Investment In Artificial Intelligence Research Articles

Increasing transparency of computer-aided detection impairs decision-making in visual search.

Recent developments in artificial intelligence (AI) have led to changes in healthcare. Government and regulatory bodies have advocated the need for transparency in AI systems with recommendations to provide users with more details about AI accuracy and how AI systems work. However, increased transparency could lead to negative outcomes if humans become overreliant on the technology. This study investigated how changes in AI transparency affected human decision-making in a medical-screening visual search task. Transparency was manipulated by either giving or withholding knowledge about the accuracy of an 'AI system'. We tested performance in seven simulated lab mammography tasks, in which observers searched for a cancer which could be correctly or incorrectly flagged by computer-aided detection (CAD) 'AI prompts'. Across tasks, the CAD systems varied in accuracy. In the 'transparent' condition, participants were told the accuracy of the CAD system, in the 'not transparent' condition, they were not. The results showed that increasing CAD transparency impaired task performance, producing an increase in false alarms, decreased sensitivity, an increase in recall rate, and a decrease in positive predictive value. Along with increasing investment in AI, this research shows that it is important to investigate how transparency of AI systems affect human decision-making. Increased transparency may lead to overtrust in AI systems, which can impact clinical outcomes.

Affect of Manufactured Insights on Human Misfortune in Choice Making, Sluggishness and Security in Instruction

The impact of artificial intelligence on decision-making, laziness, and privacy concerns among university students in Pakistan and China is examined in this study. Like other sectors, education uses artificial intelligence to address modern-day challenges. The investment in artificial intelligence will grow to more than $250 million over the course of the next decade. Researchers and institutions across the globe are praising the positive role of Artificial Intelligence, but ignoring its concerns. The data analysis of this study is based on qualitative methodology. The data was collected from students in different universities. The sample was drawn using the purposive sampling technique. The data analysis shows that artificial intelligence makes humans lazy. It impacts both security and privacy. The impact of artificial intelligence in Pakistan and Chinese society has been found to be a factor in the loss of decision-making. Humans are the most affected by the effects of artificial intelligence. The study argues that preventative measures are necessary before using artificial intelligence in education. It would be like putting the devils in charge of the major human concerns. In order to address the issue, it is recommended to cent rate on justified designing and deployment.

The Adequacy of the UAE Commercial Law in 2023 in Regulating Artificial Intelligence as a Subject of the Contract

The study aims to identify the adequacy of the UAE Commercial Law in 2023 in regulating artificial intelligence as a subject of the contract, This will be done by searching for the way in which the UAE legislator intervened to control the good and effective use of artificial intelligence, whether it was able to reconcile between protecting the interests of the parties to the transaction and encouraging technological development and investment in artificial intelligence to achieve the goals of the National Strategy for Artificial Intelligence. Then, the cases of using artificial intelligence as a subject for commercial transactions will be highlighted, because artificial intelligence is considered one of the funds and products that can be the subject of commercial transactions between professional traders, which remain subject to the applicable legislation, even those that link the trader to the consumer due to the availability of the elements of the consumer process in this type of transaction, which is concerned with protecting the consumer because the consumer transaction must be carried out with transparency and responsibility. However, as a result of the privacy of such transactions, the current legislation does not appear sufficient, but rather work must be done to establish legislative provisions to control dealings in artificial intelligence at the local and international levels to govern artificial intelligence.

Exploring the coherency and predictability between the stocks of artificial intelligence and energy corporations

This paper employs wavelet coherence, Cross-Quantilogram (CQ), and Time-Varying Parameter Vector-Autoregression (TVP-VAR) estimation strategies to investigate the dependence structure and connectedness between investments in artificial intelligence (AI) and eight different energy-focused sectors. We find significant evidence of dependence and connectedness between the stock returns of AI and those of the energy-focused sectors, especially during intermediate and long-term investment horizons. The relationship has become stronger since the COVID-19 pandemic. More specifically, results from the wavelet coherence approach show a stronger association between the stock returns of energy-focused sectors and AI, while results from the CQ analysis show that directional predictability from AI to energy-focused sectors varies across sectors, investment horizons, and market conditions. TVP-VAR results show that since the COVID-19 outbreak, AI has become more of a net shock receiver from the energy market. Our study offers crucial implications for investors and policymakers.

How Does Artificial Intelligence Shape Audit Firms?

