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on Artificial Intelligence |
| By: | Congressional Budget Office |
| Abstract: | Because artificial intelligence has the potential to change how businesses and the federal government provide goods and services, it could affect economic growth, employment and wages, and the distribution of income in the economy. Such changes could in turn affect the federal budget. The direction of those effects—whether they increased or decreased federal revenues or spending—along with their size and timing, are uncertain. In this report, CBO provides an overview of the channels through which the adoption of AI could affect the U.S. economy and the federal budget. |
| JEL: | D83 E20 E66 H20 J21 J23 J31 O31 O33 O38 |
| Date: | 2024–12–20 |
| URL: | https://d.repec.org/n?u=RePEc:cbo:report:60774 |
| By: | SOLER GARRIDO Josep (European Commission - JRC); DE NIGRIS Sarah (European Commission - JRC); BASSANI Elias (European Commission - JRC); SANCHEZ Ignacio (European Commission - JRC); EVAS Tatjana; ANDRÉ Antoine-Alexandre; BOULANGÉ Thierry |
| Abstract: | The European Union adopted the AI Act in August 2024, and the provisions for high-risk AI systems will start to apply after a transition period of 2 or 3 years. European harmonised standards for the AI Act, provided they are published in the Official Journal of the EU, will grant a legal presumption of conformity to AI systems developed in accordance with them. European standardisation organisations, led by CEN and CENELEC, are in the process of drafting the necessary AI standards, following a request from the European Commission. This brief discusses some of the key characteristics expected from upcoming standards that would support the implementation of the AI Act. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc139430 |
| By: | Yuhao Fu; Nobuyuki Hanaki |
| Abstract: | This experimental study investigates whether people rely more on ChatGPT (GPT-4) than on their human peers when detecting AI-generated fake news (deepfake news). In multiple rounds of deepfake detection tasks conducted in a laboratory setting, student participants exhibited a greater reliance on ChatGPT compared to their peers. We explored this over-reliance on AI from two perspectives: the weight of advice (WOA) and the decomposition of reliance (DOR) into two stages. Our analysis indicates that reliance on external advice is primarily influenced by the source and quality of the advice, as well as the subjects’ prior beliefs, knowledge, and experience, while the type of news and time spent on tasks have no effect. Additionally, our study indicates a potential sequential mechanism of advice utilization, wherein the advice source affects reliance in both stages—activation and integration—whereas the quality of the advice, along with knowledge and experience, influences only the second stage. Our findings suggest that relying on AI to detect AI may not be detrimental and could, in fact, contribute to a deeper understanding of human-AI interaction and support advancements in AI development during the Generative Artificial Intelligence (GAI) era. |
| Date: | 2024–03 |
| URL: | https://d.repec.org/n?u=RePEc:dpr:wpaper:1233r |
| By: | Nobuyuki Hanaki; Bolin Mao; Tiffany Tsz Kwan Tse; Wenxin Zhou |
| Abstract: | This study examined participants’ willingness to pay for stock price forecasts provided by an algorithm, financial experts, and peers. Participants valued algorithmic advice more highly and relied on it as much as expert advice. This preference for algorithms – despite their similar or even lower performance – suggests a shift in perception, particularly among students, toward viewing AI as a reliable and valuable source. However, this “algorithm appreciation” reduced participants’ payoffs, as they overpaid for advice that did not sufficiently enhance performance. These findings underscore the need to develop tools and policies that enable individuals to better assess algorithm performance. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:dpr:wpaper:1268 |
| By: | Peede, Lennert (Institute for Employment Research (IAB), Nuremberg, Germany); Stops, Michael (Institute for Employment Research (IAB), Nuremberg, Germany) |
| Abstract: | "We study how artificial intelligence (AI) affects labour demand at the establishment level. We use the share of AI related vacancy postings at the establishment level to measure efforts to develop, implement or use AI technologies. Low overall AI vacancy shares show that we study a phase of early AI adoption. At the establishment level, the AI vacancy share relates to a small reduction in those skills which are not related to AI technologies. We further find no effects on overall employment growth but slightly higher employment growth in jobs for highly skilled workers." (Author's abstract, IAB-Doku) ((en)) |
| Keywords: | Bundesrepublik Deutschland ; IAB-Open-Access-Publikation ; Auswirkungen ; Kompetenzprofil ; Beschäftigungseffekte ; Betrieb ; Entwicklung ; Jobbörse ; künstliche Intelligenz ; Anwendung ; Qualifikationsanforderungen ; IAB-Stellenerhebung ; Stellenanzeige ; Stellenausschreibung ; Arbeitskräftenachfrage ; 2015-2019 |
| JEL: | E23 J24 J63 O33 |
| Date: | 2024–11–22 |
| URL: | https://d.repec.org/n?u=RePEc:iab:iabdpa:202415 |
