Artificial Intelligence Research Research Articles

ARTIFICIAL INTELLIGENCE AS AN INNOVATIVE TOOL OF CORRUPTION PREVENTION IN JUDICIARY: THE ADVANTAGES

There has been made an attempt in the article to assess the possibility and expediency of application of information and communication technologies and their highest manifestation – artificial intelligence – as an anti-corruption tool in the judiciary. The essence and modern forms of the artificial intelligence phenomenon and its connection with information and communication technologies have been analyzed. There has been researched national and foreign legal regulation on the application of artificial intelligence both in the field of public administration in general and in the field of the judiciary and administration of justice in particular. The cases of the most «significant» corruption events in the judiciary have been presented and an attempt to find out the reasons of the failure of traditional tools to prevent corruption has been made. There has been called into question the ability of the state to overcome such a negative socio-power phenomenon as corruption without the implementation of radical steps which have long been overdue and became even more actualized during the armed conflict. Some positive examples of the information and communication technologies use in the judiciary have been demonstrated. Separate advantages of using artificial intelligence as a new tool for preventing corruption in the administration of justice have been briefly systematized. There has been emphasized that artificial intelligence due to its impartiality is fully capable to ensure the realization of the right to a fair trial, preserving time, financial and human resources, and reducing the level of corruption. At the same time, there has been underlined that without studying the risks and threats of using computer programs at this level of their development, relying only on them as a panacea for the destruction of corruption practices is imprudent and premature. Finally, an impulse for further research of artificial intelligence and its features as a tool of corruption prevention in the judicial system has been given.

Research trends in the use of artificial intelligence in higher education

The latest technological advancements have greatly interested researchers in artificial intelligence (AI) in education. In parallel, researchers have expressed concern about using and applying AI in education. However, there is a shortage of research that comprehensively and holistically examines trends in the use of AI in higher education. Hence, this study aimed to comprehensively analyze and assess AI research trends in higher education. In the SCOPUS database, we conducted a bibliometric analysis of 1,563 articles on research on AI in education. Our results revealed that the use of AI in education has increased dramatically from 2004 to 2023. In particular, a dramatic increase and peak exist after 2019. We also found limited interaction among scholars studying AI. Furthermore, our findings indicate that most of the most influential institutions are located in developed countries. Moreover, our findings demonstrated that AI research primarily concentrated on comprehending the impact of AI-based instruction, with the majority of these studies taking place in engineering education between 2017 and 2020. We also noticed that research on medical education in higher education occurred between 2015 and 2017. In addition, before 2015, research used AI in medical education as a teaching method to implement problem-based learning in higher education.

Artificial Intelligence and Linguistic Landscape research

Abstract This article explores applications of artificial intelligence (AI) technologies in Linguistic Landscape research. Traditionally, LL research has relied on manual data collection and analysis, often involving photographs of public signage, advertisements, and other visual language displays. However, this manual approach can present challenges, including time-consuming data collection, inconsistent data quality, and potential researcher bias. Two AI technologies in particular hold promise for addressing these challenges in LL research: computer vision (CV) and large language models (LLMs). CV automates the identification and extraction of text from images, improving data accuracy and enabling large-scale image analysis. LLMs, based on natural language processing, can detect, translate, and interpret multilingual text. This article explores the affordances and challenges of using AI technologies in LL research and discusses methods to improve data collection, enhance accuracy, and support the analysis of multilingual environments. It also raises ethical issues and limitations of the technologies.

Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives.

Autoimmune rheumatic diseases (ARD) present a significant global health challenge characterized by a rising prevalence. These highly heterogeneous diseases involve complex pathophysiological mechanisms, leading to variable treatment efficacies across individuals. This variability underscores the need for personalized and precise treatment strategies. Traditionally, clinical practices have depended on empirical treatment selection, which often results in delays in effective disease management and can cause irreversible damage to multiple organs. Such delays significantly affect patient quality of life and prognosis. Artificial intelligence (AI) has recently emerged as a transformative tool in rheumatology, offering new insights and methodologies. Current research explores AI's capabilities in diagnosing diseases, stratifying risks, assessing prognoses, and predicting treatment responses in ARD. These developments in AI offer the potential for more precise and targeted treatment strategies, fostering optimism for enhanced patient outcomes. This paper critically reviews the latest AI advancements for predicting treatment responses in ARD, highlights the current state of the art, identifies ongoing challenges, and proposes directions for future research. By capitalizing on AI's capabilities, researchers and clinicians are poised to develop more personalized and effective interventions, improving care and outcomes for patients with ARD.