Does artificial intelligence (AI) displace auditors? We exploit the staggered hiring of AI employees at audit office locations across the United States as a proxy for the use of AI at local audit offices. The main findings are as follows. First, relative to audit offices that do not yet hire AI employees, those that do hire AI employees have a 4.3% increase in the number of auditor jobs, particularly among junior and midlevel auditors. Second, using AI is associated with an increased demand for soft skills (e.g., cognitive skills) in auditor jobs. Third, audit offices that use AI have more accurate going concern and internal control opinions. Semistructured interviews of 11 seasoned audit partners confirm that investment in AI is centralized at the national level, but the decision to deploy it often resides at the local audit office level. Notably, none of the partners believe that AI has replaced or will replace human auditors. Overall, our study—comprising both empirical and qualitative data—suggests that using AI does not replace auditors, but rather changes the skills required for these jobs and improves audit quality. This paper was accepted by Suraj Srinivasan, accounting. Funding: K. K. F. Law’s research is supported by the Start-Up Grant from Nanyang Technological University and the Ministry of Education (MOE), Singapore, under its Academic Research Fund Tier 1 [Grant RG128/20]. M. Shen’s research is supported by the Start-Up Grant from the National University of Singapore [Grant A-0003914-00-00] and the MOE under its Academic Research Fund Tier 1 [Grant A-8000098-00-00]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.04040 .

Implications of causality in artificial intelligence.

Over the last decade, investment in artificial intelligence (AI) has grown significantly, driven by technology companies and the demand for PhDs in AI. However, new challenges have emerged, such as the 'black box' and bias in AI models. Several approaches have been developed to reduce these problems. Responsible AI focuses on the ethical development of AI systems, considering social impact. Fair AI seeks to identify and correct algorithm biases, promoting equitable decisions. Explainable AI aims to create transparent models that allow users to interpret results. Finally, Causal AI emphasizes identifying cause-and-effect relationships and plays a crucial role in creating more robust and reliable systems, thereby promoting fairness and transparency in AI development. Responsible, Fair, and Explainable AI has several weaknesses. However, Causal AI is the approach with the slightest criticism, offering reassurance about the ethical development of AI.

ICRC’s Intervention on AI Based Weapon under International Humanitarian Law: A Critical Analysis

The International Committee of the Red Cross (ICRC) has exerted pressure on nations to enact new international legislation that would limit the use of certain autonomous weapons, including those under third-party control, and forbid the deployment of others. The International Committee of the Red Cross (ICRC) is a neutral, independent organization that offers victims of armed conflict and other perilous situations humanitarian protection and support. In addition to responding to emergencies, it promotes compliance with international humanitarian law and its assimilation into national laws. In modern times, the military is making significant investments in artificial intelligence, and there are already instances of AI being used in conflict. A number of sectors that create serious humanitarian concerns include those that the ICRC has identified as areas in which artificial intelligence is being developed for use in combat by armed actors. The International Committee of the Red Cross (ICRC) has been pressuring governments to pass new international laws that would prohibit the deployment of some autonomous weapons and restrict the use of others, including those that are controlled by third parties.

A stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide

As artificial intelligence (AI) begins to take center stage in technological innovations, it is essential to understand the business value of AI innovation efforts and investments. While some early work at the firm level exists, there is a shortage of literature that takes a larger country-level perspective. This study investigated the effect of AI innovation efforts on production efficiency across countries using stochastic production frontier approaches. In addition, our model also included the traditional economic inputs of capital and labor. We used both the Cobb–Douglas function and Constant Elastic Substitution model specifications. The significant findings of this study are as follows: Innovation efforts in AI measured by the number of AI-related patents and capital investment in AI have a substantial effect on economic output. The significance of AI investments indicates the need for a robust digital infrastructure as a prerequisite for harnessing AI capabilities. The complementary relationship between labor and AI-related patents implies that high-skilled labor is often necessary to incorporate AI inputs into production. However, as AI capabilities develop, they weaken the effect on labor input. The study also distinguishes between AI innovation (research and development activities indicated by AI patents) and the production efficiency of AI investments (return on every dollar invested), highlighting that more AI innovation does not always translate into better production efficiency. The findings indicate that while the United States leads innovation in AI, the UK has the best production efficiency. China ranked fourth in AI innovation and has the lowest production efficiency among the countries included in the study.

