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on Artificial Intelligence |
| By: | Xiang Hui; Oren Reshef; Luofeng Zhou |
| Abstract: | Generative Artificial Intelligence (AI) holds the potential to either complement knowledge workers by increasing their productivity or substitute them entirely. We examine the short-term effects of the recent release of the large language model (LLM), ChatGPT, on the employment outcomes of freelancers on a large online platform. We find that freelancers in highly affected occupations suffer from the introduction of generative AI, experiencing reductions in both employment and earnings. We find similar effects studying the release of other image-based, generative AI models. Exploring the heterogeneity by freelancers’ employment history, we do not find evidence that high-quality service, measured by their past performance and employment, moderates the adverse effects on employment. In fact, we find suggestive evidence that top freelancers are disproportionately affected by AI. These results suggest that in the short term generative AI reduces overall demand for knowledge workers of all types, and may have the potential to narrow gaps among workers. |
| Keywords: | generative AI, large language model (LLM), online labor market |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10601&r=ain |
| By: | Gmyrek, Pawel,; Berg, Janine,; Bescond, David, |
| Abstract: | This study assesses the potential global exposure of occupations to Generative AI, particularly GPT-4. It predicts that the overwhelming effect of the technology will be to augment occupations, rather than to automate them. The greatest impact is likely to be in high and upper-middle income countries due to a higher share of employment in clerical occupations. As clerical jobs are an important source of female employment, the effects are highly gendered. Insights from this study underline the need for proactive policies that focus on job quality, ensure fair transitions, and that are based on dialogue and adequate regulation. |
| Keywords: | artificial intelligence, employment opportunity, working conditions, women workers, access to information technology, impact evaluation |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ilo:ilowps:995324892702676&r=ain |
| By: | Ajay K. Agrawal; John McHale; Alexander Oettl |
| Abstract: | We model a key step in the innovation process, hypothesis generation, as the making of predictions over a vast combinatorial space. Traditionally, scientists and innovators use theory or intuition to guide their search. Increasingly, however, they use artificial intelligence (AI) instead. We model innovation as resulting from sequential search over a combinatorial design space, where the prioritization of costly tests is achieved using a predictive model. We represent the ranked output of the predictive model in the form of a hazard function. We then use discrete survival analysis to obtain the main innovation outcomes of interest – the probability of innovation, expected search duration, and expected profit. We describe conditions under which shifting from the traditional method of hypothesis generation, using theory or intuition, to instead using AI that generates higher fidelity predictions, results in a higher likelihood of successful innovation, shorter search durations, and higher expected profits. We then explore the complementarity between hypothesis generation and hypothesis testing; potential gains from AI may not be realized without significant investment in testing capacity. We discuss the policy implications. |
| JEL: | O31 O33 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31558&r=ain |
| By: | Rama K. Malladi |
| Abstract: | This study evaluated three Artificial Intelligence (AI) large language model (LLM) enabled platforms - ChatGPT, BARD, and Bing AI - to answer an undergraduate finance exam with 20 quantitative questions across various difficulty levels. ChatGPT scored 30 percent, outperforming Bing AI, which scored 20 percent, while Bard lagged behind with a score of 15 percent. These models faced common challenges, such as inaccurate computations and formula selection. While they are currently insufficient for helping students pass the finance exam, they serve as valuable tools for dedicated learners. Future advancements are expected to overcome these limitations, allowing for improved formula selection and accurate computations and potentially enabling students to score 90 percent or higher. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.07979&r=ain |
| By: | Fabian Heymann (SFOE - Swiss Federal Office of Energy); Konstantinos Parginos (PERSEE - Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique); Ali Hariri (EPFL - Ecole Polytechnique Fédérale de Lausanne); Gabriele Franco (PANETTA Law Firm) |
| Abstract: | The growing prevalence and potential impact of artificial intelligence (AI) on society rises the need for regulation. In return, the shape of regulations will affect the application potential of AI across all economic sectors. This study compares the approaches to regulate AI in the European Union (EU), the United States (US) and China (CN). We then apply the findings of our comparative analysis on the energy sector, assessing the effects of each regulatory approach on the operation of a AI-based short-term electricity demand forecasting application. Our findings show that operationalizing AI applications will face very different challenges across geographies, with important implications for policy making and business development. |
| Keywords: | Artificial Intelligence, energy policy, load fore- casting, regulation |
| Date: | 2023–10–23 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04167091&r=ain |
| By: | Eleanor Toye Scott; Philip Stiles; Pradeep Debata (Cambridge Judge Business School, University of Cambridge) |
