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on Artificial Intelligence |
| By: | Daron Acemoglu; Ali Makhdoumi; Azarakhsh Malekian; Asuman Ozdaglar |
| Abstract: | We build a model of online behavioral manipulation driven by AI advances. A platform dynamically offers one of n products to a user who slowly learns product quality. User learning depends on a product’s “glossiness, ’ which captures attributes that make products appear more attractive than they are. AI tools enable platforms to learn glossiness and engage in behavioral manipulation. We establish that AI benefits consumers when glossiness is short-lived. In contrast, when glossiness is long-lived, users suffer because of behavioral manipulation. Finally, as the number of products increases, the platform can intensify behavioral manipulation by presenting more low-quality, glossy products. |
| JEL: | D83 D90 D91 L86 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31872&r=ain |
| By: | Martin Beraja; Andrew Kao; David Y. Yang; Noam Yuchtman |
| Abstract: | Can frontier innovation be sustained under autocracy? We argue that innovation and autocracy can be mutually reinforcing when: (i) the new technology bolsters the autocrat's power; and (ii) the autocrat's demand for the technology stimulates further innovation in applications beyond those benefiting it directly. We test for such a mutually reinforcing relationship in the context of facial recognition AI in China. To do so, we gather comprehensive data on AI firms and government procurement con-tracts, as well as on social unrest across China during the last decade. We first show that autocrats benefit from AI: local unrest leads to greater government procurement of facial recognition AI, and increased AI procurement suppresses subsequent unrest. We then show that AI innovation benefits from autocrats' suppression of unrest: the contracted AI firms innovate more both for the government and commercial markets. Taken together, these results suggest the possibility of sustained AI innovation under the Chinese regime: AI innovation entrenches the regime, and the regime's investment in AI for political control stimulates further frontier innovation. |
| Keywords: | artificial intelligence, autocracy, innovation, data, China, surveillance, political unrest |
| Date: | 2021–11–02 |
| URL: | http://d.repec.org/n?u=RePEc:cep:poidwp:020&r=ain |
| By: | Jiang, Jennifer; Lippert, Isabell; Alizadeh, Armin |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:141319&r=ain |
| By: | Nicolas Kusz (PRISM Sorbonne - Pôle de recherche interdisciplinaire en sciences du management - UP1 - Université Paris 1 Panthéon-Sorbonne, UP1 EMS - Université Paris 1 Panthéon-Sorbonne - École de Management de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne); Jean-François Lemoine (PRISM Sorbonne - Pôle de recherche interdisciplinaire en sciences du management - UP1 - Université Paris 1 Panthéon-Sorbonne, ESSCA Research Lab - ESSCA - Ecole Supérieure des Sciences Commerciales d'Angers) |
| Abstract: | More and more companies develop "voice app" integrated into Google and Alexa systems, to offer a new channel of interaction to their consumers in addition to their website and their smartphone application. With spectacular progress in voice recognition and synthetic voice technologies, the voice assistant makes it possible to establish a real dialogue between the human and the system. If the voice is no longer the privilege of humans, our research aims to explore the influence of the type of voice of a voice assistant (human versus artificial) on the cognitive reactions of consumers. Based on an exploratory qualitative study conducted with 15 people, the results suggest that the voice type of the assistant influences ease of use and perceived anthropomorphism by users. Furthermore, our study reveals that anthropomorphism has a negative impact on usability; the user seems to forget that he's talking to a machine and forget adjusting his requests accordingly. |
| Abstract: | Les « voice app » intégrées aux systèmes Google et Alexa se multiplient, de plus en plus d'entreprises proposent ce nouveau canal d'interaction à leurs consommateurs en complément de leur site web et de leur application smartphone. Avec les progrès majeurs des technologies de reconnaissance vocale et de voix de synthèse, l'assistant vocal permet d'instaurer un véritable dialogue entre l'homme et le système. Si la voix n'est désormais plus le privilège des humains, notre étude cherche à explorer les effets du type de voix d'un assistant vocal (humain versus artificiel) sur les réactions des consommateurs. En nous appuyant sur une étude qualitative exploratoire menée auprès de 15 répondants, les résultats suggèrent que le type de voix de l'assistant influence la facilité d'utilisation perçue et l'anthropomorphisme perçu par les consommateurs. En outre, notre étude révèle que l'anthropomorphisme a un impact négatif sur la facilité d'utilisation perçue ; l'utilisateur semble oublier qu'il s'adresse à une machine et qu'il doit ajuster ses requêtes en conséquence. Mots clefs : assistant vocal ; voix de synthèse ; facilité d'utilisation ; anthropomorphisme. |
