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on Artificial Intelligence |
| By: | von Schenk, Alicia; Klockmann, Victor; Bonnefon, Jean-François; Rahwan, Iyad; Köbis, Nils |
| Abstract: | People are not very good at detecting lies, which may explain why they refrain from accusing others of lying, given the social costs attached to false accusations — both for the accuser and the accused. Here we consider how this social balance might be disrupted by the availability of lie-detection algorithms powered by Artificial Intelligence (AI). Will people elect to use lie-detection AI that outperforms humans, and if so, will they show less restraint in their accusations? To find out, we built a machine learning classifier whose accuracy (66.86%) was significantly better than human accuracy (46.47%) lie-detection task. We conducted an incentivized lie-detection experiment (N = 2040) in which we measured participants’ propensity to use the algorithm, as well as the impact of that use on accusation rates and accuracy. Our results reveal that (a) requesting predictions from the lie-detection AI and especially (b) receiving AI predictions that accuse others of lying increase accusation rates. Due to the low uptake of the algorithm (31.76% requests), we do not see an improvement in accuracy when the AI prediction becomes available for purchase. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:128163&r=ain |
| By: | Pranjal Rawat |
| Abstract: | This paper examines the impact of different payment rules on efficiency when algorithms learn to bid. We use a fully randomized experiment of 427 trials, where Q-learning bidders participate in up to 250, 000 auctions for a commonly valued item. The findings reveal that the first price auction, where winners pay the winning bid, is susceptible to coordinated bid suppression, with winning bids averaging roughly 20% below the true values. In contrast, the second price auction, where winners pay the second highest bid, aligns winning bids with actual values, reduces the volatility during learning and speeds up convergence. Regression analysis, incorporating design elements such as payment rules, number of participants, algorithmic factors including the discount and learning rate, asynchronous/synchronous updating, feedback, and exploration strategies, discovers the critical role of payment rules on efficiency. Furthermore, machine learning estimators find that payment rules matter even more with few bidders, high discount factors, asynchronous learning, and coarse bid spaces. This paper underscores the importance of auction design in algorithmic bidding. It suggests that computerized auctions like Google AdSense, which rely on the first price auction, can mitigate the risk of algorithmic collusion by adopting the second price auction. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.09437&r=ain |
| By: | Yurong Chen; Qian Wang; Zhijian Duan; Haoran Sun; Zhaohua Chen; Xiang Yan; Xiaotie Deng |
| Abstract: | In online ad markets, a rising number of advertisers are employing bidding agencies to participate in ad auctions. These agencies are specialized in designing online algorithms and bidding on behalf of their clients. Typically, an agency usually has information on multiple advertisers, so she can potentially coordinate bids to help her clients achieve higher utilities than those under independent bidding. In this paper, we study coordinated online bidding algorithms in repeated second-price auctions with budgets. We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding. We show that these algorithms achieve maximal coalition welfare and discuss bidders' incentives to misreport their budgets, in symmetric cases. Our proofs combine the techniques of online learning and equilibrium analysis, overcoming the difficulty of competing with a multi-dimensional benchmark. The performance of our algorithms is further evaluated by experiments on both synthetic and real data. To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.07709&r=ain |
| By: | Mehler, Maren F.; Vetter, Oliver A. |
| Abstract: | Machine Learning (ML) technologies have become the foundation of a plethora of products and services. While the economic potential of such ML-infused solutions has become irrefutable, there is still uncertainty on pricing. Currently, software testing is one area to benefit from ML services assisting in the creation of test cases; a task both complex and demanding human-like outputs. Yet, little is known on the willingness to pay of users, inhibiting the suppliers' incentive to develop suitable tools. To provide insights into desired features and willingness to pay for such ML-based tools, we perform a choice-based conjoint analysis with 119 participants in Germany. Our results show that a high level of accuracy is particularly important for users, followed by ease of use and integration into existing environments. Thus, we not only guide future developers on which attributes to prioritize but also which characteristics of ML-based services are relevant for future research. |
| Date: | 2023–06–14 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:138317&r=ain |
| By: | Hendriks, Patrick; Sturm, Timo; Olt, Christian M.; Buxmann, Peter |
