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on Artificial Intelligence |
| By: | Daniel Garcia; Juha Tolvanen; Alexander K. Wagner |
| Abstract: | We study the interaction between algorithmic advice and human decisions using high-resolution hotel-room pricing data. We document that price setting frictions, arising from adjustment costs of human decision makers, induce a conflict of interest with the algorithmic advisor. A model of advice with costly price adjustments shows that, in equilibrium, algorithmic price recommendations are strategically biased and lead to suboptimal pricing by human decision makers. We quantify the losses from the strategic bias in recommendations using as structural model and estimate the potential benefits that would result from a shift to fully automated algorithmic pricing. |
| Keywords: | advice, algorithmic recommendations, human decisions, adjustment cost, delegation |
| JEL: | D22 D83 L13 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10849&r=ain |
| By: | Christoph Engel (Max Planck Institute for Research on Collective Goods); Max R. P. Grossmann (University of Cologne); Axel Ockenfels (University of Cologne, Max Planck Institute for Research on Collective Goods) |
| Abstract: | Large Language Models (LLMs) have the potential to profoundly transform and enrich experimental economic research. We propose a new software framework, “alter_ego†, which makes it easy to design experiments between LLMs and to integrate LLMs into oTree-based experiments with human subjects. Our toolkit is freely available at github.com/mrpg/ego. To illustrate, we run differently framed prisoner’s dilemmas with interacting machines as well as with human machine interaction. Framing effects in machine-only treatments are strong and similar to those expected from previous human-only experiments, yet less pronounced and qualitatively different if machines interact with human participants. |
| Keywords: | Software for experiments, large language models, humanmachine interaction, framing |
| JEL: | C91 C92 D91 O33 L86 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:mpg:wpaper:2024_01&r=ain |
| By: | Unver, Hamid Akin (Ozyegin University); Ertan, Arhan S. (Bogazici University) |
| Abstract: | Does acquiring artificial intelligence (A.I.) technologies from the U.S. or China render countries more authoritarian or technologically less advantageous? In this article, we explore to what extent importing A.I./high-tech from the U.S. and/or China goes parallel with importers’ a) democratization or autocratization, b) state capacity, and c) technological progress across a decade (2010–2020). Our work demonstrates that not only are Chinese A.I./high-tech exports not congruous with importers’ democratic backsliding, but autocratization attributed to Chinese A.I. is also visible in importers of U.S. [AH1] A.I. In addition, for most indicators, we do not observe any significant effect of acquiring A.I. from the U.S. or China on importers’ state capacity or technological progress across the same period. Instead, we find that the story has a global inequality dimension as Chinese exports are clustered around countries with a lower GDP per capita, whereas U.S. high-technology exports are clustered around relatively wealthier states with slightly weaker capacity over territorial control. Overall, the article empirically demonstrates the limitations of some of the prevalent policy discourses surrounding the global diffusion of A.I. and its contribution to democratization, state capacity, and technological development of importer nations. |
| Date: | 2023–12–17 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:ud94h&r=ain |
| By: | Mauro Cazzaniga; Ms. Florence Jaumotte; Longji Li; Mr. Giovanni Melina; Augustus J Panton; Carlo Pizzinelli; Emma J Rockall; Ms. Marina Mendes Tavares |
| Abstract: | Artificial Intelligence (AI) has the potential to reshape the global economy, especially in the realm of labor markets. Advanced economies will experience the benefits and pitfalls of AI sooner than emerging market and developing economies, largely due to their employment structure focused on cognitive-intensive roles. There are some consistent patterns concerning AI exposure, with women and college-educated individuals more exposed but also better poised to reap AI benefits, and older workers potentially less able to adapt to the new technology. Labor income inequality may increase if the complementarity between AI and high-income workers is strong, while capital returns will increase wealth inequality. However, if productivity gains are sufficiently large, income levels could surge for most workers. In this evolving landscape, advanced economies and more developed emerging markets need to focus on upgrading regulatory frameworks and supporting labor reallocation, while safeguarding those adversely affected. Emerging market and developing economies should prioritize developing digital infrastructure and digital skills |
| Keywords: | Artificial Intelligence; Labor Market; Job Displacement; Income Inequality; Advanced Economies; Emerging Market Economies; Low-Income Developing Countries; AI preparedness index; AI benefit; AI exposure; ICT employment share; AI adoption; Emerging and frontier financial markets; Income; Global; Africa |
| Date: | 2024–01–14 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfsdn:2024/001&r=ain |
| By: | Georgios Fatouros; Konstantinos Metaxas; John Soldatos; Dimosthenis Kyriazis |