| Keywords: | voice assistant, synthetic voice, trust, usability, anthropomorphism, Assistant vocal, Voix de synthèse, Facilité d’utilisation, Anthropomorphisme |
| Date: | 2023–10–11 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04281434&r=ain |
| By: | Pinski, Marc; Haas, Miguel; Franz, Anjuli |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:141320&r=ain |
| By: | adiid, hibanan |
| Abstract: | The integration of artificial intelligence (AI) in healthcare is poised to revolutionize diagnostics, treatment, and patient care. However, this rapid advancement raises ethical concerns related to patient privacy, data security, and the potential for bias in AI algorithms. This paper delves into the ethical implications of AI in healthcare, scrutinizing the fine balance between harnessing AI's potential for innovation and safeguarding patient privacy. Through an in-depth exploration of the ethical challenges and regulatory frameworks, this study strives to provide insights for stakeholders in healthcare, technology, and policy domains. |
| Date: | 2023–10–28 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:aw6g3&r=ain |
| By: | Gorwa, Robert; Veale, Michael (University College London) |
| Abstract: | The AI development community is increasingly making use of hosting intermediaries such as Hugging Face provide easy access to user-uploaded models and training data. These model marketplaces lower technical deployment barriers for hundreds of thousands of users, yet can be used in numerous potentially harmful and illegal ways. In this article, we argue that explain ways in which AI systems, which can both `contain' content and be open-ended tools, present one of the trickiest platform governance challenges seen to date. We provide case studies of several incidents across three illustrative platforms --- Hugging Face, GitHub and Civitai --- to examine how model marketplaces moderate models. Building on this analysis, we outline important (and yet nevertheless limited) practices that industry has been developing to respond to moderation demands: licensing, access and use restrictions, automated content moderation, and open policy development. While the policy challenge at hand is a considerable one, we conclude with some ideas as to how platforms could better mobilize resources to act as a careful, fair, and proportionate regulatory access point. |
| Date: | 2023–11–17 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:6dfk3&r=ain |
| By: | Jochen Wulf; Juerg Meierhofer |
| Abstract: | Research on the role of Large Language Models (LLMs) in business models and services is limited. Previous studies have utilized econometric models, technical showcases, and literature reviews. However, this research is pioneering in its empirical examination of the influence of LLMs at the firm level. The study introduces a detailed taxonomy that can guide further research on the criteria for successful LLM-based business model implementation and deepen understanding of LLM-driven business transformations. Existing knowledge on this subject is sparse and general. This research offers a more detailed business model design framework based on LLM-driven transformations. This taxonomy is not only beneficial for academic research but also has practical implications. It can act as a strategic tool for businesses, offering insights and best practices. Businesses can lev-erage this taxonomy to make informed decisions about LLM initiatives, ensuring that technology in-vestments align with strategic goals. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.05288&r=ain |
| By: | Alizadeh, Armin; Hirsch, Felix; Jiang, Jennifer; Wiener, Martin; Benlian, Alexander |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:141314&r=ain |
| By: | Nils Grashof; Alexander Kopka |
| Abstract: | Artificial intelligence (AI) is seen as a key technology for economic growth. However, the impact of AI on firm productivity has been under researched – particularly through the lens of inequality and clusters. Based on a unique sample of German firms, filling at least one patent between 2013 and 2019, we find evidence for a positive influence of AI on firm productivity. Moreover, our analysis shows that while AI knowledge does not contribute to productivity divergences in general, it increases the productivity gap between laggard and all other firms. Nevertheless, this effect is reduced through the localisation in clusters. |
| Keywords: | Artificial intelligence, Inequality, Productivity, Clusters, Patents, Firm-level |
| JEL: | O18 O30 R10 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:atv:wpaper:2304&r=ain |
| By: | Khaoula Naili (Université de Franche-Comté, CRESE, F-25000 Besançon, France) |