The role of artificial intelligence in decolonising academic writing for inclusive knowledge production

This conceptual article delves into the integration of Artificial Intelligence (AI) in academia, focusing on its potential to decolonise academic writing for inclusive knowledge production. The paper begins with an overview of decolonisation in academic discourse and introduces AI's emerging role in this field. It then reviews the literature on decolonial perspectives in academia, the challenges faced by non-native English speakers in academic writing, and previous AI research in education, highlighting gaps that necessitate a decolonial and critical approach. The theoretical framework combines decoloniality and critical theory, linking these to empower non-native English-speaking academics. Using a theory synthesis design, the discussion explores this group's unique challenges in academic writing and how AI, specifically applications like ChatGPT, can be a transformative tool for inclusivity in publication spaces. It critically examines how AI can contribute to decolonising academic knowledge writing. However, it also addresses potential challenges and ethical considerations in merging AI with decolonial perspectives. The article forecasts future AI developments and their implications for decolonising academic experiences, emphasising the need for inclusive technological advancements. In conclusion, the article stresses AI's potential role in decolonising academic practices and calls for further interdisciplinary dialogue and exploration. Recommendations for universities, academics, policymakers, and curriculum designers, as well as implications for decolonial and critical discourses, are provided.

Navigating the Nexus of Artificial Intelligence and Renewable Energy for the Advancement of Sustainable Development Goals

The integration of artificial intelligence (AI) into renewable energy and sustainability represents a transformative approach toward achieving sustainable development goals (SDGs), especially SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). This study utilized the PRISMA framework to conduct a systematic review, focusing on the role of AI in renewable energy and sustainable development. This research utilized Scopus’s curated AI research area, which employs text mining to refine AI concepts into unique keywords. Further refinement via the All Science Journals Classification system and SDG-mapping filters narrowed the focus to publications relevant to renewable energy and SDGs. By employing the BERTopic modeling approach, our study identifies major topics, such as enhancing wind speed forecasts, performance analysis of fuel cells, energy management in elective vehicles, solar irradiance prediction, optimizing biofuel production, and improving energy efficiency in buildings. AI-driven models offer promising solutions to address the dynamic challenges of sustainable energy. Insights from academia-industry collaborations indicate that such partnerships significantly accelerate sustainable-energy transitions, with a focus on AI-driven energy storage, grid management, and renewable-energy forecasting. A global consensus on the critical role of investing in technology-driven solutions for energy sustainability was underscored by the relationship between funding data and global R&D spending patterns. This study serves as a resource for practitioners to harness AI technologies for renewable energy, where for example, AI’s accurate wind speed predictions can increase wind farm efficiency, highlighting the necessity of innovation and collaboration for sustainable development.

Frontier AI developers need an internal audit function.

This article argues that frontier artificial intelligence (AI) developers need an internal audit function. First, it describes the role of internal audit in corporate governance: internal audit evaluates the adequacy and effectiveness of a company's risk management, control, and governance processes. It is organizationally independent from senior management and reports directly to the board of directors, typically its audit committee. In the Institute of Internal Auditors' Three Lines Model, internal audit serves as the third line and is responsible for providing assurance to the board, whereas the combined assurance framework highlights the need to coordinate the activities of internal and external assurance providers. Next, the article provides an overview of key governance challenges in frontier AI development: Dangerous capabilities can arise unpredictably and undetected; it is difficult to prevent a deployed model from causing harm; frontier models can proliferate rapidly; it is inherently difficult to assess frontier AI risks; and frontier AI developers do not seem to follow best practices in risk governance. Finally, the article discusses how an internal audit function could address some of these challenges: Internal audit could identify ineffective risk management practices; it could ensure that the board of directors has a more accurate understanding of the current level of risk and the adequacy of the developer's risk management practices; and it could serve as a contact point for whistleblowers. But frontier AI developers should also be aware of key limitations: Internal audit adds friction; it can be captured by senior management; and the benefits depend on the ability of individuals to identify ineffective practices. In light of rapid progress in AI research and development, frontier AI developers need to strengthen their risk governance. Instead of reinventing the wheel, they should follow existing best practices. Although this might not be sufficient, they should not skip this obvious first step.