| Abstract: | To make sense of their increasingly digital and complex environments, organizations strive for a future in which machine learning (ML) systems join humans in collaborative learning partnerships to complement each other’s learning capabilities. While these so-called artificial assistants enable their human partners (and vice versa) to gain insights about unique knowledge domains that would otherwise remain hidden from them, they may also disrupt and impede each other's learning. To explore the virtuous and vicious dynamics that affect organizational learning, we conduct a series of agent-based simulations of different learning modes between humans and artificial assistants in an organization. We find that aligning the learning of humans and artificial assistants and allowing them to influence each other’s learning processes equally leads to the highest organizational performance. |
| Date: | 2023–06–16 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:138376&r=ain |
| By: | Alain Lacroux (UP1 EMS - Université Paris 1 Panthéon-Sorbonne - École de Management de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne); Christelle Martin Lacroux (Univ. Grenoble Alpes, Grenoble INP) |
| Abstract: | Resume pre-screening assisted by decision support systems integrating artificial intelligence is currently undergoing a strong development in many organizations, raising technical, managerial, legal and ethical issues. This paper aims to better understand the reactions of recruiters when they are confronted with algorithm-based recommendations during the CV screening process. Two major attitudes have been identified in the literature on users' reactions to algorithm-based recommendations: algorithm aversion, which reflects a general distrust and preference for human recommendations; and automation bias, corresponding to an overconfidence in the decisions or recommendations made by algorithmic decision support systems (ADSS). Based on the results obtained in the field of automated decision support, we hypothesize in general that recruiters trust human experts more than algorithmic decision support systems because they distrust algorithms for subjective decisions such as hiring. An experimental study on resume selection was conducted on a sample of professionals (N=1, 100) who were asked to review a job offer and then evaluate two fictitious resumes in a 2×2 factorial design with the manipulation of the type of recommendation (no recommendation/algorithmic recommendation/human expert recommendation) and the relevance of recommendations (relevant vs. irrelevant recommendation). Our results support the general hypothesis of preference for human recommendations: recruiters demonstrate a higher level of trust in human expert recommendations compared to algorithmic recommendations. However, we also found that recommendation relevance has an unexpected differential impact on decisions: in the case of an irrelevant algorithmic recommendation, recruiters favored the least relevant resume over the best resume. This discrepancy between attitudes and behaviors suggests a possible automation bias. Our results also show that some specific personality traits (extraversion, neuroticism, and self-confidence) are associated with differential use of algorithmic recommendations. |
| Abstract: | La présélection des CV assistée par des systèmes d'aide à la décision intégrant l'intelligence artificielle connaît actuellement un fort développement dans de nombreuses organisations, soulevant des questions techniques, managériales, juridiques et éthiques. L'objectif de la présente communication vise à mieux comprendre les réactions des recruteurs lorsqu'ils se voient proposer des recommandations basées sur des algorithmes lors de la présélection des CV. Deux attitudes majeures ont été identifiées dans la littérature sur les réactions des utilisateurs aux recommandations basées sur des algorithmes : l'aversion pour les algorithmes, qui reflète une méfiance générale et une préférence pour les recommandations humaines ; et le biais d'automation, qui correspond à une confiance excessive dans les décisions ou les recommandations faites par les systèmes algorithmiques d'aide à la décision (ADSS). En s'appuyant sur les résultats obtenus dans le domaine de l'aide à la décision automatisée, nous faisons l'hypothèse générale que les recruteurs font plus confiance aux experts humains qu'aux systèmes algorithmiques d'aide à la décision, car ils se méfient des algorithmes pour des décisions subjectives comme le recrutement. Une expérimentation sur la sélection des CV a été menée sur un échantillon de professionnels (N=1 100) auxquels il a été demandé d'étudier une offre d'emploi, puis d'évaluer deux CV fictifs dans un plan factoriel 2×2 avec manipulation du type de recommandation (pas de recommandation / recommandation algorithmique / recommandation d'un expert humain) et de la pertinence des recommandations (recommandation pertinente vs non pertinente). Nos résultats confirment l'hypothèse générale de préférence pour les recommandations humaines : les recruteurs font preuve d'un niveau de confiance plus élevé envers les recommandations d'experts humains par rapport aux recommandations algorithmiques. Cependant, nous avons également constaté que la pertinence de la recommandation a un impact différentiel et inattendu sur les décisions : en présence d'une recommandation algorithmique non pertinente, les recruteurs ont favorisé le CV le moins pertinent par rapport au meilleur CV. Ce décalage entre les attitudes et les comportements suggère un possible biais d'automation. Nos résultats montrent également que des traits de personnalité spécifiques (extraversion, neuroticisme et confiance en soi) sont associés à une utilisation différentielle des recommandations algorithmiques. Les implications pour la recherche et les politiques RH sont enfin discutées. |
