|
on Artificial Intelligence |
| By: | Duk Gyoo Kim; Ahram Moon |
| Abstract: | We ran a controlled laboratory experiment to examine whether ChatGPT’s aid can increase the participants’ performance in three different—reading and writing, mathematical problem-solving, and computational thinking—tasks. We find that the math score significantly decreases with ChatGPT’s assistance. This result is mainly because the low-ability subjects couldn’t discern the hallucinated answers with the correct ones, and it contests the general idea that ChatGPT can complement the workers with less expertise. |
| Keywords: | laboratory experiment, ChatGPT, labor productivity |
| JEL: | C91 J24 O33 D83 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_11002&r=ain |
| By: | Maria A. Cattaneo; Christian Gschwendt; Stefan C. Wolter |
| Abstract: | Advances in technology have always reshaped labor markets, increasing demand for highly skilled workers and automating human labor in many areas, leading to job creation but also losses. However, emerging AI innovations like ChatGPT may reduce labor demand in occupations previously considered "safe" from automation. While initial studies suggest that individuals adjust their educational and career choices to mitigate automation risk, the subjective monetary value of reduced automation risk is unknown. This study quantifies this value by assessing individuals' preferences for occupations for a hypothetical child in a discrete-choice experiment with almost 6'000 participants. The results show that survey respondents' willingness to accept lower wages for an occupation with a lower exposure to automation of 10 percentage points is substantial and amounts to almost 20 percent of an annual gross wage. Although the preferences are quite homogeneous, there are still some significant differences in willingness to pay between groups, with men, younger people, those with higher levels of education, and those with a higher risk tolerance showing a lower willingness to pay. |
| Keywords: | Artificial intelligence, automation, willingness to pay, survey experiment |
| JEL: | J24 O33 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:iso:educat:0213&r=ain |
| By: | Yu, Chen |
| Abstract: | As the global population ages, the integration of Artificial Intelligence (AI) technologies holds significant promise in addressing the multifaceted challenges and opportunities presented by aging societies. This article explores the potential impact of AI in healthcare, the economy, social integration, and ethical considerations within the context of an aging population. By examining the role of AI in extending quality of life, promoting independence, and fostering inclusive policies, this study elucidates the ways in which AI can serve as a boon to aging societies. Through international collaboration and innovation, AI has the potential to revolutionize the landscape of aging, offering tailored solutions that enhance the well-being and social inclusion of older adults worldwide. |
| Date: | 2024–04–12 |
| URL: | http://d.repec.org/n?u=RePEc:osf:thesis:a8suh&r=ain |
| By: | Drydakis, Nick (Anglia Ruskin University) |
| Abstract: | There is limited research assessing how AI knowledge affects employment prospects. The present study defines the term 'AI capital' as a vector of knowledge, skills and capabilities related to AI technologies, which could boost individuals' productivity, employment and earnings. Subsequently, the study reports the outcomes of a genuine correspondence test in England. It was found that university graduates with AI capital, obtained through an AI business module, experienced more invitations for job interviews than graduates without AI capital. Moreover, graduates with AI capital were invited to interviews for jobs that offered higher wages than those without AI capital. Furthermore, it was found that large firms exhibited a preference for job applicants with AI capital, resulting in increased interview invitations and opportunities for higher-paying positions. The outcomes hold for both men and women. The study concludes that AI capital might be rewarded in terms of employment prospects, especially in large firms. |
| Keywords: | artificial intelligence, artificial intelligence capital, employment, wages, higher education, education |
| JEL: | E24 I26 O14 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16866&r=ain |
| By: | Carioli, Paolo; Czarnitzki, Dirk; Fernández, Gastón P. |
| Abstract: | Artificial Intelligence (AI) is considered to be the next general-purpose technology, with the potential of performing tasks commonly requiring human capabilities. While it is commonly feared that AI replaces labor and disrupts jobs, we instead investigate the potential of AI for overcoming increasingly alarming skills shortages in firms. We exploit unique German survey data from the Mannheim Innovation Panel on both the adoption of AI and the extent to which firms experience scarcity of skills. We measure skills shortage by the number of job vacancies that could not be filled as planned by firms, distinguishing among different types of skills. To account for the potential endogeneity of skills shortage, we also implement instrumental variable estimators. Overall, we find a positive and significant effect of skills shortage on AI adoption, the breadth of AI methods, and the breadth of areas of application of AI. In addition, we find evidence that scarcity of labor with academic education relates to firms exploring and adopting AI. |
| Keywords: | Artificial Intelligence, skills shortage, CIS data |
| JEL: | J63 M15 O14 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:289448&r=ain |
| By: | Jason P Davis; Jian Bai Li |
| Abstract: | How are new technologies like generative AI quickly adopted and used by executive and managerial leaders to create value in organizations? A survey of INSEAD's global alumni base revealed several intriguing insights into perceptions and engagements with generative AI across a broad spectrum of demographics, industries, and geographies. Notably, there's a prevailing optimism about the role of generative AI in enhancing productivity and innovation, as evidenced by the 90% of respondents being excited about its time-saving and efficiency benefits. Analysis revealed different attitudes about adoption and use across demographic variables. Younger respondents are significantly more excited about generative AI and more likely to be using it at work and in personal life than older participants. Those in Europe have a somewhat more distant view of generative AI than those in North America in Asia, in that they see the gains more likely to be captured by organizations than individuals, and are less likely to be using it in professional and personal contexts than those in North America and Asia. This may also be related to the fact that those in Europe are more likely to be working in Financial Services and less likely to be working in Information Technology industries than those in North America and Asia. Despite this, those in Europe are more likely to see AGI happening faster than those in North America, although this may reflect less interaction with generative AI in personal and professional contexts. These findings collectively underscore the complex and multifaceted perceptions of generative AI's role in society, pointing to both its promising potential and the challenges it presents. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.04543&r=ain |
| By: | Masanori Hirano |
| Abstract: | With the recent development of large language models (LLMs), models that focus on certain domains and languages have been discussed for their necessity. There is also a growing need for benchmarks to evaluate the performance of current LLMs in each domain. Therefore, in this study, we constructed a benchmark comprising multiple tasks specific to the Japanese and financial domains and performed benchmark measurements on some models. Consequently, we confirmed that GPT-4 is currently outstanding, and that the constructed benchmarks function effectively. According to our analysis, our benchmark can differentiate benchmark scores among models in all performance ranges by combining tasks with different difficulties. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.15062&r=ain |