteristics, and prior utilization. Only 7 studies included 5 of these groups. The main data sources were electronic health records (60%) and claims (28%). Ensemble models were the most frequently used (66.9%), whereas deep learning models were less common (16.5%). AI methods were primarily used to predict future events (90.1%), with hospitalizations (57.9%) and visits (33.1%) being the most predicted outcomes. Adherence to general reporting standards was moderate; however, compliance with AI-specific TRIPOD + AI items was limited. Conclusions: Future research should broaden predicted outcomes to include process- and logistics-oriented events, extend applications beyond prediction—such as cohort selection and matching—and explore underused AI methods, including distance-based algorithms and deep neural networks. Strengthening adherence to TRIPOD-AI reporting guidelines is also essential to enhance the reliability and impact of AI in healthcare planning and economic evaluation. |
| Keywords: | artificial intelligence; health economics; healthcare utilisation outcomes; resource allocation; review |
| JEL: | J1 |
| Date: | 2025–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129293 |
| By: | Bauer, Bernhard (Center for Responsible AI Technologies, and University of Augsburg, & Germany); Mühlbauer, Sabrina (Institute for Employment Research (IAB), Nuremberg, Germany); Schlögl-Flierl, Kerstin (Center for Responsible AI Technologies, and University of Augsburg, & Germany); Weber, Enzo (Institute for Employment Research (IAB), Nuremberg, Germany ; University of Regensburg); Ziethmann, Paula Franziska (Institute for Employment Research (IAB), Nuremberg, Germany) |
| Abstract: | "This article addresses the ethical design of artificial intelligence (AI) in the public sector, with a particular focus on Public Employment Services (PES). While AI is increasingly employed to streamline administrative processes and improve service delivery, its application in employment mediation raises fundamental concerns regarding fairness, accountability, and democratic legitimacy. The EU Artificial Intelligence Act has further underscored the urgency of addressing these challenges by classifying employment-related AI systems as high-risk, thereby mandating robust safeguards to prevent discrimination and ensure transparency. The central aim of this study is to examine how ethical and social considerations can be systematically embedded in the development and implementation of public sector AI. Using the German PES as a case study, we introduce the “Embedded Ethics and Social Sciences” approach (EE), which integrates ethical reflection and practitioner involvement from the outset. Qualitative insights from interviews with caseworkers highlight the socio-technical challenges of implementation, particularly the need to reconcile efficiency with citizen trust. Building on these insights, we propose concrete design elements emerging from the integration of ethical and social considerations into system development. In this context, we discuss issues of data ethics and bias, fairness, and the role of explainable AI (XAI). Our analysis demonstrates that this framework not only supports compliance with new regulatory requirements but also strengthens human oversight and agency, and shared decision-making. More broadly, the findings suggest that ethically grounded design can enhance fairness, transparency, and legitimacy across diverse domains of public administration, thereby contributing to more accountable and citizen-centered governance in the digital era." (Author's abstract, IAB-Doku) ((en)) |
| Keywords: | IAB-Open-Access-Publikation |
| JEL: | C49 J14 J16 J64 J71 |
| Date: | 2025–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:iab:iabdpa:202512 |
| By: | Dong, Mengchen; Rahwan, Iyad; Bonnefon, Jean-François |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:130937 |
| By: | Ceballos, Francisco; Chugh, Aditi; Kramer, Berber |
| Abstract: | The rise of artificial intelligence (AI) has heightened interest in digital models to strengthen agricultural extension. Such tools could help provide personalized advisories tailored to a farmer's unique conditions at scale and at a low cost. This study evaluates the fundamental assumption that personalized crop advisories are more effective than generic ones. By means of a large-scale randomized controlled trial (RCT), we assess the impact of personalized picture-based advisories on farmers’ perceptions, knowledge and adoption of recommended inputs and practices, and other downstream outcomes. We find that personalizing advisories does not significantly improve agricultural outcomes compared to generic ones. While farmers who engage relatively more with advisories (i.e., those who receive and read a substantial number of messages based on self-reports) tend to achieve better outcomes, this is irrespective of whether the advisories they receive are tailored to their specific situation or not. We conclude that investments in digital extension tools should aim to enhance engagement with advisories rather than focusing solely on personalization. |