Dreaming of AI: environmental sustainability and the promise of participation

AbstractThere is widespread consensus among policymakers that climate change and digitalisation constitute the most pressing global transformations shaping human life in the 21st century. Seeking to address the challenges arising at this juncture, governments, technologists and scientists alike increasingly herald artificial intelligence (AI) as a vehicle to propel climate change mitigation and adaptation. In this paper, we explore the intersection of digitalisation and climate change by examining the deployment of AI in government-led climate action. Building on participant observations conducted in the context of the “Civic Tech Lab for Green”—a government-funded public interest AI initiative—and eight expert interviews, we investigate how AI shapes the negotiation of environmental sustainability as an issue of public interest. Challenging the prescribed means–end relationship between AI and environmental protection, we argue that the unquestioned investment in AI curtails political imagination and displaces discussion of climate “problems” and possible “solutions” with “technology education”. This line of argumentation is rooted in empirical findings that illuminate three key tensions in current coproduction efforts: “AI talk vs. AI walk”, “civics washing vs. civics involvement” and “public invitation vs. public participation”. Emphasising the importance of re-exploring the innovative state in climate governance, this paper extends academic literature in science and technology studies that examines public participation in climate change adaptation by shedding light on the emergent phenomenon of public interest AI.

Artificial intelligence and labor demand: An empirical analysis of Chinese small and micro enterprises

The widespread application of artificial intelligence (AI) technology has triggered a significant transformation in the economic structure and has brought profound changes to human society. As China promotes the digital transformation of industries, understanding how the investment in AI by small and micro enterprises (SMEs) affects labor demand, which is inextricably linked to “stable employment”, becomes an important question. This paper uses special data from 127 SMEs in 14 provinces from 2016 to 2020 and employs a two-way fixed effects model to study the impact of AI inputs on enterprises’ labor demand. The empirical results show that the impact of AI inputs on the labor demand of SMEs is not significant overall, but shows a significant negative effect in non-state-owned enterprises, private enterprises, and high-tech enterprises. There is a significant difference in the impact of AI inputs on the labor demand of different industries, with only the wholesale and retail industry demonstrating a significant positive impact. From the results of mechanism analysis, the substitution effect and creation effect of AI inputs on labor demand coexist, and in general, these two effects cancel each other out. However, the substitution effect dominates in some types of enterprises and industries. Finally, this paper discusses the government and enterprise coping strategies for the employment impact of AI applications based on empirical evidence and research results. This paper not only theoretically demonstrates that the impact of AI investment on firms' labor demand is uncertain, but also empirically demonstrates that Chinese firms' AI investment does not significantly affect firms' overall labor demand. This facilitates the government and enterprises to formulate strategies that can enhance the level of enterprise intelligence without impacting the labor market.

The mediating role of governance in creating a nexus between investment in artificial intelligence (AII) and human well-being in the BRICS countries

The BRICS countries (Brazil, Russia, India, China, and South Africa) aim to achieve Sustainable Development Goals 3 and 16, which involve promoting human well-being for all and building strong institutions and governance. This study examines the AII-HWBG nexus contingent on governance indicators within the BRICS nations in 2012-2022 using the Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL) technique. Its findings reveal a long-term relationship among variables with varied causality directions and point to the necessity of integrating governance quality into AII to boost HWBG in both the short- and long-term perspective. Since AII has not so far been used to support HWBG there is a dire need for caution when considering AII’s interaction with institutional governance, economic governance, control of corruption, political stability, regulatory quality and voice and accountability. The paper highlights the crucial role of governance quality in shaping the way AI investment impacts the human well-being. To ensure an overall improvement of well-being, priority should be given to strategies that promote positive synergy between AI investment and governance while mitigating possible harmful effects. Carefully targeted measures in governance areas can create an environment conducive to AI development where it will significantly benefit the citizens of the BRICS countries.

Artificial intelligence in drug repurposing for rare diseases: a mini-review.

Drug repurposing, the process of identifying new uses for existing drugs beyond their original indications, offers significant advantages in terms of reduced development time and costs, particularly in addressing unmet medical needs in rare diseases. Artificial intelligence (AI) has emerged as a transformative force in healthcare, and by leveraging AI technologies, researchers aim to overcome some of the challenges associated with rare diseases. This review presents concrete case studies, as well as pre-existing platforms, initiatives, and companies that demonstrate the application of AI for drug repurposing in rare diseases. Despite representing a modest part of the literature compared to other diseases such as COVID-19 or cancer, the growing interest, and investment in AI for drug repurposing in rare diseases underscore its potential to accelerate treatment availability for patients with unmet medical needs.