| Keywords: | Personnel selection, Artficial Intelligence, Human resource management, Automation biais, Algorithm aversion, recrutement, intelligence artificielle, gestion des ressources humaines |
| Date: | 2022–10–19 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04095500&r=ain |
| By: | Tania Babina; Anastassia Fedyk; Alex X. He; James Hodson |
| Abstract: | We study the shifts in U.S. firms' workforce composition and organization associated with the use of AI technologies. To do so, we leverage a unique combination of worker resume and job postings datasets to measure firm-level AI investments and workforce composition variables, such as educational attainment, specialization, and hierarchy. We document that firms with higher initial shares of highly-educated workers and STEM workers invest more in AI. As firms invest in AI, they tend to transition to more educated workforces, with higher shares of workers with undergraduate and graduate degrees, and more specialization in STEM fields and IT skills. Furthermore, AI investments are associated with a flattening of the firms' hierarchical structure, with significant increases in the share of workers at the junior level and decreases in shares of workers in middle-management and senior roles. Overall, our results highlight that adoption of AI technologies is associated with significant reorganization of firms' workforces. |
| JEL: | D22 E22 J01 J23 J24 |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31325&r=ain |
| By: | Albanesi, Stefania (University of Pittsburgh); Dias da Silva, António (European Central Bank); Jimeno, Juan F. (Bank of Spain); Lamo, Ana (European Central Bank); Wabitsch, Alena (University of Oxford) |
| Abstract: | We examine the link between labour market developments and new technologies such as artificial intelligence (AI) and software in 16 European countries over the period 2011- 2019. Using data for occupations at the 3-digit level in Europe, we find that on average employment shares have increased in occupations more exposed to AI. This is particularly the case for occupations with a relatively higher proportion of younger and skilled workers. This evidence is in line with the Skill Biased Technological Change theory. While there exists heterogeneity across countries, only very few countries show a decline in employment shares of occupations more exposed to AI-enabled automation. Country heterogeneity for this result seems to be linked to the pace of technology diffusion and education, but also to the level of product market regulation (competition) and employment protection laws. In contrast to the findings for employment, we find little evidence for a relationship between wages and potential exposures to new technologies. |
| Keywords: | artificial intelligence, employment, skills, occupations |
| JEL: | J23 O33 |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16227&r=ain |
| By: | DUCH BROWN Nestor (European Commission - JRC); GOMEZ LOSADA Alvaro; MIGUEZ Sebastián; ROSSETTI Fiammetta; VAN ROY Vincent |
| Abstract: | This report provides an overview of the robotics industry in Europe, as well as a description of the definitions, typologies and main differences between industrial and service robots. The aim is to build up a stronger and updated knowledge of research questions, approaches and data that scholars and policy makers could use to study robotics around the world, and more specifically in Europe. It also identifies the necessary actions to merge heterogeneous data into a meaningful and consistent dataset to estimate the EU shares of robotics from the demand and supply perspectives, and for both industrial and service robots. Complementing these data with other sources to enhance the value and significance of the overall estimation exercise of the EU robotics market shares, provides a comprehensive overview of the production and adoption sides for both industrial and service robots. The three main objectives of the report are: to build a dataset including the market shares of robots in the EU; to describe the main trends that can be extracted from data; and, to sketch a conceptual framework to contextualise the results from the first two objectives. |
| Keywords: | industrial robots, service robots, robotics value chain |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc132724&r=ain |
| By: | Zihan Chen; Lei Nico Zheng; Cheng Lu; Jialu Yuan; Di Zhu |
| Abstract: | ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier. In this research, we introduce a novel framework that leverages ChatGPT's graph inference capabilities to enhance Graph Neural Networks (GNN). Our framework adeptly extracts evolving network structures from textual data, and incorporates these networks into graph neural networks for subsequent predictive tasks. The experimental results from stock movement forecasting indicate our model has consistently outperformed the state-of-the-art Deep Learning-based benchmarks. Furthermore, the portfolios constructed based on our model's outputs demonstrate higher annualized cumulative returns, alongside reduced volatility and maximum drawdown. This superior performance highlights the potential of ChatGPT for text-based network inferences and underscores its promising implications for the financial sector. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.03763&r=ain |
| By: | Hongyang Yang; Xiao-Yang Liu; Christina Dan Wang |
