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on Artificial Intelligence |
| By: | Marie Obidzinski (Université Paris Panthéon Assas, CRED, Paris, France); Yves Oytana (Université de Franche-Comté, CRESE, Besançon, France) |
| Abstract: | We characterize the socially optimal liability sharing rule in a situation where a manufacturer develops an artificial intelligence (AI) system that is then used by a human operator (or user). First, the manufacturer invests to increase the autonomy of the AI (i.e., the set of situations that the AI can handle without human intervention) and sets a selling price. The user then decides whether or not to buy the AI. Since the autonomy of the AI remains limited, the human operator must sometimes intervene even when the AI is in use. Our main assumptions relate to behavioral inattention. Behavioral inattention reduces the effectiveness of user intervention and increases the expected harm. Only some users are aware of their own attentional limits. Under the assumption that AI outperforms users, we show that policymakers may face a tradeoff when choosing how to allocate liability between the manufacturer and the user. Indeed, the manufacturer may underinvest in the autonomy of the AI. If this is the case, the policymaker can incentivize the latter to invest more by increasing his share of liability. On the other hand, increasing the liability of the manufacturer may come at the cost of slowing down the diffusion of AI technology. |
| Keywords: | K4 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:afd:wpaper:2406 |
| By: | Herbert Dawid (Bielefeld University, Germany); Philipp Harting (Université Côte d'Azur, CNRS, GREDEG, France; Bielefeld University, Germany); Michal Neugart (Technical University of Darmstadt, Germany) |
| Abstract: | Artificial intelligence algorithms are increasingly used for online pricing and are seen as a major threat to competitive markets. We show that if firms use a deep Q-network (DQN) as an example of a state-of-the-art machine learning algorithm, prices are supra-competitive in duopoly but quickly move to competitive prices as the number of competitors in an oligopoly increases. This finding is very robust concerning variations of the exploration and learning rate used in the DQN algorithm. |
| Keywords: | algorithmic price setting, deep Q-network, oligopoly, supracompetitive prices |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2024-32 |
| By: | Gustavo de Souza; Jacob S. Herbstman; Jack Mannion |
| Abstract: | We study how the demand for programming skills has impacted inequality. We create a new dataset with information on wages, employment, and software of Brazilian programmers, covering the period from the birth of information technology (IT) to the rise of artificial intelligence (AI). High-ability, high-wage, and highly educated individuals in key technology hubs are more likely to become programmers. Creating software boosts both wages and career prospects of programmers, especially for those with specialized skills in AI and cybersecurity. These wage gains are concentrated among top programmers, increasing inequality within the profession. Therefore, increased demand for specialized skills in programming has contributed to wage inequality both within the programming field and between programmers and other occupations. |
| Keywords: | technological progress; AI; software |
| JEL: | J23 J24 O33 |
| Date: | 2024–09–30 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedhwp:99304 |
| By: | Markus Leippold (University of Zurich; Swiss Finance Institute); Saeid Vaghefi (University of Zurich); Veruska Muccione (University of Zurich - Department of Geography; University of Geneva - Institute for Environmental Sciences); Julia Bingler (University of Oxford); Dominik Stammbach (ETH Zürich); Chiara Colesanti Senni (University of Zurich - Department of Finance); Jingwei Ni (ETH Zurich); Tobias Wekhof (ETH Zürich - CER-ETH - Center of Economic Research at ETH Zurich); Tingyu Yu (University of Zurich - Department Finance); Tobias Schimanski (University of Zurich); Glen Gostlow (University of Zurich - Department Finance); Jürg Luterbacher (World Health Organization (WHO) - World Health Organization, Geneva); Christian Huggel (University of Zurich) |
| Abstract: | This paper presents Climinator, a novel AI-based tool designed to automate the fact-checking of climate change claims. Utilizing an array of Large Language Models (LLMs) informed by authoritative sources like the IPCC reports and peer-reviewed scientific literature, Climinator employs an innovative Mediator-Advocate framework. This design allows Climinator to effectively synthesize varying scientific perspectives, leading to robust, evidence-based evaluations. Our model demonstrates remarkable accuracy when testing claims collected from Climate Feedback and Skeptical Science. Notably, when integrating an advocate with a climate science denial perspective in our framework, Climinator's iterative debate process reliably converges towards scientific consensus, underscoring its adeptness at reconciling diverse viewpoints into science-based, factual conclusions. While our research is subject to certain limitations and necessitates careful interpretation, our approach holds significant potential. We hope to stimulate further research and encourage exploring its applicability in other contexts, including political fact-checking and legal domains. |