| Abstract: | We use AI methods to evaluate the accuracy of several standard machine learning models for predicting judicial decision outcomes. We highlight the key steps and challenges in predicting judicial outcomes by applying these models to a database of administrative court decisions.These findings significantly contribute to our understanding of the potential advantages of AI in the context of predictive justice. We utilize AI methods to analyze administrative court decisions sourced from the database provided by the French Council of State. This analysis has been made possible due to the Council of State’s decision to make its decisions publicly accessible since March 2022. Our innovative approach pioneers the use of prediction models on the open data from the French Council of State, addressing the complexities associated with data analysis. Our primary objective is to assess the accuracy of these models in predicting outcomes in French administrative tribunals and identify the most effective model for forecasting administrative tribunal court decisions. The selected models are trained and evaluated on multi-class datasets, where decisions are traditionally categorized into various classes. |
| Keywords: | artificial intelligence, machine learning, natural language processing, Predictive jus- tice, Legal text. |
| JEL: | K4 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:crb:wpaper:2023-06&r=ain |
| By: | Abdel-Karim, Benjamin M.; Benlian, Alexander; Hinz, Oliver |
| Abstract: | Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases. |
| Date: | 2023–11–20 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:141359&r=ain |
| By: | Badrane Nohayla (ENCGS - Ecole Nationale de Commerce et de Gestion de SETTAT); Bamousse Zineb (ENCGS - Ecole Nationale de Commerce et de Gestion de SETTAT) |
| Abstract: | In a turbulent, unpredictable and hyper-competitive environment, the performance and hence the sustainability of companies are drawing the international community's attention. In Morocco, performance is one of the most desirable objectives, and the path of innovation remains the vast area to be followed in order to boost this performance. In fact, while innovation has long been the preserve of Research and Development departments, the time has come to extend it to penetrate the cash management function as a performance catalyst. In this respect, the integration of artificial intelligence at the heart of cash management will be strategic to sustain the performance of Moroccan companies and maintain their survival. Thus, the objective of this research is to better understand the link between innovative cash management and the corporate financial performance through a rigorous analysis of the literature. In this regard, the findings reveal that artificial intelligence at the service of cash management remains a key springboard for growth. It is proving to be a valuable decision making tool aimed at supporting the development and boosting the performance of Moroccan companies in the national and international economic landscape |
| Abstract: | Dans un environnement turbulent, imprévisible et hyperconcurrentiel, la performance et partant la pérennité des entreprises éveillent l'intérêt de la communauté internationale. Par ailleurs, au Maroc, la performance se positionne comme l'une des objectifs tant souhaités et le chemin de l'innovation demeure le vaste chantier à emprunter afin de booster cette performance. En effet, si l'innovation a longtemps été la chasse gardée des départements Recherche et Développement, l'heure est venue de l'étendre pour pénétrer la fonction de la trésorerie en vue d'en faire un atout catalyseur de la performance. A ce propos, l'intégration de l'intelligence artificielle au cœur de la gestion de la trésorerie sera stratégique pour pérenniser la performance des entreprises marocaines et en maintenir la survie. Ainsi, l'objectif de la présente recherche est de mieux appréhender le lien entre une gestion de trésorerie innovante et la performance financière des entreprises à travers une analyse rigoureuse de la littérature. A cet égard, les résultats de cette recherche enseignent que l'intelligence artificielle au service de la gestion de la trésorerie demeure un ressort clé de la croissance. Elle s'affirme un outil d'aide à la décision précieux visant à soutenir le développement et stimuler la performance des entreprises marocaines dans le paysage économique national voire international. |
| Keywords: | Gestion de la trésorerie, Intelligence Artificielle, Performance financière, Innovation, Entreprises marocaines |
| Date: | 2023–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04280353&r=ain |
| By: | Christopher Mulenga; Joseph Phiri |
| Abstract: | Real estate valuations, especially the case of mass valuation where statistical analysis methods are applied. New methods of determination of real estate value should be explored. Artificial omputerizat provides an alternative for the omputer applied method of multiple linear regressions. The omputerization of real estate values has been in existence since the 2000s with the consideration of various artificial intelligence techniques which include Artificial Neural Network, fuzzy logic, generic algorithm, and expert system. Since most properties comprise of both physical and economic characteristics which renders the conventional valuation methods cumbersome. In order to counter these challenges, soft computing techniques with higher data handling capabilities maybe an optimum choice. |
