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on Artificial Intelligence |
| By: | Josie Zvelebilova; Saiph Savage; Christoph Riedl |
| Abstract: | How does the presence of an AI assistant affect the collective attention of a team? We study 20 human teams of 3-4 individuals paired with one voice-only AI assistant during a challenging puzzle task. Teams are randomly assigned to an AI assistant with a human- or robotic-sounding voice that provides either helpful or misleading information about the task. Treating each individual AI interjection as a treatment intervention, we identify the causal effects of the AI on dynamic group processes involving language use. Our findings demonstrate that the AI significantly affects what teams discuss, how they discuss it, and the alignment of their mental models. Teams adopt AI-introduced language for both terms directly related to the task and for peripheral terms, even when they (a) recognize the unhelpful nature of the AI, (b) do not consider the AI a genuine team member, and (c) do not trust the AI. The process of language adaptation appears to be automatic, despite doubts about the AI's competence. The presence of an AI assistant significantly impacts team collective attention by modulating various aspects of shared cognition. This study contributes to human-AI teaming research by highlighting collective attention as a central mechanism through which AI systems in team settings influence team performance. Understanding this mechanism will help CSCW researchers design AI systems that enhance team collective intelligence by optimizing collective attention. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.17489 |
| By: | Alexandre Georgieff |
| Abstract: | This paper looks at the links between AI and wage inequality across 19 OECD countries. It uses a measure of occupational exposure to AI derived from that developed by Felten, Raj and Seamans (2019) – a measure of the degree to which occupations rely on abilities in which AI has made the most progress. The results provide no indication that AI has affected wage inequality between occupations so far (over the period 2014-2018). At the same time, there is some evidence that AI may be associated with lower wage inequality within occupations – consistent with emerging findings from the literature that AI reduces productivity differentials between workers. Further research is needed to identify the exact mechanisms driving the negative relationship between AI and wage inequality within occupations. One possible explanation is that low performers have more to gain from using AI because AI systems are trained to embody the more accurate practices of high performers. It is also possible that AI reduces performance differences within an occupation through a selection effect, e.g. if low performers leave their job because they are unable to adapt to AI tools by shifting their activities to tasks that AI cannot automate. |
| Keywords: | Artificial intelligence, Employment, Skills |
| JEL: | J21 J23 J24 O33 |
| Date: | 2024–04–10 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:13-en |
| By: | Andrew Green |
| Abstract: | Most workers who will be exposed to artificial intelligence (AI) will not require specialised AI skills (e.g. machine learning, natural language processing, etc.). Even so, AI will change the tasks these workers do, and the skills they require. This report provides first estimates for the effect of artificial intelligence on the demand for skills in jobs that do not require specialised AI skills. The results show that the skills most demanded in occupations highly exposed to AI are management and business skills. These include skills in general project management, finance, administration and clerical tasks. The results also show that there have been increases over time in the demand for these skills in occupations highly exposed to AI. For example, the share of vacancies in these occupations that demand at least one emotional, cognitive or digital skill has increased by 8 percentage points. However, using a panel of establishments (which induces plausibly exogenous variation in AI exposure), the report finds evidence that the demand for these skills is beginning to fall. |
| Keywords: | Artificial intelligence, Labour demand, Skills |
| JEL: | J23 J24 J63 |
| Date: | 2024–04–10 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:14-en |
| By: | Francesca Borgonovi; Flavio Calvino; Chiara Criscuolo; Lea Samek; Helke Seitz; Julia Nania; Julia Nitschke; Layla O’Kane |
| Abstract: | This report analyses the demand for positions that require skills needed to develop or work with AI systems across 14 OECD countries between 2019 and 2022. It finds that, despite rapid growth in the demand for AI skills, AI-related online vacancies comprised less than 1% of all job postings and were predominantly found in sectors such as ICT and Professional Services. Skills related to Machine Learning were the most sought after. The US-focused part of the study reveals a consistent demand for socio-emotional, foundational, and technical skills across all AI employers. However, leading firms – those who posted the most AI jobs – exhibited a higher demand for AI professionals combining technical expertise with leadership, innovation, and problem-solving skills, underscoring the importance of these competencies in the AI field. |
| Keywords: | Artificial Intelligence, Online vacancies, Skills |
| JEL: | C81 J23 J24 O33 |
| Date: | 2023–10–17 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:2-en |
| By: | Chloé Touzet |
| Abstract: | People with disability face persisting difficulties in the labour market. There are concerns that AI, if managed poorly, could further exacerbate these challenges. Yet, AI also has the potential to create more inclusive and accommodating environments and might help remove some of the barriers faced by people with disability in the labour market. Building on interviews with more than 70 stakeholders, this report explores the potential of AI to foster employment for people with disability, accounting for both the transformative possibilities of AI-powered solutions and the risks attached to the increased use of AI for people with disability. It also identifies obstacles hindering the use of AI and discusses what governments could do to avoid the risks and seize the opportunities of using AI to support people with disability in the labour market. |
| Keywords: | Artificial Intelligence, Disability, Employment |