| Abstract: | Large language models (LLMs) have shown the potential of revolutionizing natural language processing tasks in diverse domains, sparking great interest in finance. Accessing high-quality financial data is the first challenge for financial LLMs (FinLLMs). While proprietary models like BloombergGPT have taken advantage of their unique data accumulation, such privileged access calls for an open-source alternative to democratize Internet-scale financial data. In this paper, we present an open-source large language model, FinGPT, for the finance sector. Unlike proprietary models, FinGPT takes a data-centric approach, providing researchers and practitioners with accessible and transparent resources to develop their FinLLMs. We highlight the importance of an automatic data curation pipeline and the lightweight low-rank adaptation technique in building FinGPT. Furthermore, we showcase several potential applications as stepping stones for users, such as robo-advising, algorithmic trading, and low-code development. Through collaborative efforts within the open-source AI4Finance community, FinGPT aims to stimulate innovation, democratize FinLLMs, and unlock new opportunities in open finance. Two associated code repos are \url{https://github.com/AI4Finance-Foundation/FinGPT} and \url{https://github.com/AI4Finance-Found ation/FinNLP} |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.06031&r=ain |
| By: | Inssafe Bachir (Université Mohamed 5-Agdal); Abdnbi El Marzouki (Université Mohamed V de rabat-Maroc) |
| Abstract: | The use of artificial intelligence has become increasingly a necessity in all areas of life. The field of finance and the stock exchange was also affected by the major advantages presented by the latter, from the speed of execution to the simplification of processes and transactions. Consequently, in this article, we intend to infer the development of financial technologies, often mentioned in brief by finTech in the Moroccan financial sector, by adopting a theoretical analysis method, to examine the place of this new industry in our country. This method consists of detecting the challenges and opportunities of this new industry and studying the internal and external environment of artificial intelligence in Morocco. Based on a review of theoretical and empirical literature, this article reveals the use of artificial intelligence in the financial sector specifically in the stock market. Also relying on theories that put the relationship between artificial intelligence and the financial market. The central objective is to focus on the factors that influence, negatively and positively the rise of these technologies in the Moroccan stock market. Our study explores in another aspect the progress of artificial intelligence in the financial sector, its main stock market strategies and its revolution in the Moroccan stock market. The findings revealed the power of artificial intelligence as a tool for improving the productivity and efficiency of the financial market. As well as they revealed the steady steps of Morocco towards the transformation and technologicaldevelopment of the financial sector, which are explained by the intense efforts provided, and by the necessary measures taken to overcome all the obstacles. |
| Abstract: | De nos jours, l'usage de l'intelligence artificielle est devenu de plus en plus une nécessité dans tous les domaines de la vie. Le domaine de la finance et de la bourse également été touchés par les avantages majeurs présenter par cette dernière, dès la rapidité de l'exécution jusqu'à la simplification des processus et des transactions. En conséquence, dans cet article, nous avons l'intention d'inférer le développement des technologies financière, souvent mentionnée en bref par finTech dans le secteur financier Marocain, en adoptant une méthode d'analyse théorique, pour examiner la place de cette nouvelle industrie a notre pays. Cette méthode consiste à détecter les challenges et les opportunités de cette nouvelle industrie et d'étudier l'environnement interne et externe d'intelligence artificielle au Maroc. Cet article dévoile en se basant sur une revue de littérature théorique et empirique, l'utilisation d'intelligence artificielle au secteur financier précisément en marché boursier. En s'appuyant aussi sur les théories qui mis la relation entre l'intelligence artificielle et le marché financier. L'objectif central est de mettre le point sur les facteurs qui influent, négativement et positivement l'essor de ces technologies dans le marché boursier marocain. Notre étude explore dans un autre volet le progrès de l'intelligence artificielle dans le secteur financier, ses principales stratégies boursières ainsi que sa révolution dans la bourse marocaine. Les conclusions ont révélé, la puissance d'intelligence artificielle comme un outil d'amélioration de la productivité et l'efficacité du marché financier. Ainsi qu'ils ont révélé les pas réguliers du Maroc envers la transformation et le développement technologique du secteur financier, qui s'expliquent par les efforts intenses fournis, et par les mesures nécessaires prisent pour dépasser tous les obstacles. |
| Keywords: | Artificial intelligence, financial sector, stock market, finTech, Morocco., Intelligence artificielle, secteur financier, marché boursier, Maroc. |
| Date: | 2023–04–26 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04089635&r=ain |
| By: | Dylan Brewer (Georgia Institute of Technology); Alyssa Carlson (Department of Economics, University of Missouri) |
