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on Artificial Intelligence |
| By: | Shawn Berry |
| Abstract: | The use of online reviews to aid with purchase decisions is popular among consumers as it is a simple heuristic tool based on the reported experiences of other consumers. However, not all online reviews are written by real consumers or reflect actual experiences, and present implications for consumers and businesses. This study examines the effects of fake online reviews written by artificial intelligence (AI) on consumer decision making. Respondents were surveyed about their attitudes and habits concerning online reviews using an online questionnaire (n=351), and participated in a restaurant choice experiment using varying proportions of fake and real reviews. While the findings confirm prior studies, new insights are gained about the confusion for consumers and consequences for businesses when reviews written by AI are believed rather than real reviews. The study presents a fake review detection model using logistic regression modeling to score and flag reviews as a solution. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.11345&r=ain |
| By: | Mathieu Chevrier (Université Côte d'Azur, CNRS, GREDEG, France); Brice Corgnet (Emlyon Business School, GATE UMR 5824, France); Eric Guerci (Université Côte d'Azur, CNRS, GREDEG, France); Julie Rosaz (CEREN EA 7477, Burgundy School of Business, Université Bourgogne Franche-Comté, Dijon, France) |
| Abstract: | This study examines algorithm credulity by which people rely on faulty algorithmic advice without critical evaluation. Using a prediction task comparing human and algorithm advisors, we find that participants are more likely to follow the same deficient advice when issued by an algorithm than by a human. We show that algorithm credulity reduces expected earnings by 13%. To explain this finding, we propose the Algo-Intelligibility-Credulity Model, which posits that people are more likely to perceive as intelligible an unpredictable and deficient piece of advice when produced by an algorithm than by a human. These results imply that humans might be particularly susceptible to the influence of malicious algorithmic advice, potentially due to limitations in our evolved epistemic vigilance when applied to interactions with automated agents. |
| Keywords: | Algorithm credulity, algorithmic advice, intelligibility, trust, laboratory experiments |
| JEL: | C92 D91 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:gre:wpaper:2024-03&r=ain |
| By: | Marie Obidzinski (Université Paris Panthéon Assas, CRED UR 7321, 75005 Paris, France); Yves Oytana (CRESE UR3190, Univ. Bourgogne Franche-Comté, F-25000 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 assumption is that users are subject 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 trade-off 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: | liability rules, artificial intelligence, inattention |
| JEL: | K4 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:crb:wpaper:2024-08&r=ain |
| By: | Wadim Strielkowski |
| Abstract: | Artificial-intelligence tools in research like ChatGPT are playing an increasingly transformative role in revolutionizing scientific publishing and re-shaping its economic background. They can help academics to tackle such issues as limited space in academic journals, accessibility of knowledge, delayed dissemination, or the exponential growth of academic output. Moreover, AI tools could potentially change scientific communication and academic publishing market as we know them. They can help to promote Open Access (OA) in the form of preprints, dethrone the entrenched journals and publishers, as well as introduce novel approaches to the assessment of research output. It is also imperative that they should do just that, once and for all. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.14952&r=ain |
| By: | Yvonne Giesing |
| Abstract: | We study the effects of technological change on immigration flows as well as the labor market outcomes of migrants versus natives. We analyse and compare the effects of two different automation technologies: Industrial robots and artificial intelligence. We exploit data provided by the Industrial Federation of Robotics as well as online job vacancy data on Germany, a highly automated economy and the main destination for migrants in Europe. We apply an instrumental variable strategy and identify how robots decrease the wage of migrants across all skill groups, while neither having a significant impact on the native population nor immigration flows. In the case of AI, we determine an increase in the wage gap as well as the unemployment gap of migrant and native populations. This applies to the low-, medium- and high-skilled and is indicative of migrants facing displacement effects, while natives might benefit from productivity and complementarity effects. In addition, AI leads to a significant inflow of immigrants. Policymakers should devote special attention to the migration population when designing mitigation policies in response to technological change to avoid further increases in inequality between migrants and natives. |
| Keywords: | technological change, AI, robots, immigration |
| JEL: | F22 J15 J61 J78 O15 O33 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10876&r=ain |
| By: | Gmyrek, Pawel,; Lutz, Christoph,; Newlands, Gemma, |