| JEL: | J14 J18 J20 |
| Date: | 2023–11–24 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:7-en |
| By: | Joshua S. Gans |
| Abstract: | This paper examines the impact of Generative AI (GAI) on communication through the lens of salience and signalling models. It explores how GAI affects both senders' ability to create salient messages and receivers' costs of absorbing them. The analysis reveals that while GAI can increase communication by reducing costs, it may also disrupt traditional signalling mechanisms. In a salience model, GAI generally improves outcomes but can potentially reduce receiver welfare. In a pure signalling model, GAI may hinder effective communication by making it harder to distinguish high-quality messages. This suggests that GAI's introduction necessitates new instruments and mechanisms to facilitate effective communication and quality assessment in this evolving landscape. |
| JEL: | D83 O31 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32690 |
| By: | Joshua S. Gans |
| Abstract: | When AI prediction substantially resolves trial uncertainty, a party purchasing AI prediction will disclose it if it is in their favour and not otherwise, signalling the outcome to the other party. Thus, the trial outcome becomes common knowledge. However, this implies that the parties will settle rather than purchase the AI prediction. When parties have differing prior beliefs regarding trial outcomes, these differences are only resolved if the AI prediction is purchased and utilised. In this case, AI will be purchased in equilibrium. Different trial cost allocation rules awarding all costs to the losing party (the English Rule) or having each party bear their own costs (the American Rule) can impact the demand for AI for settlement negotiations, but how this occurs interacts with the expectations regarding whether a settlement will occur or not in AI's absence. |
| JEL: | K41 O31 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32685 |
| By: | Joshua S. Gans |
| Abstract: | This paper examines and finds that the answer is likely to be no. The environment examined starts with users who contribute based on their motives to create a public good. Their own actions determine the quality of that public good but also embed a free-rider problem. When AI is trained on that data, it can generate similar contributions to the public good. It is shown that this increases the incentive of human users to provide contributions that are more costly to supply. Thus, the overall quality of contributions from both AI and humans rises compared to human-only contributions. In situations where platform providers want to generate more contributions using explicit incentives, the rate of return on such incentives is shown to be lower in this environment. |
| JEL: | D70 H44 O31 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32686 |
| By: | Flavio Calvino; Chiara Criscuolo; Hélène Dernis; Lea Samek |
| Abstract: | This report outlines a new methodology and provides a first exploratory analysis of technologies and applications that are at the core of recent advances in AI. Using AI-related keywords and technology classes, the study identifies AI-related patents protected in the United States in 2000-18. Among those, “core” AI patents are selected based on their counts of AI-related forward citations. The analysis finds that, compared to other (AI and non-AI) patents, they are more original and general, and tend to be broader in technological scope. Technologies related to general AI, robotics, computer/image vision and recognition/detection are consistently listed among core AI patents, with autonomous driving and deep learning having recently become more prominent. Finally, core AI patents tend to spur innovation across AI-related domains, although some technologies – likely AI applications, such as autonomous driving or robotics – appear to increasingly contribute to developments in their own field. |
| Keywords: | Artificial Intelligence, Innovation, Patents |
| JEL: | C81 O31 O33 O34 |
| Date: | 2023–11–13 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:6-en |
| By: | Damioli, Giacomo; Van Roy, Vincent; Vertesy, Daniel; Vivarelli, Marco |
| 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, Technological Paradigm, Structural Change, Patents |
| JEL: | O31 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1467 |
| By: | Gaétan de Rassenfosse; Adam B. Jaffe; Joel Waldfogel |
| Abstract: | The arrival of creative machines—software capable of producing human-like creative content—has triggered a series of legal challenges about intellectual property. The outcome of these legal challenges will shape the future of the creative industry in ways that could enhance or jeopardize welfare. Policymakers are already tasked with creating regulations for a post-generative AI creative industry. Economics may offer valuable insights, and this paper is our attempt to contribute to the discussion. We identify the main economic issues and propose a framework and some tools for thinking about them. |
| JEL: | O38 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32698 |
| By: | Francesco Filippucci; Peter Gal; Cecilia Jona-Lasinio; Alvaro Leandro; Giuseppe Nicoletti |
| Abstract: | This paper explores the economics of Artificial Intelligence (AI), focusing on its potential as a new General-Purpose Technology that can significantly influence economic productivity and societal wellbeing. It examines AI's unique capacity for autonomy and self-improvement, which could accelerate innovation and potentially revive sluggish productivity growth across various industries, while also acknowledging the uncertainties surrounding AI's long-term productivity impacts. The paper discusses the concentration of AI development in big tech firms, uneven adoption rates, and broader societal challenges such as inequality, discrimination, and security risks. It calls for a comprehensive policy approach to ensure AI's beneficial development and diffusion, including measures to promote competition, enhance accessibility, and address job displacement and inequality. |
| Keywords: | Artificial intelligence, Competition, Productivity |
| JEL: | O15 |
| Date: | 2024–04–16 |
| URL: | https://d.repec.org/n?u=RePEc:oec:comaaa:15-en |
| By: | Adria Pop; Jan Sp\"orer; Siegfried Handschuh |