| Date: | 2024–03 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2493 |
| By: | Chiara Colesanti Senni (University of Zurich - Department of Finance); Tobias Schimanski (University of Zurich); Julia Bingler (University of Oxford); Jingwei Ni (ETH Zurich); Markus Leippold (University of Zurich; Swiss Finance Institute) |
| Abstract: | Company transition plans toward a low-carbon economy are key for effective capital allocation and risk management. This paper proposes a set of 64 indicators to comprehensively assess transition plans and develops a Large Language Model-based tool to automate the assessment of company disclosures. We evaluate our tool with experts from 26 institutions, including financial regulators, investors, and non-governmental organizations. We apply the tool to the sustainability reports from carbon-intensive Climate Action 100+ companies. Our results show that companies tend to disclose more information related to target setting (talk), but fewer information related to the concrete implementation of strategies (walk). In addition, companies that disclose more information tend to have lower emissions. Our results highlight the need for increased scrutiny of companies' efforts and potential greenwashing risks. The complexity of transition activities presents a major challenge for comprehensive large-scale assessments. As shown in this paper, novel and flexible approaches using Large Language Models can serve as a remedy. |
| Keywords: | Climate disclosure, Large Language Models, RAG system, transition plans, human evaluation, CA100+ |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2492 |
| By: | Wendy E. Dunn; Raakin Kabir; Ellen E. Meade; Nitish R. Sinha |
| Abstract: | In an era increasingly shaped by artificial intelligence (AI), the public’s understanding of economic policy may be filtered through the lens of generative AI models (also called large language models or LLMs). Generative AI models offer the promise of quickly ingesting and interpreting large amounts of textual information. |
| Date: | 2024–12–06 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfn:2024-12-06-1 |
| By: | Ludovic Dibiaggio (SKEMA Business School); Lionel Nesta (Université Côte d'Azur, CNRS, GREDEG, France; SKEMA Business School; Sciences Po Paris, OFCE, France); Simone Vannuccini (Université Côte d'Azur, CNRS, GREDEG, France) |
| Abstract: | We present a first-of-its-kind empirical study of technological sovereignty in artificial intelligence, adopting a competence-based perspective. We use patents and publication data to map competencies across AI techniques, functions and applications, and develop a novel measure of integration based on relative specializations and complementarities. We argue that our measure approximates technological sovereignty by capturing local capabilities to innovate in AI. We use our novel measure to explain AI innovation, and unpack integration determinants. Our focus is on the European Union, given its lagging position yet key role in a global landscape increasingly characterized by growing rivalries and fragmentation. |
| Keywords: | Greening value chains, Firm internal markets failures, Transfer pricing, Fiscal compliance and the environment, environmental governance |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2024-34 |
| By: | Quimba, Francis Mark A.; Moreno, Neil Irwin S.; Salazar, Alliah Mae C. |
| Abstract: | This paper examines the current state of artificial intelligence (AI) adoption in Philippine businesses and industries, analyzing the barriers to adoption and evaluating the government's role in fostering AI-driven industrial development. Through an analysis of various AI readiness indices and case studies, the research finds that while basic digital infrastructure is widespread, with 90.8 percent of establishments having computers and 81 percent having internet access, advanced technology adoption remains limited. Only 14.9 percent of firms use AI technologies, with adoption concentrated in urban areas and larger firms, particularly in the ICT and BPO sectors. The study identifies key barriers including limited digital infrastructure, low awareness of AI technologies, significant skills gaps, and insufficient funding opportunities. Drawing from economic theory and international case studies, the paper outlines three critical domains for government intervention: market facilitation, capability building, and ecosystem coordination. The research proposes policy recommendations focusing on infrastructure development, human capital development, regulatory frameworks, public-private partnerships, and ethical guidelines. These recommendations emphasize the need for coordinated action across government agencies, substantial investment in digital infrastructure and education, and the establishment of clear governance frameworks to ensure responsible AI adoption while fostering innovation and competitiveness in the Philippine business sector. Comments on this paper are welcome within 60 days from the date of posting. Email publications@pids.gov.ph. |