Artificial Intelligence in the Effective Execution Process of Construction Projects in the Future

The construction industry currently constitutes 13% of the global gross domestic product (GDP), with projections indicating an 85% increase in value to $15.5 trillion by 2030. The widespread adoption of information technology (IT) has significantly enhanced the integration of disparate data in construction project environments. Consequently, the construction sector including full construction value chain, is undergoing a transformative phase. The increasing investment in artificial intelligence (AI) makes it impossible to keep pace with its rapid advancements. Hence, this study aims to examine the role of AI in facilitating the effective execution of construction projects in the future. This research employs a document analysis approach and scrutinizes 20 relevant papers from both domestic and international scientific databases. Methodologically, this study adopts an applied research approach, and based on the method of data collection, it is considered a descriptive survey method. Therefore, a questionnaire was designed and distributed among 100 experts and practitioners familiar with AI concepts in Tehran for data collection to conduct a census-style field study. Subsequently, Smart PLS software was employed for data analysis. The findings not only validate the model's reliability, validity and fit but also present solutions and pertinent issues related to challenges concerning AI future role in enhancing project execution efficacy.

Efficacy Analysis of Online Artificial Intelligence Fact-Checking Tools

Investments in artificial intelligence (AI) spurred development of online fact-checking tools; positioned to potentially serve as more accurate alternatives or appendages to search engines and/or nascent chatbots. This study analyzed the efficacy of four AI tools (ClaimBuster, Full Fact, TheFactual - IsThisCredible?, and Google’s Fact-Check Explorer) in producing accurate readings measured by a consensus of independent fact-checking organizations. 10 unique claims were inputted into each tool to produce individual fact-check reports, resulting in 40 fact-check reports being conducted. The results reflect an efficacy rating of 100% regarding the ability of the selected tools to produce an overall accurate reading with 89% of reports producing a unanimous determination. Additionally, recommendations were made to further map and analyze the efficacy of AI fact-checking. These findings support the notion that AI can play an effective role in aiding online truth-seeking when its determinations depend on transparently referencing its source of independent human fact-checkers.

Our Vision for JGR: Machine Learning and Computation

AbstractThis editorial introduces the inaugural issue of the Journal of Geophysical Research: Machine Learning and Computation to the scientific community, elucidating the motivations and vision behind its establishment. The landscape of computational tools for geoscientists has undergone a rapid transformation in the last decade, akin to a new scientific revolution challenging the traditional scientific method. The paradigm shift emphasizes the integration of data‐driven methods and the possibility of predicting and/or reproducing the evolution of natural phenomena with computers as the fourth pillar of scientific discovery, sparking debates on trustworthiness, and ethical implications. The data science revolution is fueled by the convergence of advancements, including the big‐data revolution, GPU market expansion, and significant investments in Artificial Intelligence and high performance computing by both institutional and private players. This transformation has given rise to a trans‐disciplinary community that has investigated a wide range of questions under the lens of machine learning (ML) approaches and has generally advanced the field of computational methods within the broader geosciences community, the core of the American Geophysical Union (AGU) membership. Responding to an unmet demand in the existing worldwide editorial offer, the Journal of Geophysical Research: Machine Learning and Computation aims to serve as an intellectual crucible, fostering collaborations across multiple geophysical disciplines and data scientists. The journal welcomes papers with strong methodological developments that allow for geoscience advancements grounded in specific computational and data‐driven methods, leveraging ML as well as innovative computational strategies, and leading to breakthrough discoveries and original scientific outcomes. Authors are encouraged to balance succinctness in introducing methods with a thorough exploration of the novelty of the work proposed and its future applications placing special emphasis on the connection between the data science approach and the scientific outcome, considering a broad readership. Emphasis on result reproducibility aligns with AGU guidance, inviting active participation from the community in shaping geophysical research in the era of machine learning and computation.