The Framework of GeoSOT-3D Grid Modeling for Spatial Artificial Intelligence

Abstract. With the development of society, spatial artificial intelligence (spatial AI) research is gradually able to play a greater role. However, spatial AI has problems such as data alignment, poor interpretability, and cross domain learning. Therefore, this paper proposes an innovative GeoSOT-3D grid modeling framework for spatial AI research, which enhances the application capabilities of spatial AI. Grid modeling will be able to run through the upstream and downstream of spatial AI research, providing encoding calculations and spatial neighborhood embedding matrices for spatial data. This paper also uses task examples to demonstrate how to effectively organize and index spatial data using GeoSOT-3D grids and conduct spatial AI research. The use of GeoSOT-3D grids for spatial AI analysis has enormous potential and broad application prospects, which will help promote the further development and application of spatial AI.

A Rossian Method for Applying Principles in AI

In the past several years there has been a rapid development of new technologies, applications, organizations, and institutions in the area of artificial intelligence (AI). At the same time, ethical reasoning about AI has not been able to keep up with the speed of these advances. As a result, developers are left to rely on existing rules, professional codes, policies and personal ethics which may not provide the appropriate guidance about ethical conduct and may require greater specificity (O'Leary). Many commentators have acknowledged the need for a clearer understanding of ethical values and principles to guide AI research. Drawing on insights from the formation of the field of biomedical ethics, we argue that AI ethics should make use of the method based on prima facie duties derived from W.D. Ross’ approach to ethics. A Rossian approach has proved influential in biomedical ethics. We further propose a modification to the list of principles proposed by Floridi and Cowls, arguing that the principles of explicability and accountability should be separated for ease of application. We argue that this method of applying principles is just what has been missing in AI ethics and is the crucial link between the now common lists of principles and putting them into practice in a way that can inform actual developments on the ground.

Green AI in the finance industry: Exploring the impact of feature engineering on the accuracy and computational time of Machine Learning models

As research and practice in Artificial Intelligence (AI) applications rapidly expand, the support for AI deployment is also increasing. While the abundance of data allows for sophisticated feature engineering techniques that can enhance accuracy, it is crucial to highlight both the computational costs and the efficiency with which these models operate. This paper compares the processing time and accuracy of individual and hybrid Machine Learning (ML) models in predicting customer loyalty within financial contexts. Frameworks that incorporate feature engineering and green AI principles are used separately in both individual and hybrid approaches. The individual models are the commonly used regressor-based algorithms applied to business problems. The hybrid models first use k-Means to cluster customers, followed by the application of individual regressor-based models (e.g., decision trees, gradient boosting, and LightGBM). The present results show that using fewer features results in only a marginally lower accuracy compared to models with more features (a difference of ≈0.01 in MAE when comparing the use of 18 versus 85 features). Additionally, this article clearly demonstrate the trade-off between higher accuracy and longer computational time in hybrid ML models versus lower accuracy and shorter computational time in individual models when predicting customer loyalty. Hybrid models exhibit a lower MSE (≈0.88) compared to individual models (≈0.91). These findings provide managers with insights on selecting the most appropriate model based on their organization’s specific needs.

Harnessing Artificial Intelligence for Advancing Medical Manuscript Composition: Applications and Ethical Considerations.

Scientific medical manuscripts are fundamental to advancing research and enhancing patient care. With the emergence of artificial intelligence (AI), the process of composing such manuscripts has witnessed profound transformations. This review delves into the multifaceted role of AI in medical manuscript composition, analyzing its applications, benefits, drawbacks, and ethical implications. Employing a comprehensive narrative review methodology, we explored databases such as PubMed, Google Scholar, and Science Direct. The review charts the evolution of AI in medical writing, from basic word processing to sophisticated neural network-based models like GPT-3 and GPT-4. Various AI-powered tools such as ChatGPT, Google Bard, Elicit, and Consensus AI are examined in terms of their functionalities and contributions to research and medical writing. While AI technologies offer notable advantages in automating content creation and boosting research productivity, concerns persist regarding overreliance, potential homogenization of writing styles, and ethical considerations such as originality and authorship. Because of this concern, some companies are restricting the use of AI in peer review processes, medical examinations, etc. It is crucial to strike a balance in integrating AI tools, ensuring human oversight, conducting thorough algorithm audits, addressing financial implications, and upholding academic integrity. The review underscores the transformative potential of AI in medical manuscript composition while emphasizing the ongoing significance of human expertise, creativity, and ethical responsibility in scientific communication. Recommendations are provided for the effective integration of AI tools into medical writing processes, emphasizing collaborative efforts between AI developers, researchers, and journal editors to navigate ethical dilemmas and maximize the benefits of AI-driven advancements in scientific publishing.