| Abstract: | Despite initial research about the biases and perceptions of Large Language Models (LLMs), we lack evidence on how LLMs evaluate occupations, especially in comparison to human evaluators. In this paper, we present a systematic comparison of occupational evaluations by GPT-4 with those from an in-depth, high-quality and recent human respondents survey in the United Kingdom. Covering the full ISCO-08 occupational landscape, with 580 occupations and two distinct metrics (prestige and social value), our findings indicate that GPT-4 and human scores are highly correlated across all ISCO-08 major groups. In absolute terms, GPT-4 scores are more generous than those of the human respondents. At the same time, GPT-4 substantially under or overestimates the occupational prestige and social value of many occupations, particularly for emerging digital and stigmatized occupations. |
| Keywords: | occupational classification, occupational qualification, artificial intelligence, survey |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ilo:ilowps:995347793502676&r=ain |
| By: | Aumaima Wahbi (Université Mohamed V de rabat-Maroc, Faculté des sciences juridiques, économiques et sociales de Rabat Souissi); Karim Khaddouj (Université Mohamed V de rabat-Maroc, ENSAM - École Nationale Supérieure des Arts et Métiers); Naoufal Lahlimi (Université Mohamed V de rabat-Maroc, Faculté des sciences juridiques, économiques et sociales de Rabat Souissi) |
| Abstract: | This article delves into the pivotal role of chatbots as key facilitators in enhancing the customer experience. Portrayed as providers of swift and convenient assistance, chatbots are considered major contributors to the transformation of customer interactions. Their impact is evident in real-time information delivery, execution of transactional operations, and efficient resolution of common issues. The central question addressed in the article is formulated as follows: "How do customer service chatbots influence user experience and satisfaction?" The primary objective is to significantly contribute to the understanding of this emerging phenomenon by conducting an in-depth assessment of existing literature and meticulously examining various factors influencing the adoption and utilization of chatbots. The research methodology employed relies on a comprehensive literature review, involving a critical analysis of 21 specifically selected articles for their relevance in the studied domain. This approach allows for embracing a variety of perspectives and offering a panoramic view of current trends. The consistent results presented in the article underscore that chatbots have a substantial positive impact on the customer experience. Their ability to provide rapid and personalized assistance is emphasized, along with their significant contribution to reducing human errors. Chatbots are thus recognized as valuable tools for optimizing the customer journey and enhancing overall satisfaction. In conclusion, the article advocates for a thoughtful design of chatbots in the tourism sector. This design should be centered around a deep understanding of specific customer needs and how chatbots can effectively complement human interactions. By implementing chatbots judiciously, businesses can aim to provide a comprehensive, consistent, and rewarding customer experience in the tourism sector. |
| Abstract: | Cet article explore le rôle des chatbots en tant que facilitateurs clés dans l'amélioration de l'expérience client. En mettant en avant leur capacité à fournir une assistance rapide et pratique, les chatbots sont considérés comme des acteurs majeurs dans la transformation de l'interaction client. Leur contribution se manifeste à travers la fourniture d'informations en temps réel, la réalisation d'opérations transactionnelles et la résolution efficace de problèmes courants. La question fondamentale de l'article est formulée comme suit : "Comment les chatbots du service client influent-ils sur l'expérience et la satisfaction des utilisateurs ?" L'objectif principal de l'article est de contribuer de manière significative à la compréhension de ce phénomène émergent en conduisant une évaluation approfondie de la littérature existante et en examinant attentivement les divers facteurs qui exercent une influence sur l'adoption et l'utilisation des chatbots. La méthodologie de recherche adoptée repose sur une revue de littérature minutieuse, impliquant l'analyse critique de 21 articles spécifiquement sélectionnés pour leur pertinence dans le domaine étudié. Cette approche permet d'embrasser une variété de perspectives et d'offrir une vision panoramique des tendances actuelles. Les résultats présentés dans l'article soulignent de manière cohérente que les chatbots ont un impact positif substantiel sur l'expérience client. Leur capacité à offrir une assistance rapide et personnalisée est mise en avant, tout comme leur contribution significative à la réduction des erreurs humaines. Les chatbots sont donc reconnus comme des outils précieux pour optimiser le parcours client et renforcer la satisfaction globale. En conclusion, l'article préconise une conception réfléchie des chatbots dans le secteur du tourisme. Cette conception devrait être axée sur la compréhension approfondie des besoins spécifiques des clients et sur la manière dont les chatbots peuvent complémenter efficacement les interactions humaines. Ainsi, en mettant en œuvre des chatbots de manière judicieuse, les entreprises peuvent viser à offrir une expérience client globale, cohérente et gratifiante dans le secteur du tourisme. |
| Keywords: | Artificial intelligence, Chatbot technology, Customer experience, Customer satisfaction, Customer service., Intelligence artificielle, technologie de chatbots, expérience client, satisfaction client, service client |
| Date: | 2023–12–29 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04403080&r=ain |
| By: | Guy Ben-Ishai; Jeff Dean; James Manyika; Ruth Porat; Hal Varian; Kent Walker |