| Abstract: | We study approaches for adjusting machine learning methods when the training sample differs from the prediction sample on unobserved dimensions. The machine learning literature predominately assumes selection only on observed dimensions. Common approaches are to weight or include variables that influence selection as solutions to selection on observables. Simulation results show that selection on unobservables increases mean squared prediction error using popular machine-learning algorithms. Common machine learning practices such as weighting or including variables that influence selection into the training or prediction sample often worsens sample selection bias. We propose two control-function approaches that remove the effects of selection bias before training and find that they reduce mean-squared prediction error in simulations. We apply these approaches to predicting the vote share of the incumbent in gubernatorial elections using previously observed re-election bids. We find that ignoring selection on unobservables leads to substantially higher predicted vote shares for the incumbent than when the control function approach is used. |
| Keywords: | sample selection, machine learning, control function, inverse probability weighting |
| JEL: | C13 C31 C55 D72 |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:umc:wpaper:2310&r=ain |
| By: | Kucklick (Paderborn University); Priefer (Paderborn University); Beverungen (Paderborn University); Müller (Paderborn University) |
| Abstract: | Information systems have proven their value in facilitating pricing decisions. Still, predicting prices for complex goods remains challenging due to information asymmetries. Beyond Search qualities that sellers can identify ex-ante of a purchase, these goods possess Experience qualities only identifiable ex-post. While research has discussed how information asymmetries cause market failure, it remains unclear what benefits Search and Experience qualities offer for information systems that enable pricing on online markets. In a Machine Learning-based study, we quantify their predictive power for online real estate pricing. We use Geographic Information Systems and Computer Vision to incorporate spatial and image data into a Machine Learning algorithm for price prediction. We find that these secondary use data can transform Experience qualities to Search qualities, increasing the predictive power by up to 15.4%. Our results suggest that secondary use data can provide valuable resources for improving the predictive power of pricing complex goods. |
| Keywords: | information asymmetries, real estate appraisal; SEC theory; Machine Learning; Geographic Information Systems, Computer Vision |
| JEL: | C45 R32 R00 |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:pdn:dispap:112&r=ain |
| By: | Shadi Haj-Yahia; Omar Mansour; Tomer Toledo |
| Abstract: | Discrete choice models (DCM) are widely employed in travel demand analysis as a powerful theoretical econometric framework for understanding and predicting choice behaviors. DCMs are formed as random utility models (RUM), with their key advantage of interpretability. However, a core requirement for the estimation of these models is a priori specification of the associated utility functions, making them sensitive to modelers' subjective beliefs. Recently, machine learning (ML) approaches have emerged as a promising avenue for learning unobserved non-linear relationships in DCMs. However, ML models are considered "black box" and may not correspond with expected relationships. This paper proposes a framework that expands the potential of data-driven approaches for DCM by supporting the development of interpretable models that incorporate domain knowledge and prior beliefs through constraints. The proposed framework includes pseudo data samples that represent required relationships and a loss function that measures their fulfillment, along with observed data, for model training. The developed framework aims to improve model interpretability by combining ML's specification flexibility with econometrics and interpretable behavioral analysis. A case study demonstrates the potential of this framework for discrete choice analysis. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2306.00016&r=ain |
| By: | Sebastian Galiani; Ramiro H. Gálvez; Ian Nachman |
| Abstract: | This article presents a comprehensive analysis of trends in the publication and citation of economics scholarly research, with a focus on specialization within fields of economics research (i.e., applied, applied theory, econometrics methods, and theory). We collected detailed data on 24, 273 articles published from 1970 to 2016 in highly regarded general research economics journals. We then used state-of-the-art machine learning and natural language processing techniques to further enrich the collected data. Our findings reveal significant disparities in article content and citations across fields of economics research. The analysis indicates growing specialization trends in theory and econometric methods. In contrast, applied papers are covering a wider range of topics and receiving an increasing proportion of extramural citations over time. By 2016, applied ranked among the most or second most cited field by any other field of economics research. These patterns are consistent with applied papers becoming more multidisciplinary. Applied theory articles have also demonstrated a growing breadth of topics covered (similar to applied articles); however, this has not been accompanied by an increase in extramural citations or in the share of citations received from other fields of economics research (as observed with theory articles). This makes it challenging to determine their specialization status. |
| JEL: | A1 |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31295&r=ain |