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on Artificial Intelligence |
| By: | Shuo Zhang; Peter J. Kuhn |
| Abstract: | We audit the job recommender algorithms used by four Chinese job boards by creating fictitious applicant profiles that differ only in their gender. Jobs recommended uniquely to the male and female profiles in a pair differ modestly in their observed characteristics, with female jobs advertising lower wages, requesting less experience, and coming from smaller firms. Much larger differences are observed in these ads’ language, however, with women’s jobs containing 0.58 standard deviations more stereotypically female content than men’s. Using our experimental design, we can conclude that these gender gaps are generated primarily by content-based matching algorithms that use the worker’s declared gender as a direct input. Action-based processes like item-based collaborative filtering and recruiters’ reactions to workers’ resumes contribute little to these gaps. |
| JEL: | C99 J16 J71 M50 O33 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32889 |
| By: | Shiqi Fang; Zexun Chen; Jake Ansell |
| Abstract: | This paper introduces a novel framework, "peer-induced fairness", to scientifically audit algorithmic fairness. It addresses a critical but often overlooked issue: distinguishing between adverse outcomes due to algorithmic discrimination and those resulting from individuals' insufficient capabilities. By utilizing counterfactual fairness and advanced causal inference techniques, such as the Single World Intervention Graph, this model-agnostic approach evaluates fairness at the individual level through peer comparisons and hypothesis testing. It also tackles challenges like data scarcity and imbalance, offering a flexible, plug-and-play self-audit tool for stakeholders and an external audit tool for regulators, while providing explainable feedback for those affected by unfavorable decisions. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.02558 |
| By: | Ning Li; Huaikang Zhou; Mingze Xu |
| Abstract: | This study explores the potential of Large Language Models (LLMs), specifically GPT-4, to enhance objectivity in organizational task performance evaluations. Through comparative analyses across two studies, including various task performance outputs, we demonstrate that LLMs can serve as a reliable and even superior alternative to human raters in evaluating knowledge-based performance outputs, which are a key contribution of knowledge workers. Our results suggest that GPT ratings are comparable to human ratings but exhibit higher consistency and reliability. Additionally, combined multiple GPT ratings on the same performance output show strong correlations with aggregated human performance ratings, akin to the consensus principle observed in performance evaluation literature. However, we also find that LLMs are prone to contextual biases, such as the halo effect, mirroring human evaluative biases. Our research suggests that while LLMs are capable of extracting meaningful constructs from text-based data, their scope is currently limited to specific forms of performance evaluation. By highlighting both the potential and limitations of LLMs, our study contributes to the discourse on AI role in management studies and sets a foundation for future research to refine AI theoretical and practical applications in management. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.05328 |
| By: | Keppeler, Florian; Borchert, Jana; Pedersen, Mogens Jin; Nielsen, Vibeke Lehmann |
| Abstract: | Artificial Intelligence (AI) applications are transforming public sector decision-making. However, most research conceptualizes AI as a form of specialized algorithmic decision support tool. In contrast, this study introduces the concept of human-AI ensembles, where humans and AI tackle the same tasks together, rather than specializing in certain parts. We argue that this is particularly relevant for many public sector decisions, where neither human nor AI-based decision-making has a clear advantage over the other in terms of legitimacy, efficacy, or legality. We illustrate this design theory within access to public employment, focusing on two key areas: (a) the potential of ensembling human and AI to reduce biases and (b) the inclinations of public managers to use AI advice. Study 1 presents evidence from the assessment of real-life job candidates (n = 2, 000) at the intersection of gender and ethnicity by public managers compared to AI. The results indicate that ensembled decision- making may alleviate ethnic biases. Study 2 examines how receptive public managers are to AI advice. Results from a pre-registered survey experiment involving managers (n = 538 with 4 observations each) show that decision-makers, when reminded of the unlawfulness of hiring discrimination, prioritize AI advice over human advice. |
| Date: | 2024–08–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:2yf6r |
| By: | Raphael Auer; David Köpfer; Josef Sveda |
| Abstract: | How exposed is the labour market to ever-advancing AI capabilities, to what extent does this substitute human labour, and how will it affect inequality? We address these questions in a simulation of 711 US occupations classified by the importance and level of cognitive skills. We base our simulations on the notion that AI can only perform skills that are within its capabilities and involve computer interaction. At low AI capabilities, 7% of skills are exposed to AI uniformly across the wage spectrum. At moderate and high AI capabilities, 17% and 36% of skills are exposed on average, and up to 45% in the highest wage quartile. Examining complementary versus substitution, we model the impact on side versus core occupational skills. For example, AI capable of bookkeeping helps doctors with administrative work, freeing up time for medical examinations, but risks the jobs of bookkeepers. We find that low AI capabilities complement all workers, as side skills are simpler than core skills. However, as AI capabilities advance, core skills in lower-wage jobs become exposed, threatening substitution and increased inequality. In contrast to the intuitive notion that the rise of AI may harm white-collar workers, we find that those remain safe longer as their core skills are hard to automate. |
| Keywords: | labour market, artificial intelligence, employment, inequality, automation, ChatGPT, GPT, LLM, wage, technology |
| JEL: | E24 E51 G21 G28 J23 J24 M48 O30 O33 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1207 |