| Abstract: | Artificial intelligence (AI) and machine learning (ML) offer organisations expanding opportunities for greater control and efficiency and more timely and accurate results, but at the same time bring escalating emergent risks. Two significant and complementary approaches to the organisational challenges posed by AI and ML are model risk management (MRM) and enterprise risk management (ERM). In this review we identify the key literature on technology risk and organisations and use it to consider how effectively MRM policies nested within an ERM approach can resolve the risk conundrum created by the growing complexity of algorithmic technologies. We develop here a framework of the elements of MRM and ERM and the links between them. We first look at MRM and highlight four areas of model development (data, design, implementation and performance) and their associated risks. We then consider ERM and how digital technology implementation affects the entire organisation. We highlight the need to move away from a measurement and compliance approach to risk towards a broader and more proactive approach, aimed at organising technology risk, to which our MRM/ERM framework contributes. We argue that careful attention to the roles, aspirations and incentives of human operators and other stakeholders will be critical in making this transition successfully. Our review has implications for future research in several areas, including the design of human-machine hybrid systems, development of organisational best practice in managing risks arising from algorithmic bias, the design of effective government regulation for artificial intelligence and machine learning, and the role of algorithms in regulatory regimes. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:jbs:wpaper:202201&r=ain |
| By: | Abdulrezzak Zekiye; Semih Utku; Fadi Amroush; Oznur Ozkasap |
| Abstract: | Cryptocurrencies have become a popular and widely researched topic of interest in recent years for investors and scholars. In order to make informed investment decisions, it is essential to comprehend the factors that impact cryptocurrency prices and to identify risky cryptocurrencies. This paper focuses on analyzing historical data and using artificial intelligence algorithms on on-chain parameters to identify the factors affecting a cryptocurrency's price and to find risky cryptocurrencies. We conducted an analysis of historical cryptocurrencies' on-chain data and measured the correlation between the price and other parameters. In addition, we used clustering and classification in order to get a better understanding of a cryptocurrency and classify it as risky or not. The analysis revealed that a significant proportion of cryptocurrencies (39%) disappeared from the market, while only a small fraction (10%) survived for more than 1000 days. Our analysis revealed a significant negative correlation between cryptocurrency price and maximum and total supply, as well as a weak positive correlation between price and 24-hour trading volume. Moreover, we clustered cryptocurrencies into five distinct groups using their on-chain parameters, which provides investors with a more comprehensive understanding of a cryptocurrency when compared to those clustered with it. Finally, by implementing multiple classifiers to predict whether a cryptocurrency is risky or not, we obtained the best f1-score of 76% using K-Nearest Neighbor. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.08554&r=ain |
| By: | Dimitrios Vamvourellis; M\'at\'e Toth; Snigdha Bhagat; Dhruv Desai; Dhagash Mehta; Stefano Pasquali |
| Abstract: | Identifying companies with similar profiles is a core task in finance with a wide range of applications in portfolio construction, asset pricing and risk attribution. When a rigorous definition of similarity is lacking, financial analysts usually resort to 'traditional' industry classifications such as Global Industry Classification System (GICS) which assign a unique category to each company at different levels of granularity. Due to their discrete nature, though, GICS classifications do not allow for ranking companies in terms of similarity. In this paper, we explore the ability of pre-trained and finetuned large language models (LLMs) to learn company embeddings based on the business descriptions reported in SEC filings. We show that we can reproduce GICS classifications using the embeddings as features. We also benchmark these embeddings on various machine learning and financial metrics and conclude that the companies that are similar according to the embeddings are also similar in terms of financial performance metrics including return correlation. |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2308.08031&r=ain |
| By: | Nicolas Ameye; Jacques Bughin; Nicolas van Zeebroeck |
| Date: | 2023–08–25 |
| URL: | http://d.repec.org/n?u=RePEc:ulb:ulbeco:2013/362348&r=ain |
| By: | Zuzana Irsova (Charles University, Prague & Anglo-American University, Prague); Hristos Doucouliagos (Department of Economics and Deakin Laboratory for the Meta-Analysis of Research. Deakin University, Melbourne, Australia.); Tomas Havranek (Charles University, Prague & Centre for Economic Policy Research, London); T. D. Stanley (4Department of Economics and Deakin Laboratory for the Meta-Analysis of Research, Deakin University, Melbourne, Australia) |
| Abstract: | This paper provides concise, nontechnical, step-by-step guidelines on how to conduct a modern meta-analysis, especially in social sciences. We treat publication bias, p-hacking, and heterogeneity as phenomena meta-analysts must always confront. To this end, we provide concrete methodological recommendations. Meta-analysis methods have advanced notably over the last few years. Yet many meta-analyses still rely on outdated approaches, some ignoring publication bias and systematic heterogeneity. While limitations persist, recently developed techniques allow robust inference even in the face of formidable problems in the underlying empirical literature. The purpose of this paper is to summarize the state of the art in a way accessible to aspiring meta-analysts in any field. We also discuss how meta-analysts can use advances in artificial intelligence to work more efficiently. |
| Keywords: | meta-analysis, publication bias, p-hacking, artificial intelligence, model uncertainty |
| JEL: | C83 H52 I21 |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2023_25&r=ain |