| Keywords: | agricultural extension; artificial intelligence; farmers; inputs; India; Kenya; Asia; Southern Asia; Africa; Eastern Africa |
| Date: | 2024–12–31 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:ifprid:169348 |
| By: | Bojidara Doseva; Catherine Dehon; Antonio Estache |
| Date: | 2025–09–25 |
| URL: | https://d.repec.org/n?u=RePEc:eca:wpaper:2013/394559 |
| By: | Seung Jung Lee; Anne Lundgaard Hansen |
| Abstract: | This paper investigates the impact of the adoption of generative AI on financial stability. We conduct laboratory-style experiments using large language models to replicate classic studies on herd behavior in investment decisions. Our results show that AI agents make more rational decisions than humans, relying predominantly on private information over market trends. Increased reliance on AI-powered investment advice could therefore potentially lead to fewer asset price bubbles arising from animal spirits that trade by following the herd. However, exploring variations in the experimental settings reveals that AI agents can be induced to herd optimally when explicitly guided to make profit-maximizing decisions. While optimal herding improves market discipline, this behavior still carries potential implications for financial stability. In other experimental variations, we show that AI agents are not purely algorithmic, but have inherited some elements of human conditioning and bias. |
| Keywords: | Herd behavior; Large language models; AI-powered traders; Financial markets; Financial stability |
| JEL: | C90 D82 G11 G14 G40 |
| Date: | 2025–09–26 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-90 |
| By: | Matteo Aquilina; Douglas Kiarelly Godoy de Araujo; Gaston Gelos; Taejin Park; Fernando Perez-Cruz |
| Abstract: | Predicting financial market stress has long proven to be a largely elusive goal. Advances in artificial intelligence and machine learning offer new possibilities to tackle this problem, given their ability to handle large datasets and unearth hidden nonlinear patterns. In this paper, we develop a new approach based on a combination of a recurrent neural network (RNN) and a large language model. Focusing on deviations from triangular arbitrage parity (TAP) in the Euro-Yen currency pair, our RNN produces interpretable daily forecasts of market dysfunction 60 business days ahead. To address the "black box" limitations of RNNs, our model assigns data-driven, time-varying weights to the input variables, making its decision process transparent. These weights serve a dual purpose. First, their evolution in and of itself provides early signals of latent changes in market dynamics. Second, when the network forecasts a higher probability of market dysfunction, these variable-specific weights help identify relevant market variables that we use to prompt an LLM to search for relevant information about potential market stress drivers. |
| Keywords: | market dysfunction, liquidity, arbitrage, artificial intelligence, financial stability |
| JEL: | G14 G15 G17 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1291 |
| By: | Bach, Ruben L.; Klamm, Christopher; Heyne, Stefanie; Kogan, Irena; Kononykhina, Olga; Jarck, Jana |
| Abstract: | Accurate occupational classification from open-ended survey responses is vital for research in sociology, economics, and political science, yet manual coding remains resource-intensive and difficult to scale. We propose a novel pipeline that leverages large language models (LLMs) augmented with retrieval (RAG) to automate the assignment of International Standard Classification of Occupations (ISCO) codes. Drawing on survey data from a sample of recently arrived Afghan and Syrian refugees in Germany, we preprocess noisy occupational descriptions using LLMs and apply vector-based similarity search to retrieve candidate ISCO codes. The final classification is selected by LLMs, constrained to the retrieved candidates and accompanied by interpretable justifications. We evaluate the system’s performance against expert-coded labels, demonstrating high agreement and robustness across languages. Our findings suggest that RAG-powered LLMs can substantially improve the accuracy, scalability, and accessibility of occupational classification, with particular benefits for multilingual and resource-constrained research settings. In addition, we describe a prototypical pipeline that other researchers can readily adapt for applying LLMs to similar classification tasks, facilitating transparency, reproducibility, and broader adoption. |
| Date: | 2025–09–24 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:ge56f_v1 |