| By: | Gathmann, Christina (LISER); Grimm, Felix (LISER); Winkler, Erwin (University of Erlangen-Nuremberg) |
| Abstract: | How does Artificial Intelligence (AI) affect the task content of work, and how do workers adjust to the diffusion of AI in the economy? To answer these important questions, we combine novel patent-based measures of AI and robot exposure with individual survey data on tasks performed on the job and administrative data on worker careers. Like prior studies, we find that robots have reduced routine tasks. In sharp contrast, AI has reduced non-routine abstract tasks like information gathering and increased the demand for 'high-level' routine tasks like monitoring processes. These task shifts mainly occur within detailed occupations and become stronger over time. While displacement effects are small, workers have responded by switching jobs, often to less exposed industries. We also document that low-skilled workers suffer some wage losses, while high-skilled incumbent workers experience wage gains. |
| Keywords: | Artificial Intelligence, tasks, skills, reallocation, robots, patents |
| JEL: | J23 J24 J31 J62 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17554 |
| By: | Elina M\"akel\"a; Fabian Stephany |
| Abstract: | The question of whether AI substitutes or complements human work is central to debates on the future of work. This paper examines the impact of AI on skill demand and compensation in the U.S. economy, analysing 12 million online job vacancies from 2018 to 2023. It investigates internal effects (within-job substitution and complementation) and external effects (across occupations, industries, and regions). Our findings reveal a significant increase in demand for AI-complementary skills, such as digital literacy, teamwork, and resilience, alongside rising wage premiums for these skills in AI roles like Data Scientist. Conversely, substitute skills, including customer service and text review, have declined in both demand and value within AI-related positions. Examining external effects, we find a notable rise in demand for complementary skills in non-AI roles linked to the growth of AI-related jobs in specific industries or regions. At the same time, there is a moderate decline in non-AI roles requiring substitute skills. Overall, AI's complementary effect is up to 50% larger than its substitution effect, resulting in net positive demand for skills. These results, replicated for the UK and Australia, highlight AI's transformative impact on workforce skill requirements. They suggest reskilling efforts should prioritise not only technical AI skills but also complementary skills like ethics and digital literacy. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.19754 |
| By: | Kauhanen, Antti; Rouvinen, Petri |
| Abstract: | Abstract The labor market implications of generative artificial intelligence (GenAI) have been discussed since the launch of ChatGPT. So far, the most reliable studies on the labour market effects of GenAI have been conducted by studying online labour markets. These studies compare jobs that are more exposed to GenAI with less exposed jobs before and after the launch of ChatGPT. Studies have found that demand for work has decreased in tasks more exposed to GenAI. In this brief, we present the results of a study in which we compare earnings and employment development in occupations more exposed to GenAI with occupations less exposed to GenAI before and after the publication of ChatGPT using Statistics Finland’s Incomes Register. The results show that earnings have increased faster in exposed occupations than in non-exposed occupations. There have been no differences in employment development between these groups. In Finland, GenAI has so far not caused any negative labour market effects. The results suggest that GenAI has increased labour productivity and thus labour demand. Of course, in the longer term (beyond the 20-month ex post window in our study), the results might be different as GenAI evolves and its use expands. |
| Keywords: | Generative artificial intelligence, Technological change, Employment, Wages, Occupations |
| JEL: | E24 J21 O33 |
| Date: | 2024–11–19 |
| URL: | https://d.repec.org/n?u=RePEc:rif:briefs:143 |
| By: | Kauhanen, Antti; Kässi, Otto; Pajarinen, Mika; Rouvinen, Petri; Vanhala, Pekka |
| Abstract: | Abstract Half of Finns have tried generative artificial intelligence (AI). Among the employed, thirty percent have used generative artificial intelligence for work purposes. Generative artificial intelligence has already had a significant impact on leisure and work in Finland. Although the United States is the leading country in generative artificial intelligence, Finns are more active users than Americans – especially thanks to women. Finns view the effects of generative artificial intelligence on work productivity, quality, and satisfaction positively. The situation among employer organizations is mixed: one-third of employees have received AI guidance or training from their employer, but another one-third have not received any AI instructions from their employer. Despite many positive aspects, intensities and domains of Finns’ AI use suggest that they are far from fully utilizing it. Generative AI is used for work purposes by 29%, but only 11% have weekly use, and only 8% have use in domains where generative AI is particularly suitable. For example, Nobel laureate in economics Daron Acemoglu has suggested that – without significant advancements – the economic growth impact of generative artificial intelligence will be only modestly positive. The evidence presented in this brief suggests that, to have a sizable impact on Finland’s future growth, AI applications ought to be more widespread and deeper than what they currently are. |
