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on Artificial Intelligence |
| By: | Yan Liu; He Wang |
| Abstract: | Leveraging unconventional data, including website traffic data and Google Trends, this paper unveils the real-time usage patterns of generative artificial intelligence tools by individuals across countries. The paper also examines country-level factors driving the uptake and early impacts of generative artificial intelligence on online activities. As of March 2024, the top 40 generative artificial intelligence tools attract nearly 3 billion visits per month from hundreds of millions of users. ChatGPT alone commanded 82.5 percent of the traffic, yet reaching only one-eightieth of Google’s monthly visits. Generative artificial intelligence users skew young, highly educated, and male, particularly for video generation tools, with usage patterns strongly indicating productivity-related activities. Generative artificial intelligence has achieved unprecedentedly rapid global diffusion, reaching almost all economies worldwide within 16 months of ChatGPT’s release. Middle-income economies have disproportionately high adoption of generative artificial intelligence relative to their economic scale, now contribute more than 50 percent of global traffic, while low-income economies contribute less than 1 percent. Regression analysis reveals that income level, share of youth population, digital infrastructure, specialization in high-skill tradable services, English proficiency, and human capital are strongly correlated with higher uptake of generative artificial intelligence. The paper also documents disruptions in online traffic patterns and emphasizes the need for targeted investments in digital infrastructure and skills development to harness the full potential of artificial intelligence. |
| Date: | 2024–08–19 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10870 |
| By: | Stefania Albanesi; António Dias da Silva; Juan F. Jimeno; Ana Lamo; Alena Wabitsch |
| Abstract: | We examine the link between the diffusion of artificial intelligence (AI) enabled technologies and changes in the female employment share in 16 European countries over the period 2011-2019. Using data for occupations at the 3-digit level, we find that on average female employment shares increased in occupations more exposed to AI. Countries with high initial female labor force participation and higher initial female relative education show a stronger positive association. While there exists heterogeneity across countries, almost all show a positive relation between changes in female employment shares within occupations and exposure to AI-enabled automation. |
| JEL: | J23 O33 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33451 |
| By: | Gmyrek, Paweł; Winkler-Seales, Hernan Jorge; Garganta, Santiago |
| Abstract: | Empirical evidence on the potential impacts of generative artificial intelligence (GenAI) is mostly focused on high-income countries. In contrast, little is known about the role of this technology on the future economic pathways of developing economies. This paper contributes to fill this gap by estimating the exposure of the Latin American labor market to GenAI. It provides detailed statistics of GenAI exposure between and within countries by leveraging a rich set of harmonized household and labor force surveys. To account for the slower pace of technology adoption in developing economies, it adjusts the measures of exposure to GenAI by using the likelihood of accessing digital technologies at work. This is then used to assess the extent to which the digital divide across and within countries will be a barrier to maximize the productivity gains among occupations that could otherwise be augmented by GenAI tools. The findings show that certain characteristics are consistently correlated with higher exposure. Specifically, urban-based jobs that require higher education, are situated in the formal sector, and are held by individuals with higher incomes are more likely to come into interaction with this technology. Moreover, there is a pronounced tilt toward younger workers facing greater exposure, including the risk of job automation, particularly in the finance, insurance, and public administration sectors. When adjusting for access to digital technologies, the findings show that the digital divide is a major barrier to realizing the positive effects of GenAI on jobs in the region. In particular, nearly half of the positions that could potentially benefit from augmentation are hampered by lack of use of digital technologies. This negative effect of the digital divide is more pronounced in poorer countries. |
| Date: | 2024–07–26 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10863 |
| By: | Yan Liu |