| Abstract: | In the dynamic and data-driven landscape of financial markets, this paper introduces MarketSenseAI, a novel AI-driven framework leveraging the advanced reasoning capabilities of GPT-4 for scalable stock selection. MarketSenseAI incorporates Chain of Thought and In-Context Learning methodologies to analyze a wide array of data sources, including market price dynamics, financial news, company fundamentals, and macroeconomic reports emulating the decision making process of prominent financial investment teams. The development, implementation, and empirical validation of MarketSenseAI are detailed, with a focus on its ability to provide actionable investment signals (buy, hold, sell) backed by cogent explanations. A notable aspect of this study is the use of GPT-4 not only as a predictive tool but also as an evaluator, revealing the significant impact of the AI-generated explanations on the reliability and acceptance of the suggested investment signals. In an extensive empirical evaluation with S&P 100 stocks, MarketSenseAI outperformed the benchmark index by 13%, achieving returns up to 40%, while maintaining a risk profile comparable to the market. These results demonstrate the efficacy of Large Language Models in complex financial decision-making and mark a significant advancement in the integration of AI into financial analysis and investment strategies. This research contributes to the financial AI field, presenting an innovative approach and underscoring the transformative potential of AI in revolutionizing traditional financial analysis investment methodologies. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.03737&r=ain |
| By: | Patel, Rajesh; Khan, Fatima; Silva, Buddhika; Shaturaev, Jakhongir |
| Abstract: | This research paper examines the impact of Artificial Intelligence (AI) on the financial audit process and explores how it enhances auditing practices. The integration of AI technology in financial audits has the potential to revolutionize the profession by automating tasks, providing real-time analysis, enhancing risk assessment capabilities, and offering valuable insights. This research investigates the implications, benefits, challenges, and ethical considerations associated with AI integration in the audit process. The literature review reveals that AI improves audit efficiency by automating manual processes and reducing the time required for data analysis. AI-powered tools enable real-time analysis, enhancing risk assessment by detecting anomalies and potential fraud indicators promptly. AI algorithms also contribute to more accurate and informed decision-making by analyzing complex datasets and identifying patterns. Ethical considerations, such as fairness, transparency, and unbiased decision-making, must be addressed when integrating AI technology into audits. Based on the literature review, hypotheses are developed to test the relationships between AI and audit efficiency, risk assessment, audit quality, and decision-making. These hypotheses propose that AI integration improves audit efficiency, enhances risk assessment capabilities, facilitates more informed decision-making, and requires ethical considerations and collaboration with IT professionals for successful implementation. The findings and discussion emphasize that AI technology has significant potential implications for audit quality, efficiency, risk assessment, and decision-making. By leveraging AI's analytical capabilities, auditors can improve audit quality, proactively address risks, and make more accurate decisions. However, further empirical research is needed to validate these findings and address ethical considerations. Future research should focus on the long-term effects of AI on audit quality, explore ethical frameworks for AI integration, and examine auditors' technological skills and collaboration with IT professionals. |
| Keywords: | Artificial Intelligence; Audit; Transparency; Fraud; Financial Accounting |
| JEL: | M4 M42 M48 O3 O33 |
| Date: | 2023–08–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119616&r=ain |
| By: | Shivam Gupta (NEOMA - Neoma Business School); Sachin Modgil (IMI Kolkata - International Management Institute); Ajay Kumar (EM - emlyon business school); Uthayasankar Sivarajah (University of Bradford); Zahir Irani (University of Bradford) |
| Abstract: | "Natural disasters are often unpredictable and therefore there is a need for quick and effective response to save lives and infrastructure. Hence, this study is aimed at achieving timely, anticipated and effective response throughout the cycle of a disaster, extreme weather and emergency operations management with the help of advanced technologies. This study proposes a novel, evidence-based framework (4-AIDE) that highlights the role of artificial intelligence (AI) and cloud-based collaborative platforms in disaster, extreme weather and emergency situations. A qualitative approach underpinned by organizational information processing theory (OIPT) is employed to design, develop and conduct semi-structured interviews with 33 respondents having experience in AI and cloud computing industries during emergency and extreme weather situations. For analysing the collected data, axial, open and selective coding is used that further develop themes, propositions and an evidence-based framework. The study findings indicate that AI and cloud-based collaborative platforms offer a structured and logical approach to enable two-way, algorithm-based communication to collect, analyse and design effective management strategies for disaster and extreme weather situations. Managers of public systems or businesses can collect and analyse data to predict possible outcomes and take necessary actions in an extreme weather situation. Communities and societies can be more resilient by transmitting and receiving data to AI and cloud-based collaborative platforms. These actions can also help policymakers identify critical pockets and guide administration for their necessary preparation for unexpected, extreme weather, and emergency events." |
| Keywords: | Artificial intelligence, Cloud technologies, Disaster management, Extreme weather, Organizational information processing theory |
| Date: | 2022–09–22 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04325638&r=ain |