| Keywords: | Generative artificial intelligence, Technological change, Employment, Labor market, Occupations |
| JEL: | E24 J21 O33 |
| Date: | 2024–11–19 |
| URL: | https://d.repec.org/n?u=RePEc:rif:briefs:144 |
| By: | GRIMMELIKHUIJSEN Stephan; TANGI Luca (European Commission - JRC) |
| Abstract: | The adoption of artificial intelligence (AI) in the public sector is now reaching a stage where, drawing on the experience of early pilots and adoptions, EU public administrations are starting to face the challenges of implementing AI solutions. In response, this study investigates AI adoption in the public sector with a twofold goal: Add evidence to the existing body of knowledge to have a better understanding of the dynamics underlying AI adoption in the EU. We do this by providing quantitative (survey) insights into AI readiness and adoption in the public sector, across different country contexts. By offering a picture of the status of AI adoption and readiness in public administrations, we identify the main challenges and drivers of AI adoption, which are required for ensuring AI’s trustworthy use. Define recommendations for managers in the public sector and public administrations. Based on the insights from the first aim, we formulate ways forward to inform policymakers. We surveyed 576 public managers in seven countries: Germany, Spain, France, the Netherlands, Austria, Poland and Sweden. The sample was diverse in age, job level, organisation size and geographical origin. We asked each of them about the level of AI adoption in their organisation. This was measured in two ways: we asked specifically about the extent to which they thought that their organisation had implemented AI projects in service delivery, internal operations and policy decision-making. Next, we asked about the exact number of projects that were either planned or implemented, with the response options of 0, 1, 2–5 or more than 5. Building on the latest scientific insights, we look at what combination of technological, organisational, environmental and individual-level factors contributes to AI adoption. Based on our research, we have three key conclusions: 1. AI adoption is no longer a promise; it is a reality, in particular for service delivery and internal operations. 2. Soft factors and in-house expertise are important internal factors for AI adoption. 3. Citizen needs are an important external factor for AI adoption. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc138684 |
| By: | MEDAGLIA Rony; MIKALEF Patrick; TANGI Luca (European Commission - JRC) |
| Abstract: | The diffusion of artificial intelligence (AI) in the public sector depends largely on ensuring the presence of appropriate competences and establishing appropriate governance practices to deploy solutions. This report builds on a synthesis of empirical research, grey and policy literature, on an expert workshop and on interviews from seven case studies of European public organisations to identify the competences and governance practices around AI required to enable value generation in the public sector. Based on the analysis, we present a comprehensive framework for relevant competences and a framework for the governance practices for AI in the public sector. The report also introduces six recommendations to be implemented through 18 actions to facilitate the development of the competences and governance practices needed for AI in the public sector in Europe. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc138702 |
| By: | MORIKAWA Masayuki |
| Abstract: | Based on a survey of Japanese workers, this study documents the characteristics of workers who use artificial intelligence (AI) in their jobs and estimates the effects of this new general-purpose technology on macroeconomic productivity. The results indicate, first, 8.3% of workers used AI in their jobs in 2024, which is approximately 1.5 times than in 2023. Second, more educated and high-wage workers tend to use AI, suggesting that its diffusion may increase labor market inequality. Third, the use of AI is estimated to have increased labor productivity in the macroeconomy by 0.5–0.6%. Fourth, nearly 30% of workers expect to use AI for their jobs in the future, suggesting that its macroeconomic effects will increase. However, the productivity effect of AI for those who recently started using it is relatively small, suggesting a diminishing productivity impact of AI. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:24084 |
| By: | Yichen Luo; Yebo Feng; Jiahua Xu; Paolo Tasca; Yang Liu |