| Abstract: | This paper presents a multi-sector growth model to elucidate the general equilibrium effects of generative artificial intelligence on economic growth, structural transformation, and international production specialization. Using parameters from the literature, the paper employs simulations to quantify the impacts of artificial intelligence across various scenarios. The paper introduces a crucial distinction between high-skill, highly digitalized, tradable services and low-skill, less digitalized, less-tradable services. The model’s key propositions align with empirical evidence, and the simulations yield novel and sobering predictions. Unless artificial intelligence achieves widespread cross-sector adoption and catalyzes paradigm-shifting innovations that fundamentally reshape consumer preferences, its growth benefits may be limited. Conversely, its disruptive impact on labor markets could be profound. This paper highlights the risk of “premature de-professionalization”, where artificial intelligence likely shrinks the space for countries to generate well-paid jobs in high-skill services. The analysis portends that developing countries failing to adopt artificial intelligence swiftly risk entrapment as commodity exporters, potentially facing massive youth underemployment, diminishing social mobility, and stagnating or even declining living standards. The paper also discusses artificial intelligence’s broader implications on inequality, exploring multiple channels through which it may exacerbate or mitigate economic disparities. |
| Date: | 2024–09–17 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10915 |
| By: | Hipólito, Inês |
| Abstract: | This paper applies complex systems theory to examine generative artificial intelligence (AI) as a contemporary wicked problem. Generative AI technologies, which autonomously create content like images and text, intersect with societal domains such as ethics, economics, and governance, exhibiting complex interdependencies and emergent behaviors. Using methodologies like network analysis and agent-based modeling, the paper maps these interactions and explores potential interventions. A mathematical model is developed to simulate the dynamics between key components of the AI-society system, including AI development, economic concentration, labor markets, regulatory frameworks, public trust, ethical implementation, global competition, and distributed AI ecosystems. The model demonstrates non-linear dynamics, feedback loops, and sensitivity to initial conditions characteristic of complex systems. By simulating various interventions, the study provides insights into strategies for steering AI development towards more positive societal outcomes. These include strengthening regulatory frameworks, enhancing ethical implementation, and promoting distributed AI ecosystems. The paper advocates for using this complex systems framework to inform inclusive policy and regulatory strategies that balance innovation with societal well-being. It concludes that embracing complexity enables stakeholders to better navigate the evolving challenges of generative AI, fostering more sustainable and equitable technological advancements. |
| Date: | 2024–08–29 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:aq4tw_v1 |
| By: | Altunay, Paul-Christoph; Vetter, Oliver A. |
| Abstract: | The rapid advancement of artificial intelligence (AI) has ushered in a surge of AI startups. AI startups have been hypothesized to be organized in and benefit from ecosystems, with concrete research to substantiate this claim remaining scarce. We aim to bridge this gap by providing a first step towards a scientific understanding of AI startup ecosystems, focusing on investor-related ecosystem effects. We employ a network theory approach to examine the relationship between investor-related ecosystems and AI startup success. We provide an overview of how investor-related ecosystems influence the success of AI startups and how investor types affect their success differently. Findings suggest that AI startups disproportionately benefit from investor-related ecosystem effects and that they differ by investor type, suggesting a new pecking order for choosing investors. In light of these results, practitioners and scholars are prompted to reassess established norms of entrepreneurial finance and startup success factors concerning AI startups. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:152916 |
| By: | Gorwa, Robert; Veale, Michael (University College London) |
| Abstract: | Cite as: Robert Gorwa and Michael Veale, 2024. `Moderating Model Marketplaces: Platform Governance Puzzles for AI Intermediaries.' Law, Innovation and Technology 16 (2). (First uploaded as a preprint November 2023.) The AI development community is increasingly making use of hosting intermediaries such as Hugging Face that provide easy access to user-uploaded models and training data. These model marketplaces lower technical deployment barriers for hundreds of thousands of users, yet can be used in numerous potentially harmful and illegal ways. In this article, we argue that AI models, which can both `contain' content and be open-ended tools, present one of the trickiest platform governance challenges seen to date. We provide case studies of several incidents across three illustrative platforms --- Hugging Face, GitHub and Civitai --- to examine how model marketplaces moderate models. Building on this analysis, we outline important (and yet nevertheless limited) practices that industry has been developing to respond to moderation demands: licensing, access and use restrictions, automated content moderation, and open policy development. While the policy challenge at hand is a considerable one, we conclude with some ideas as to how platforms could better mobilize resources to act as a careful, fair, and proportionate regulatory access point. |