| Keywords: | artificial intelligence;AI policy;industrial policy;AI adoption |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:phd:dpaper:dp_2024-35 |
| By: | Kaiji Chen; Mr. Yunhui Zhao |
| Abstract: | We construct a daily Chinese Housing Market Sentiment Index by applying GPT-4o to Chinese news articles. Our method outperforms traditional models in several validation tests, including a test based on a suite of machine learning models. Applying this index to household-level data, we find that after monetary easing, an important group of homebuyers (who have a college degree and are aged between 30 and 50) in cities with more optimistic housing sentiment have lower responses in non-housing consumption, whereas for homebuyers in other age-education groups, such a pattern does not exist. This suggests that current monetary easing might be more effective in boosting non-housing consumption than in the past for China due to weaker crowding-out effects from pessimistic housing sentiment. The paper also highlights the need for complementary structural reforms to enhance monetary policy transmission in China, a lesson relevant for other similar countries. Methodologically, it offers a tool for monitoring housing sentiment and lays out some principles for applying generative AI models, adaptable to other studies globally. |
| Keywords: | Chinese Housing Market Sentiment; Generative AI; Monetary Policy Transmission; Consumption; Crowding-Out |
| Date: | 2024–12–23 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/264 |
| By: | Munipalle, Pravith |
| Abstract: | Bot trading, or algorithmic trading, has transformed modern financial markets by using advanced technologies like artificial intelligence and machine learning to execute trades with unparalleled speed and efficiency. This paper examines the mechanisms and types of trading bots, their impact on market liquidity, efficiency, and stability, and the ethical and regulatory challenges they pose. Key findings highlight the dual nature of bot trading—enhancing market performance while introducing systemic risks, such as those observed during the 2010 Flash Crash. Emerging technologies like blockchain and predictive analytics, along with advancements in AI, present opportunities for innovation but also underscore the need for robust regulations and ethical design. To provide deeper insights, we conducted an experiment analyzing the performance of different trading bot strategies in simulated market conditions, revealing the potential and pitfalls of these systems under varying scenarios. |
| Date: | 2024–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:p98zv |
| By: | Mahalakshmi Manian (Research Scholar); Parthajit Kayal ((corresponding author), Assistant Professor Madras School of Economics, Chennai) |
| Abstract: | This research investigates the phenomenon of economic or financial bubbles within the Indian stock market context, characterized by pronounced asset price inflation exceeding the intrinsic worth of the underlying assets. Leveraging data from the NIFTY 500 index spanning the period 2003 to 2021, the study utilizes the Phillips, Shi, and Yu (PSY) method (Phillips et. al., 2015b), which employs a right-tailed unit root test, to discern the presence of financial bubbles. Subsequently, machine learning algorithms are employed to predict real-time occurrences of such bubbles. Analysis reveals the manifestation of financial bubbles within the Indian stock market notably in the years 2007 and 2017. Moreover, empirical evidence underscores the superior predictive efficacy of Artificial Neural Networks, Random Forest, and Gradient Boosting algorithms vis-à-vis conventional statistical methodologies in forecasting financial bubble occurrences within the Indian stock market. Policymakers should use advanced machine learning techniques for real-time financial bubble detection to improve regulation and mitigate market risks. |
| Keywords: | Financial Bubbles; Machine Learning; K-nearest Neighbour; Random Forest Classifier; Artificial Neural Network; Naïve Bayes |
| JEL: | G1 G2 G3 C1 C5 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:mad:wpaper:2024-270 |