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on Artificial Intelligence |
| By: | Jiafu An; Difang Huang; Chen Lin; Mingzhu Tai |
| Abstract: | In traditional decision making processes, social biases of human decision makers can lead to unequal economic outcomes for underrepresented social groups, such as women, racial or ethnic minorities. Recently, the increasing popularity of Large language model based artificial intelligence suggests a potential transition from human to AI based decision making. How would this impact the distributional outcomes across social groups? Here we investigate the gender and racial biases of OpenAIs GPT, a widely used LLM, in a high stakes decision making setting, specifically assessing entry level job candidates from diverse social groups. Instructing GPT to score approximately 361000 resumes with randomized social identities, we find that the LLM awards higher assessment scores for female candidates with similar work experience, education, and skills, while lower scores for black male candidates with comparable qualifications. These biases may result in a 1 or 2 percentage point difference in hiring probabilities for otherwise similar candidates at a certain threshold and are consistent across various job positions and subsamples. Meanwhile, we also find stronger pro female and weaker anti black male patterns in democratic states. Our results demonstrate that this LLM based AI system has the potential to mitigate the gender bias, but it may not necessarily cure the racial bias. Further research is needed to comprehend the root causes of these outcomes and develop strategies to minimize the remaining biases in AI systems. As AI based decision making tools are increasingly employed across diverse domains, our findings underscore the necessity of understanding and addressing the potential unequal outcomes to ensure equitable outcomes across social groups. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.15281&r=ain |
| By: | Mallory Avery; Andreas Leibbrandt; Joseph Vecci |
| Abstract: | The use of Artificial Intelligence (AI) in recruitment is rapidly increasing and drastically changing how people apply to jobs and how applications are reviewed. In this paper, we use two field experiments to study how AI recruitment tools can impact gender diversity in the male-dominated technology sector, both overall and separately for labor supply and demand. We find that the use of AI in recruitment changes the gender distribution of potential hires, in some cases more than doubling the fraction of top applicants that are women. This change is generated by better outcomes for women in both supply and demand. On the supply side, we observe that the use of AI reduces the gender gap in application completion rates. Complementary survey evidence suggests that anticipated bias is a driver of increased female application completion when assessed by AI instead of human evaluators. On the demand side, we find that providing evaluators with applicants’ AI scores closes the gender gap in assessments that otherwise disadvantage female applicants. Finally, we show that the AI tool would have to be substantially biased against women to result in a lower level of gender diversity than found without AI. |
| Keywords: | artificial intelligence, gender, diversity, field experiment |
| JEL: | C93 J23 J71 J78 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10996&r=ain |
| By: | Shun Yiu; Rob Seamans; Manav Raj; Ted Liu |
| Abstract: | AI technologies have the potential to affect labor market outcomes by both increasing worker productivity and reducing the demand for certain skills or tasks. Such changes may have important implications for the ways in which workers seek jobs and position themselves. In this project, we examine how exposure to generative AI technologies affects the strategic behavior of freelancer workers on an online work platform following the launch of ChatGPT in December 2022. Relative to their less exposed counterparts, we show that freelancers that are more exposed to language modeling technologies apply for more job posts on the platform following the launch of ChatGPT and increase the concentration of these applications across specializations. We document heterogeneity in this effect across freelancer characteristics and consider how such behaviors shape whether and to what extent the technological shock affects freelancer performance on the platform. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.15262&r=ain |
| By: | Joshua S. Gans |
| Abstract: | This paper surveys the relevant existing literature that can help researchers and policy makers understand the drivers of competition in markets that constitute the provision of artificial intelligence products. The focus is on three broad markets: training data, input data, and AI predictions. It is shown that a key factor in determining the emergence and persistence of market power will be the operation of markets for data that would allow for trading data across firm boundaries. |
| JEL: | L15 L40 O34 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32270&r=ain |
| By: | Mariarosaria Comunale; Andrea Manera |
| Abstract: | We review the literature on the effects of Artificial Intelligence (AI) adoption and the ongoing regulatory efforts concerning this technology. Economic research encompasses growth, employment, productivity, and income inequality effects, while regulation covers market competition, data privacy, copyright, national security, ethics concerns, and financial stability. We find that: (i) theoretical research agrees that AI will affect most occupations and transform growth, but empirical findings are inconclusive on employment and productivity effects; (ii) regulation has focused primarily on topics not explored by the academic literature; (iii) across countries, regulations differ widely in scope and approaches and face difficult trade-offs. |
| Keywords: | Artificial Intelligence (AI); labor market; task exposure; productivity; regulation; governance. |
| Date: | 2024–03–22 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2024/065&r=ain |