| Date: | 2024–09–09 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:6dfk3_v1 |
| By: | Hötte, Kerstin; Tarannum, Taheya; Verendel, Vilhelm; Bennett, Lauren |
| Abstract: | Governing artificial intelligence (AI) is high on the political agenda, but it is still not clear how to define and measure it. We compare four approaches to identifying AI patented inventions that reflect different ways of understanding AI with divergent definitions. Using US patents from 1990-2019, we assess the extent to which each approach qualifies AI as a general purpose technology (GPT) and study patterns of concentration, which both are criteria relevant for regulation. The four approaches overlap on only 1.37% of patents and vary in scale, accounting for shares that range from 3-17% of all US patents in 2019. The smallest set of AI patents in our sample, identified by the latest AI keywords, is most GPT-like with high levels of growth and generality. All four approaches show AI inventions to be concentrated in few firms, confirming worries about competition. Our results suggest that regulation may not be straightforward, as the identification of AI inventions ultimately depends on how AI is defined. |
| Keywords: | Artificial Intelligence, Governance, General Purpose Technology, Concentration, Patent, Classification |
| JEL: | O31 O33 O34 |
| Date: | 2024–03 |
| URL: | https://d.repec.org/n?u=RePEc:amz:wpaper:2024-02 |
| By: | Pat Pataranutaporn; Nattavudh Powdthavee; Pattie Maes |
| Abstract: | Surnames often convey implicit markers of social status, wealth, and lineage, shaping perceptions in ways that can perpetuate systemic biases. This study investigates whether and how surnames influence AI-driven decision-making, focusing on their effects across key areas such as hiring recommendations, leadership appointments, and loan approvals. Drawing on 600 surnames from the United States and Thailand, countries with differing sociohistorical dynamics and surname conventions, we categorize names into Rich, Legacy, Normal, and phonetically similar Variant groups. Our findings reveal that elite surnames consistently predict AI-generated perceptions of power, intelligence, and wealth, leading to significant consequences for decisions in high-stakes situations. Mediation analysis highlights perceived intelligence as a crucial pathway through which surname biases operate. Providing objective qualifications alongside the surnames reduces, but does not eliminate, these biases, especially in contexts with uniformly low credentials. These results call for fairness-aware algorithms and robust policy interventions to mitigate the reinforcement of inherited inequalities by AI systems. Our work also urges a reexamination of algorithmic accountability and its societal impact, particularly in systems designed for meritocratic outcomes. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.19407 |
| By: | Moisio, Pasi; Mesiäislehto, Merita; Peltoniemi, Johanna; Pihlajamäki, Mika; Hiilamo, Heikki |
| Abstract: | Utilizing Large Language Models (LLM), this study investigates the evolution of an innovative social security policy idea, the General Benefit concept into a policy reform proposal in Fin-land from 2007 to 2023. Drawing from the ideational analysis we hypothesize that political parties struggled over social security conditionality during the 2010s and that social security simplification was manipulated differently in relation to conditionality. Our primary data is elec-tion manifestos and governmental programs from 2007-2023. We employed LLMs, mainly a customized ChatGPT, for the text analysis of policy documents. Additionally, we conduct a critical human evaluation of the LLMs analysis and publish our model in the GPT store for the open replication of analyses. Findings indicate that the weakening of the tripartite industrial relations system and the break-ing of “status quo of three big parties” allowed new parties to influence social policy in 2010s. The General Benefit emerged as a response to calls for social security simplification and for countering (unconditional) basic income proposals. Adopted in 2023, the General Benefit concept aims to merge Finnish universal / residence-based social insurance benefits for the working-aged while preserving core principles like social risk categories and conditionality. Despite increased nativism from the rising True Finns party, and the adoption of universal / unconditional basic income by several parties, Finnish social policy trends from 2007 to 2023 continued to emphasize employment and public finance sustainability. Our study also contributes to methodological discussions on using LLMs in policy analysis. The “human evaluation”, performed by the authors, confirms that the LLM analysis accurately summarises the main features of the policy evolution. However, we also found that the LLM lacks ability to recognise the nuances of “multidimensional” political language and is not very helpful in cross-sectional evaluation, which leaves the analysis partly shallow. Thus, we con-clude that in qualitative policy analysis, LLMs in their current form are suitable for comple-menting rather than substituting human evaluation. |
| Date: | 2024–10–10 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:ab8mr_v1 |
| By: | Christian Fieberg; Lars Hornuf; Maximilian Meiler; David J. Streich |
| Abstract: | We study whether large language models (LLMs) can generate suitable financial advice and which LLM features are associated with higher-quality advice. To this end, we elicit portfolio recommendations from 32 LLMs for 64 investor profiles, which differ in their risk preferences, home country, sustainability preferences, gender, and investment experience. Our results suggest that LLMs are generally capable of generating suitable financial advice that takes into account important investor characteristics when determining market and risk exposures. The historical performance of the recommended portfolios is on par with that of professionally managed benchmark portfolios. We also find that foundation models and larger models generate portfolios that are easier to implement and more sensitive to investor characteristics than fine-tuned models and smaller models. Some of our results are consistent with LLMs inheriting human biases such as home bias. We find no evidence of gender-based discrimination, which can be found in human financial advice. |
| Keywords: | generative AI, artificial intelligence, large language models, financial advice portfolio management |
| JEL: | G00 G11 G40 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11666 |
| By: | Ziyao Zhou; Ronitt Mehra |
| Abstract: | This project introduces an end-to-end trading system that leverages Large Language Models (LLMs) for real-time market sentiment analysis. By synthesizing data from financial news and social media, the system integrates sentiment-driven insights with technical indicators to generate actionable trading signals. FinGPT serves as the primary model for sentiment analysis, ensuring domain-specific accuracy, while Kubernetes is used for scalable and efficient deployment. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.01574 |
| By: | Rajesh P. Narayanan; R. Kelley Pace |
| Abstract: | Emergent technologies such as solar power, electric vehicles, and artificial intelligence (AI) often exhibit exponential or power function price declines and various ``S-curves'' of adoption. We show that under CES and VES utility, such price and adoption curves are functionally linked. When price declines follow Moore's, Wright's and AI scaling "Laws, '' the S-curve of adoption is Logistic or Log-Logistic whose slope depends on the interaction between an experience parameter and the elasticity of substitution between the incumbent and emergent good. These functional relations can serve as a building block for more complex models and guide empirical specifications of technology adoption. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00909 |
| By: | Kamer Ali Yuksel; Hassan Sawaf |
| Abstract: | Financial metrics like the Sharpe ratio are pivotal in evaluating investment performance by balancing risk and return. However, traditional metrics often struggle with robustness and generalization, particularly in dynamic and volatile market conditions. This paper introduces AlphaSharpe, a novel framework leveraging large language models (LLMs) to iteratively evolve and optimize financial metrics. AlphaSharpe generates enhanced risk-return metrics that outperform traditional approaches in robustness and correlation with future performance metrics by employing iterative crossover, mutation, and evaluation. Key contributions of this work include: (1) an innovative use of LLMs for generating and refining financial metrics inspired by domain-specific knowledge, (2) a scoring mechanism to ensure the evolved metrics generalize effectively to unseen data, and (3) an empirical demonstration of 3x predictive power for future risk-return forecasting. Experimental results on a real-world dataset highlight the superiority of AlphaSharpe metrics, making them highly relevant for portfolio managers and financial decision-makers. This framework not only addresses the limitations of existing metrics but also showcases the potential of LLMs in advancing financial analytics, paving the way for informed and robust investment strategies. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00029 |