| Abstract: | Recent progress in artificial intelligence (AI) marks a pivotal moment in human history. It presents the opportunity for machines to learn, adapt, and perform tasks that have the potential to assist people, from everyday activities to their most creative and ambitious projects. It could also help businesses and organizations harness knowledge, increase productivity, innovate, transform, and power shared prosperity. This tremendous potential raises two fundamental questions: (1) Will AI actually advance national and global economic transformation to benefit society at large? and (2) What issues must we get right to fully realize AI's economic value, expand prosperity and improve lives everywhere? We explore these questions by considering the recent history of technology and innovation as a guide for the likely impact of AI and what we must do to realize its economic potential to benefit society. While we do not presume the future will be entirely like that past, for reasons we will discuss, we do believe prior experience with technological change offers many useful lessons. We conclude that while progress in AI presents a historic opportunity to advance our economic prosperity and future wellbeing, its economic benefits will not come automatically and that AI risks exacerbating existing economic challenges unless we collectively and purposefully act to enable its potential and address its challenges. We suggest a collective policy agenda - involving developers, deployers and users of AI, infrastructure providers, policymakers, and those involved in workforce training - that may help both realize and harness AI's economic potential and address its risks to our shared prosperity. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.09718&r=ain |
| By: | Abraham Ramos Torres; Laura N Montoya |
| Abstract: | Artificial intelligence (AI) is rapidly transforming the global economy, and Latin America is no exception. In recent years, there has been a growing interest in AI development and implementation in the region. This paper presents a ranking of Latin American (LATAM) countries based on their potential to become emerging powers in AI. The ranking is based on three pillars: infrastructure, education, and finance. Infrastructure is measured by the availability of electricity, high-speed internet, the quality of telecommunications networks, and the availability of supercomputers. Education is measured by the quality of education and the research status. Finance is measured by the cost of investments, history of investments, economic metrics, and current implementation of AI. While Brazil, Chile, and Mexico have established themselves as major players in the AI industry in Latin America, our ranking demonstrates the new emerging powers in the region. According to the results, Argentina, Colombia, Uruguay, Costa Rica, and Ecuador are leading as new emerging powers in AI in Latin America. These countries have strong education systems, well-developed infrastructure, and growing financial resources. The ranking provides a useful tool for policymakers, investors, and businesses interested in AI development in Latin America. It can help to identify emerging LATAM countries with the greatest potential for AI growth and success. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.09056&r=ain |
| By: | Valentina Aparicio; Daniel Gordon; Sebastian G. Huayamares; Yuhuai Luo |
| Abstract: | Large language models (LLMs) are deep learning algorithms being used to perform natural language processing tasks in various fields, from social sciences to finance and biomedical sciences. Developing and training a new LLM can be very computationally expensive, so it is becoming a common practice to take existing LLMs and finetune them with carefully curated datasets for desired applications in different fields. Here, we present BioFinBERT, a finetuned LLM to perform financial sentiment analysis of public text associated with stocks of companies in the biotechnology sector. The stocks of biotech companies developing highly innovative and risky therapeutic drugs tend to respond very positively or negatively upon a successful or failed clinical readout or regulatory approval of their drug, respectively. These clinical or regulatory results are disclosed by the biotech companies via press releases, which are followed by a significant stock response in many cases. In our attempt to design a LLM capable of analyzing the sentiment of these press releases, we first finetuned BioBERT, a biomedical language representation model designed for biomedical text mining, using financial textual databases. Our finetuned model, termed BioFinBERT, was then used to perform financial sentiment analysis of various biotech-related press releases and financial text around inflection points that significantly affected the price of biotech stocks. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.11011&r=ain |
| By: | Henri Arno; Klaas Mulier; Joke Baeck; Thomas Demeester |
| Abstract: | In this paper, we present ECL, a novel multi-modal dataset containing the textual and numerical data from corporate 10K filings and associated binary bankruptcy labels. Furthermore, we develop and critically evaluate several classical and neural bankruptcy prediction models using this dataset. Our findings suggest that the information contained in each data modality is complementary for bankruptcy prediction. We also see that the binary bankruptcy prediction target does not enable our models to distinguish next year bankruptcy from an unhealthy financial situation resulting in bankruptcy in later years. Finally, we explore the use of LLMs in the context of our task. We show how GPT-based models can be used to extract meaningful summaries from the textual data but zero-shot bankruptcy prediction results are poor. All resources required to access and update the dataset or replicate our experiments are available on github.com/henriarnoUG/ECL. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.12652&r=ain |