| By: | Giacomo Damioli; Vincent Van Roy; Daniel Vertesy; Marco Vivarelli |
| Abstract: | Artificial intelligence (AI) is emerging as a transformative innovation with the potential to drive significant economic growth and productivity gains. This study examines whether AI is initiating a technological revolution, signifying a new technological paradigm, using the perspective of evolutionary neo-Schumpeterian economics. Using a global dataset combining information on AI patenting activities and their applicants between 2000 and 2016, our analysis reveals that AI patenting has accelerated and substantially evolved in terms of its pervasiveness, with AI innovators shifting from the ICT core industries to non-ICT service industries over the investigated period. Moreover, there has been a decrease in concentration of innovation activities and a reshuffling in the innovative hierarchies, with innovative entries and young and smaller applicants driving this change. Finally, we find that AI technologies play a role in generating and accelerating further innovations (so revealing to be “enabling technologies”, a distinctive feature of GPTs). All these features have characterised the emergence of major technological paradigms in the past and suggest that AI technologies may indeed generate a paradigmatic shift. |
| Keywords: | Artificial Intelligence, Patents, Structural Change, Technological Paradigm |
| Date: | 2024–08–14 |
| URL: | https://d.repec.org/n?u=RePEc:ete:msiper:746877 |
| By: | D’Alessandro, Francesco (Università Cattolica del Sacro Cuore); Santarelli, Enrico (University of Bologna); Vivarelli, Marco (Università Cattolica del Sacro Cuore) |
| Abstract: | In this paper we integrate the insights of the Knowledge Spillover Theory of Entrepreneurship and Innovation (KSTE+I) with Schumpeter's idea that innovative entrepreneurs creatively apply available local knowledge, possibly mediated by Marshallian, Jacobian and Porter spillovers. In more detail, in this study we assess the degree of pervasiveness and the level of opportunities brought about by AI technologies by testing the possible correlation between the regional AI knowledge stock and the number of new innovative ventures (that is startups patenting in any technological field in the year of their foundation). Empirically, by focusing on 287 Nuts-2 European regions, we test whether the local AI stock of knowledge exerts an enabling role in fostering innovative entry within AI-related local industries (AI technologies as focused enablers) and within non AI-related local industries, as well (AI technologies as generalised enablers). Results from Negative Binomial fixed-effect and Poisson fixed-effect regressions (controlled for a variety of concurrent drivers of entrepreneurship) reveal that the local AI knowledge stock does promote the spread of innovative startups, so supporting both the KSTE+I approach and the enabling role of AI technologies; however, this relationship is confirmed only with regard to the sole high-tech/AI-related industries. |
| Keywords: | KSTE+I, Artificial Intelligence, innovative entry, enabling technologies |
| JEL: | O33 L26 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17206 |
| By: | Natália Barbosa (School of Economics and Management, University of Minho) |
| Abstract: | The adoption of new digital technologies offers new opportunities and has the scope to engender positive effects on firms’ expansion and success in international markets. This paper examines the main factors driving the adoption of Artificial Intelligence (AI) and AI-related digital technologies that enable the Industry 4.0 transformation and whether these new generation of digital technologies affect exporting performance at firm level. Using a rich and representative sample of Portuguese firms over the period 2014-2020, the estimated results suggest that firm’s ex-ante performance, digital infrastructures and in-house ICT skills are the main drivers of digitalisation. However, conditional to ex-ante firm’s performance, there are heterogenous effects on exporting performance across digital technologies and across industries. Moreover, there is evidence of positive selection towards large firms, casting doubts on the inclusiveness of the adoption process and the performance effects of AI and AI-related technologies. |
| Keywords: | Artificial Intelligence, Industry 4.0 enabling digital technologies, firms’ exporting performance |
| JEL: | L20 H81 L25 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:mde:wpaper:183 |
| By: | Hongcheng Ding; Xuanze Zhao; Zixiao Jiang; Shamsul Nahar Abdullah; Deshinta Arrova Dewi |
| Abstract: | Accurate forecasting of the EUR/USD exchange rate is crucial for investors, businesses, and policymakers. This paper proposes a novel framework, IUS, that integrates unstructured textual data from news and analysis with structured data on exchange rates and financial indicators to enhance exchange rate prediction. The IUS framework employs large language models for sentiment polarity scoring and exchange rate movement classification of texts. These textual features are combined with quantitative features and input into a Causality-Driven Feature Generator. An Optuna-optimized Bi-LSTM model is then used to forecast the EUR/USD exchange rate. Experiments demonstrate that the proposed method outperforms benchmark models, reducing MAE by 10.69% and RMSE by 9.56% compared to the best performing baseline. Results also show the benefits of data fusion, with the combination of unstructured and structured data yielding higher accuracy than structured data alone. Furthermore, feature selection using the top 12 important quantitative features combined with the textual features proves most effective. The proposed IUS framework and Optuna-Bi-LSTM model provide a powerful new approach for exchange rate forecasting through multi-source data integration. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.13214 |
| By: | OECD |
| Abstract: | The use of Artificial Intelligence (AI) in finance has increased rapidly in recent years, with the potential to deliver important benefits to market participants and to improve customer welfare. At the same time, AI in finance could also amplify existing risks in financial markets and create new ones. This report analyses different regulatory approaches to the use of AI in finance in 48 OECD and non-OECD jurisdictions based on the Survey on Regulatory Approaches to AI in Finance. |
| Date: | 2024–09–05 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:24-en |