| Abstract: | Cryptocurrency investment is inherently difficult due to its shorter history compared to traditional assets, the need to integrate vast amounts of data from various modalities, and the requirement for complex reasoning. While deep learning approaches have been applied to address these challenges, their black-box nature raises concerns about trust and explainability. Recently, large language models (LLMs) have shown promise in financial applications due to their ability to understand multi-modal data and generate explainable decisions. However, single LLM faces limitations in complex, comprehensive tasks such as asset investment. These limitations are even more pronounced in cryptocurrency investment, where LLMs have less domain-specific knowledge in their training corpora. To overcome these challenges, we propose an explainable, multi-modal, multi-agent framework for cryptocurrency investment. Our framework uses specialized agents that collaborate within and across teams to handle subtasks such as data analysis, literature integration, and investment decision-making for the top 30 cryptocurrencies by market capitalization. The expert training module fine-tunes agents using multi-modal historical data and professional investment literature, while the multi-agent investment module employs real-time data to make informed cryptocurrency investment decisions. Unique intrateam and interteam collaboration mechanisms enhance prediction accuracy by adjusting final predictions based on confidence levels within agent teams and facilitating information sharing between teams. Empirical evaluation using data from November 2023 to September 2024 demonstrates that our framework outperforms single-agent models and market benchmarks in classification, asset pricing, portfolio, and explainability performance. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.00826 |
| By: | Yijia Xiao; Edward Sun; Di Luo; Wei Wang |
| Abstract: | Significant progress has been made in automated problem-solving using societies of agents powered by large language models (LLMs). In finance, efforts have largely focused on single-agent systems handling specific tasks or multi-agent frameworks independently gathering data. However, multi-agent systems' potential to replicate real-world trading firms' collaborative dynamics remains underexplored. TradingAgents proposes a novel stock trading framework inspired by trading firms, featuring LLM-powered agents in specialized roles such as fundamental analysts, sentiment analysts, technical analysts, and traders with varied risk profiles. The framework includes Bull and Bear researcher agents assessing market conditions, a risk management team monitoring exposure, and traders synthesizing insights from debates and historical data to make informed decisions. By simulating a dynamic, collaborative trading environment, this framework aims to improve trading performance. Detailed architecture and extensive experiments reveal its superiority over baseline models, with notable improvements in cumulative returns, Sharpe ratio, and maximum drawdown, highlighting the potential of multi-agent LLM frameworks in financial trading. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.20138 |
| By: | Kemal Kirtac; Guido Germano |
| Abstract: | We investigate the efficacy of large language models (LLMs) in sentiment analysis of U.S. financial news and their potential in predicting stock market returns. We analyze a dataset comprising 965, 375 news articles that span from January 1, 2010, to June 30, 2023; we focus on the performance of various LLMs, including BERT, OPT, FINBERT, and the traditional Loughran-McDonald dictionary model, which has been a dominant methodology in the finance literature. The study documents a significant association between LLM scores and subsequent daily stock returns. Specifically, OPT, which is a GPT-3 based LLM, shows the highest accuracy in sentiment prediction with an accuracy of 74.4%, slightly ahead of BERT (72.5%) and FINBERT (72.2%). In contrast, the Loughran-McDonald dictionary model demonstrates considerably lower effectiveness with only 50.1% accuracy. Regression analyses highlight a robust positive impact of OPT model scores on next-day stock returns, with coefficients of 0.274 and 0.254 in different model specifications. BERT and FINBERT also exhibit predictive relevance, though to a lesser extent. Notably, we do not observe a significant relationship between the Loughran-McDonald dictionary model scores and stock returns, challenging the efficacy of this traditional method in the current financial context. In portfolio performance, the long-short OPT strategy excels with a Sharpe ratio of 3.05, compared to 2.11 for BERT and 2.07 for FINBERT long-short strategies. Strategies based on the Loughran-McDonald dictionary yield the lowest Sharpe ratio of 1.23. Our findings emphasize the superior performance of advanced LLMs, especially OPT, in financial market prediction and portfolio management, marking a significant shift in the landscape of financial analysis tools with implications to financial regulation and policy analysis. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.19245 |
| By: | ITO Arata; SATO Masahiro; OTA Rui |