| Abstract: | This research dissects financial equity research reports (ERRs) by mapping their content into categories. There is insufficient empirical analysis of the questions answered in ERRs. In particular, it is not understood how frequently certain information appears, what information is considered essential, and what information requires human judgment to distill into an ERR. The study analyzes 72 ERRs sentence-by-sentence, classifying their 4940 sentences into 169 unique question archetypes. We did not predefine the questions but derived them solely from the statements in the ERRs. This approach provides an unbiased view of the content of the observed ERRs. Subsequently, we used public corporate reports to classify the questions' potential for automation. Answers were labeled "text-extractable" if the answers to the question were accessible in corporate reports. 78.7% of the questions in ERRs can be automated. Those automatable question consist of 48.2% text-extractable (suited to processing by large language models, LLMs) and 30.5% database-extractable questions. Only 21.3% of questions require human judgment to answer. We empirically validate using Llama-3-70B and GPT-4-turbo-2024-04-09 that recent advances in language generation and information extraction enable the automation of approximately 80% of the statements in ERRs. Surprisingly, the models complement each other's strengths and weaknesses well. The research confirms that the current writing process of ERRs can likely benefit from additional automation, improving quality and efficiency. The research thus allows us to quantify the potential impacts of introducing large language models in the ERR writing process. The full question list, including the archetypes and their frequency, will be made available online after peer review. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.18327 |
| By: | Tian Guo; Emmanuel Hauptmann |
| Abstract: | Large language models (LLMs) and their fine-tuning techniques have demonstrated superior performance in various language understanding and generation tasks. This paper explores fine-tuning LLMs for stock return forecasting with financial newsflow. In quantitative investing, return forecasting is fundamental for subsequent tasks like stock picking, portfolio optimization, etc. We formulate the model to include text representation and forecasting modules. We propose to compare the encoder-only and decoder-only LLMs, considering they generate text representations in distinct ways. The impact of these different representations on forecasting performance remains an open question. Meanwhile, we compare two simple methods of integrating LLMs' token-level representations into the forecasting module. The experiments on real news and investment universes reveal that: (1) aggregated representations from LLMs' token-level embeddings generally produce return predictions that enhance the performance of long-only and long-short portfolios; (2) in the relatively large investment universe, the decoder LLMs-based prediction model leads to stronger portfolios, whereas in the small universes, there are no consistent winners. Among the three LLMs studied (DeBERTa, Mistral, Llama), Mistral performs more robustly across different universes; (3) return predictions derived from LLMs' text representations are a strong signal for portfolio construction, outperforming conventional sentiment scores. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.18103 |
| By: | Chong Zhang; Xinyi Liu; Mingyu Jin; Zhongmou Zhang; Lingyao Li; Zhenting Wang; Wenyue Hua; Dong Shu; Suiyuan Zhu; Xiaobo Jin; Sujian Li; Mengnan Du; Yongfeng Zhang |
| Abstract: | Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fundamentals, and global events)? These factors, which frequently influence trading behaviors, are critical elements in the quest for maximizing investors' profits. Our work attempts to solve this problem through large language model based agents. We have developed a multi-agent AI system called StockAgent, driven by LLMs, designed to simulate investors' trading behaviors in response to the real stock market. The StockAgent allows users to evaluate the impact of different external factors on investor trading and to analyze trading behavior and profitability effects. Additionally, StockAgent avoids the test set leakage issue present in existing trading simulation systems based on AI Agents. Specifically, it prevents the model from leveraging prior knowledge it may have acquired related to the test data. We evaluate different LLMs under the framework of StockAgent in a stock trading environment that closely resembles real-world conditions. The experimental results demonstrate the impact of key external factors on stock market trading, including trading behavior and stock price fluctuation rules. This research explores the study of agents' free trading gaps in the context of no prior knowledge related to market data. The patterns identified through StockAgent simulations provide valuable insights for LLM-based investment advice and stock recommendation. The code is available at https://github.com/MingyuJ666/Stockagent . |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.18957 |
| By: | Tiago Monteiro |
| Abstract: | In the field of quantitative finance, machine learning methods have become essential for alpha generation. This paper presents a pioneering method that uniquely combines Hidden Markov Models (HMM) and neural networks, creating a dual-model alpha generation system integrated with Black-Litterman portfolio optimization. The methodology, implemented on the QuantConnect platform, aims to predict future price movements and optimize trading strategies. Specifically, it filters for highly liquid, top-cap energy stocks to ensure stable and predictable performance while also accounting for broker payments. QuantConnect was selected because of its robust framework and to guarantee experimental reproducibility. The algorithm achieved a 31% return between June 1, 2023, and January 1, 2024, with a Sharpe ratio of 1.669, demonstrating its potential. The findings suggest significant improvements in trading strategy performance through the combined use of the HMM and neural networks. This study explores the architecture of the algorithm, data pre-processing techniques, model training procedures, and performance evaluation, highlighting its practical applicability and effectiveness in real-world trading environments. The full code and backtesting data are available under the MIT license. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.19858 |