| By: | Kathryn Bonney; Cory Breaux; Catherine Buffington; Emin Dinlersoz; Lucia Foster; Nathan Goldschlag; John Haltiwanger; Zachary Kroff; Keith Savage |
| Abstract: | Timely and accurate measurement of AI use by firms is both challenging and crucial for understanding the impacts of AI on the U.S. economy. We provide new, real-time estimates of current and expected future use of AI for business purposes based on the Business Trends and Outlook Survey for September 2023 to February 2024. During this period, bi-weekly estimates of AI use rate rose from 3.7% to 5.4%, with an expected rate of about 6.6% by early Fall 2024. The fraction of workers at businesses that use AI is higher, especially for large businesses and in the Information sector. AI use is higher in large firms but the relationship between AI use and firm size is non-monotonic. In contrast, AI use is higher in young firms although, on an employment-weighted basis, is U-shaped in firm age. Common uses of AI include marketing automation, virtual agents, and data/text analytics. AI users often utilize AI to substitute for worker tasks and equipment/software, but few report reductions in employment due to AI use. Many firms undergo organizational changes to accommodate AI, particularly by training staff, developing new workflows, and purchasing cloud services/storage. AI users also exhibit better overall performance and higher incidence of employment expansion compared to other businesses. The most common reason for non-adoption is the inapplicability of AI to the business. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:24-16&r=ain |
| By: | Jun Xu |
| Abstract: | The burgeoning integration of Artificial Intelligence (AI) into Environmental, Social, and Governance (ESG) initiatives within the financial sector represents a paradigm shift towards more sus-tainable and equitable financial practices. This paper surveys the industrial landscape to delineate the necessity and impact of AI in bolstering ESG frameworks. With the advent of stringent regulatory requirements and heightened stakeholder awareness, financial institutions (FIs) are increasingly compelled to adopt ESG criteria. AI emerges as a pivotal tool in navigating the complex in-terplay of financial activities and sustainability goals. Our survey categorizes AI applications across three main pillars of ESG, illustrating how AI enhances analytical capabilities, risk assessment, customer engagement, reporting accuracy and more. Further, we delve into the critical con-siderations surrounding the use of data and the development of models, underscoring the importance of data quality, privacy, and model robustness. The paper also addresses the imperative of responsible and sustainable AI, emphasizing the ethical dimensions of AI deployment in ESG-related banking processes. Conclusively, our findings suggest that while AI offers transformative potential for ESG in banking, it also poses significant challenges that necessitate careful consideration. The final part of the paper synthesizes the survey's insights, proposing a forward-looking stance on the adoption of AI in ESG practices. We conclude with recommendations with a reference architecture for future research and development, advocating for a balanced approach that leverages AI's strengths while mitigating its risks within the ESG domain. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.05541&r=ain |
| By: | Taejin Park |
| Abstract: | This paper introduces a Large Language Model (LLM)-based multi-agent framework designed to enhance anomaly detection within financial market data, tackling the longstanding challenge of manually verifying system-generated anomaly alerts. The framework harnesses a collaborative network of AI agents, each specialised in distinct functions including data conversion, expert analysis via web research, institutional knowledge utilization or cross-checking and report consolidation and management roles. By coordinating these agents towards a common objective, the framework provides a comprehensive and automated approach for validating and interpreting financial data anomalies. I analyse the S&P 500 index to demonstrate the framework's proficiency in enhancing the efficiency, accuracy and reduction of human intervention in financial market monitoring. The integration of AI's autonomous functionalities with established analytical methods not only underscores the framework's effectiveness in anomaly detection but also signals its broader applicability in supporting financial market monitoring. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.19735&r=ain |
| By: | Andrés Rodríguez-Pose; Zhuoying You; |
| Abstract: | Artificial intelligence (AI) and robotics are revolutionising production, yet their potential to stimulate innovation and change innovation patterns remains underexplored. This paper examines whether AI and robotics can spearhead technological innovation, with a particular focus on their capacity to deliver where other policies have mostly failed: less developed cities and regions. We resort to OLS and IV-2SLS methods to probe the direct and moderating influences of AI and robotics on technological innovation across 270 Chinese cities. We further employ quantile regression analysis to assess their impacts on innovation in more and less innovative cities. The findings reveal that AI and robotics significantly promote technological innovation, with a pronounced impact in cities at or below the technological frontier. Additionally, the use of AI and robotics improves the returns of investment in science and technology (S&T) on technological innovation. AI and robotics moderating effects are often more pronounced in less innovative cities, meaning that AI and robotics are not just powerful instruments for the promotion of innovation but also effective mechanisms to reduce the yawning gap in regional innovation between Chinese innovation hubs and the rest of the country. |
| Keywords: | AI, robotics, China, technological innovation, territorial inequality |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2406&r=ain |