| Keywords: | Artificial Intelligence; Fuzzy Logic; multiple regressions; statistical techniques |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:afr:wpaper:afres2023-024&r=ain |
| By: | Ikumo Isono (Economic Research Institute for ASEAN and East Asia (ERIA)); Hilmy Prilliadi (Economic Research Institute for ASEAN and East Asia (ERIA)) |
| Abstract: | Artificial intelligence (AI) is attracting significant attention worldwide in 2023 because of its potential to transform economies and societies. The Association of Southeast Asian Nations (ASEAN) must accelerate the debate on AI for five compelling reasons. First, narrowing the gaps in AI readiness within ASEAN is essential to share the benefits of AI equitably. Second, there are concerns that rapid advances in AI could result in job loss, and retraining is needed. Third, AI systems must be developed from an ASEAN-centric perspective to overcome prejudice and align AI with ASEAN values. Fourth, as developed countries implement AI regulations, ASEAN needs to consider the need for its own regional policies. Finally, now is the perfect time to discuss the positioning of AI in the regional framework as ASEAN’s digital integration initiative progresses. The paper discusses the significance of AI in 2023, the challenges in ASEAN, the need for its own policies, and policy recommendations. |
| Keywords: | Artificial Intelligence; ASEAN; Employment; Regulation; Ethics |
| JEL: | D78 F15 K23 O33 O38 |
| Date: | 2023–11–03 |
| URL: | http://d.repec.org/n?u=RePEc:era:wpaper:dp-2023-16&r=ain |
| By: | Zengqing Wu; Run Peng; Xu Han; Shuyuan Zheng; Yixin Zhang; Chuan Xiao |
| Abstract: | Computer simulations offer a robust toolset for exploring complex systems across various disciplines. A particularly impactful approach within this realm is Agent-Based Modeling (ABM), which harnesses the interactions of individual agents to emulate intricate system dynamics. ABM's strength lies in its bottom-up methodology, illuminating emergent phenomena by modeling the behaviors of individual components of a system. Yet, ABM has its own set of challenges, notably its struggle with modeling natural language instructions and common sense in mathematical equations or rules. This paper seeks to transcend these boundaries by integrating Large Language Models (LLMs) like GPT into ABM. This amalgamation gives birth to a novel framework, Smart Agent-Based Modeling (SABM). Building upon the concept of smart agents -- entities characterized by their intelligence, adaptability, and computation ability -- we explore in the direction of utilizing LLM-powered agents to simulate real-world scenarios with increased nuance and realism. In this comprehensive exploration, we elucidate the state of the art of ABM, introduce SABM's potential and methodology, and present three case studies (source codes available at https://github.com/Roihn/SABM), demonstrating the SABM methodology and validating its effectiveness in modeling real-world systems. Furthermore, we cast a vision towards several aspects of the future of SABM, anticipating a broader horizon for its applications. Through this endeavor, we aspire to redefine the boundaries of computer simulations, enabling a more profound understanding of complex systems. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06330&r=ain |
| By: | Mathias Kraus (University of Erlangen-Nuremberg); Julia Bingler (University of Oxford); Markus Leippold (University of Zurich; Swiss Finance Institute); Tobias Schimanski (University of Zurich); Chiara Colesanti Senni (ETH Zürich; University of Zurich); Dominik Stammbach (ETH Zürich); Saeid Vaghefi (University of Zurich); Nicolas Webersinke (Friedrich-Alexander-Universität Erlangen-Nürnberg) |
| Abstract: | Large language models (LLMs) have significantly transformed the landscape of artificial intelligence by demonstrating their ability to generate human-like text across diverse topics. However, despite their impressive capabilities, LLMs lack recent information and often employ imprecise language, which can be detrimental in domains where accuracy is crucial, such as climate change. In this study, we make use of recent ideas to harness the potential of LLMs by viewing them as agents that access multiple sources, including databases containing recent and precise information about organizations, institutions, and companies. We demonstrate the effectiveness of our method through a prototype agent that retrieves emission data from ClimateWatch (https://www.climatewatchdata.org/) and leverages general Google search. By integrating these resources with LLMs, our approach overcomes the limitations associated with imprecise language and delivers more reliable and accurate information in the critical domain of climate change. This work paves the way for future advancements in LLMs and their application in domains where precision is of paramount importance. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2399&r=ain |