| Abstract: | Policy uncertainty has the potential to reduce policy effectiveness. Existing studies have measured policy uncertainty by tracking the frequency of specific keywords in newspaper articles. However, this keyword-based approach fails to account for the context of the articles and differentiate the types of uncertainty that such contexts indicate. This study introduces a new method of measuring different types of policy uncertainty in news content which utilizes large language models (LLMs). Specifically, we differentiate policy uncertainty into forward-looking and backward-looking uncertainty, or in other words, uncertainty regarding future policy direction and uncertainty about the effectiveness of the current policy. We fine-tune the LLMs to identify each type of uncertainty expressed in newspaper articles based on their context, even in the absence of specific keywords indicating uncertainty. By applying this method, we measure Japan’s monetary policy uncertainty (MPU) from 2015 to 2016. To reflect the unprecedented monetary policy conditions during this period when the unconventional policies were taken, we further classify MPU by layers of policy changes: changes in specific market operations and changes in the broader policy framework. The experimental results show that our approach successfully captures the dynamics of MPU, particularly for forward-looking uncertainty, which is not fully captured by the existing approach. Forward- and backward-looking uncertainty indices exhibit distinct movements depending on the conditions under which changes in the policy framework occur. This suggests that perceived uncertainty regarding monetary policy would be state-dependent, varying with the prevailing social environment. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:24080 |
| By: | Francesco Audrino (University of St. Gallen; Swiss Finance Institute); Jessica Gentner (University of St. Gallen; Swiss National Bank); Simon Stalder (Swiss National Bank; University of Lugano) |
| Abstract: | This paper presents an innovative method for measuring uncertainty using Large Language Models (LLMs), offering enhanced precision and contextual sensitivity compared to the conventional methods used to construct prominent uncertainty indices. By analyzing newspaper texts with state-of-the-art LLMs, our approach captures nuances often missed by conventional methods. We develop indices for various types of uncertainty, including geopolitical risk, economic policy, monetary policy, and financial market uncertainty. Our findings show that shocks to these LLM-based indices exhibit stronger associations with macroeconomic variables, shifts in investor behaviour, and asset return variations than conventional indices, underscoring their potential for more accurately reflecting uncertainty. |
| Keywords: | Large Language Models, Economic policy, Geopolitical risk, Monetary policy, Financial markets, Uncertainty measurment |
| JEL: | C45 C55 E44 G12 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2468 |
| By: | Jule Schuettler (University of St.Gallen); Francesco Audrino (University of St. Gallen; Swiss Finance Institute); Fabio Sigrist (Lucerne University of Applied Sciences and Arts) |
| Abstract: | We present a novel approach to sentiment analysis in financial markets by using a state-of-the-art large language model, a market data-driven labeling approach, and a large dataset consisting of diverse financial text sources including earnings call transcripts, newspapers, and social media tweets. Based on our approach, we define a predictive high-low sentiment asset pricing factor which is significant in explaining cross-sectional asset pricing for U.S. stocks. Further, we find that a long/short equal-weighted portfolio yields an average annualized return of 35.56% and an annualized Sharpe ratio of 2.21, remaining substantially profitable even when transaction costs are considered. A comparison with an alternative financial sentiment analysis tool (FinBERT) underscores the superiority of our data-driven labeling approach over traditional human-annotated labeling. |
| Keywords: | natural language processing, large language models, DeBERTa, asset pricing |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2469 |
| By: | Oudom Hean; Utsha Saha; Binita Saha |
| Abstract: | In recent years, Large Language Models (LLMs) have emerged as a transformative development in artificial intelligence (AI), drawing significant attention from industry and academia. Trained on vast datasets, these sophisticated AI systems exhibit impressive natural language processing and content generation capabilities. This paper explores the potential of LLMs to address key challenges in personal finance, focusing on the United States. We evaluate several leading LLMs, including OpenAI's ChatGPT, Google's Gemini, Anthropic's Claude, and Meta's Llama, to assess their effectiveness in providing accurate financial advice on topics such as mortgages, taxes, loans, and investments. Our findings show that while these models achieve an average accuracy rate of approximately 70%, they also display notable limitations in certain areas. Specifically, LLMs struggle to provide accurate responses for complex financial queries, with performance varying significantly across different topics. Despite these limitations, the analysis reveals notable improvements in newer versions of these models, highlighting their growing utility for individuals and financial advisors. As these AI systems continue to evolve, their potential for advancing AI-driven applications in personal finance becomes increasingly promising. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.19784 |