Macroeconomic determinants of poverty in South Africa: the role of investments in artificial intelligence

Macroeconomic determinants of poverty in South Africa: the role of investments in artificial intelligence

The role of artificial intelligence in corporate digital strategies: evidence from China

PurposeIn the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.Design/methodology/approachTo explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.FindingsThe investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.Originality/valueThis study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

Firm digital investments and capacity utilisation: A perspective of factor inputs

Data resources empower traditional inputs and promote capacity utilisation. Previous research has proven the positive role of digitisation on capacity utilisation from the perspectives of information asymmetry and technological innovation, but there is still a lack of theoretical foundation. This study proposes a model of the impact of information and communications technology capital on capacity utilisation. We empirically test the relationship between firms' digital investments and capacity utilisation using the data of Chinese manufacturing-listed companies from 2007 to 2021. First, an inverted U-shaped feature exists between firm digital investments and capacity utilisation, and the main mechanisms are the ‘cost effect’, ‘capital allocation effect’ and ‘labour input effect’. Second, firms' bargaining power, industrial technology density and regional market development influence capacity utilisation when firms engage in digital investments. Third, narrowing the digital gaps within industries and regions and increasing investments in artificial intelligence, big data and digital technology applications can enhance the non-linear impact of firm digitisation on capacity utilisation. Fourth, digital transformation can resist the negative impact of external shocks on capacity utilisation to some extent. Our research provides theoretical and practical support to facilitate the high-quality development of firms.

Artificial Intelligence (AI) Literacy, An Investment for Enhancing Educators’ Skills in AI Powered Primary Schools in Nigeria

A descriptive survey was conducted to investigate whether an investment in artificial intelligence (AI) literacy predicts the enhancement of educators’ skills for them to be effective in an AI powered environment in state primary schools in Nigeria. Three research questions and null hypotheses were formulated at .05 level of significance for the study. 1000 teachers were recruited from across five schools located in five geopolitical zones of the country using cluster sampling. Artificial intelligence literacy and educators’ skills enhancement questionnaire (AILESEQ) was deployed to generate data. Based on the human capital development theory, data was analysed via the simple linear regression. Findings suggest that knowledge of AI, use of AI devices and understanding the ethical issues about AI technologies significantly predict the enhancement of educators’ skills in AI powered primary schools in Nigeria. It is recommended that government should organise training workshops on AI literacy for educators in schools; the policy on education needs review to produce a framework for the introduction of AI in education (AIIE) to benefit the teachers; the curriculum of teacher education has to be revised to feature AIIE; AI literacy should be a requirement for recruiting new teachers; schools have to produce guidelines for the application of AI devices; government can create a demonstration school to trial the AI initiative; government can commit one percent of value added tax to fund AIIE; awareness campaigns should be organised regularly via various media to educate relevant stakeholders about the benefits of AIIE.

КОНЦЕПТУАЛІЗАЦІЯ ЦИФРОВОЇ ТРАНСФОРМАЦІЇ У МІЖНАРОДНОМУ БІЗНЕСІ

Digital technologies fundamentally change business strategies, the mechanism of business organization both at the national and international level. Thanks to the automation of business processes, companies optimize costs, generate new types of products, transform their own business models, expanding their global presence. Based on the analysis of the existing scientometric base of the digital transformation of international business, the methodological foundations of its conceptualization were determined and a conceptual model consisting of a cognitive core, social environment, and technological superstructure was developed. It is substantiated that the methodological foundations of the conceptualization of digital transformation are long-term goal setting, digitalization, effective strategy, the principle of "empowerment", bimodal IT architecture, investment in artificial intelligence and digital competences. It was revealed that the concept of digital transformation of business lies in the plane of partnership, human-centered, knowledge, information technology, strategic dimensions and digitalization dimension. The article considers the conceptualization of digital transformation in international business. Digital transformation is a key factor in the modernization and development of modern companies operating on a global scale. The authors analyze the main trends, technologies and approaches that contribute to the introduction of digital innovations in international business, including artificial intelligence, big data, the Internet of Things, blockchain and cloud computing. Special attention is paid to the analysis of the impact of digital transformation on the competitiveness of companies in the international arena, optimization of business processes, improvement of supply chains and improvement of the quality of customer service. In addition, challenges related to the implementation of the latest technologies are considered, in particular, issues of cyber security, legal regulation and change management in organizations. The article emphasizes the importance of strategic planning and the development of digital competencies for successful adaptation to the new conditions of the business environment. Ultimately, the article highlights the importance of digital transformation for international business development, particularly as a tool.