AI types and organizational implications in various task contexts: a quantitative review from China

ABSTRACT This research delves into the nature of artificial intelligence (AI) and its influence on employees. We review existing definitions and applications of AI, categorizing it into three types: embodied robots, generative AI, and predictive AI. By systematically analysing the frequency of various application scenarios, study levels, and subjects, we identify prevalent trends and gaps in current AI research and explore the impact of AI on employees across different scenarios. The study highlights the significant potential of AI to propel workforce development and foster innovation within China’s dynamic economy, calling for more comprehensive research on AI applications across various industries.

Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance

Calls for engagement with the public in Artificial Intelligence (AI) research, development, and governance are increasing, leading to the use of surveys to capture people's values, perceptions, and experiences related to AI. In this paper, we critically examine the state of human participant surveys associated with these topics. Through both a reflexive analysis of a survey pilot spanning six countries and a systematic literature review of 44 papers featuring public surveys related to AI, we explore prominent perspectives and methodological nuances associated with surveys to date. We find that public surveys on AI topics are vulnerable to specific Western knowledge, values, and assumptions in their design, including in their positioning of ethical concepts and societal values, lack sufficient critical discourse surrounding deployment strategies, and demonstrate inconsistent forms of transparency in their reporting. Based on our findings, we distill provocations and heuristic questions for our community, to recognize the limitations of surveys for meeting the goals of engagement, and to cultivate shared principles to design, deploy, and interpret surveys cautiously and responsibly.

Social Scoring Systems for Behavioral Regulation: An Experiment on the Role of Transparency in Determining Perceptions and Behaviors

Recent developments in artificial intelligence research have advanced the spread of automated decision-making (ADM) systems used for regulating human behaviors. In this context, prior work has focused on the determinants of human trust in and the legitimacy of ADM systems, e.g., when used for decision support. However, studies assessing people's perceptions of ADM systems used for behavioral regulation, as well as the effect on behaviors and the overall impact on human communities are largely absent. In this paper, we experimentally investigate people's behavioral adaptations to, and their perceptions of an institutionalized decision-making system, which resembled a social scoring system. Using social scores as incentives, the system aimed at ensuring mutual fair treatment between members of experimental communities. We explore how the provision of transparency affected people’s perceptions, behaviors, as well as the well-being of the communities. While a non-transparent scoring system led to disparate impacts both within as well as across communities, transparency helped people develop trust in each other, create wealth, and enabled them to benefit from the system in a more uniform manner. A transparent system was perceived as more effective, procedurally just, and legitimate, and led people to rely more strongly on the system. However, transparency also made people strongly discipline those with a low score. This suggests that social scoring systems that precisely disclose past behaviors may also impose significant discriminatory consequences on individuals deemed non-compliant.

The Impact of Responsible AI Research on Innovation and Development

Translational research, especially in the fast-evolving field of Artificial Intelligence (AI), is key to converting scientific findings into practical innovations. In Responsible AI (RAI) research, translational impact is often viewed through various pathways, including research papers, blogs, news articles, and the drafting of forthcoming AI legislation (e.g., the EU AI Act). However, the real-world impact of RAI research remains an underexplored area. Our study aims to capture it through two pathways: patents and code repositories, both of which provide a rich and structured source of data. Using a dataset of 200,000 papers from 1980 to 2022 in AI and related fields, including Computer Vision, Natural Language Processing, and Human-Computer Interaction, we developed a Sentence-Transformers Deep Learning framework to identify RAI papers. This framework calculates the semantic similarity between paper abstracts and a set of RAI keywords, which are derived from the NIST's AI Risk Management Framework; a framework that aims to enhance trustworthiness considerations in the design, development, use, and evaluation of AI products, services, and systems. We identified 1,747 RAI papers published in top venues such as CHI, CSCW, NeurIPS, FAccT, and AIES between 2015 and 2022. By analyzing these papers, we found that a small subset that goes into patents or repositories is highly cited, with the translational process taking between 1 year for repositories and up to 8 years for patents. Interestingly, impactful RAI research is not limited to top U.S. institutions, but significant contributions come from European and Asian institutions. Finally, the multidisciplinary nature of RAI papers, often incorporating knowledge from diverse fields of expertise, was evident as these papers tend to build on unconventional combinations of prior knowledge.

From human-centered to social-centered artificial intelligence: Assessing ChatGPT's impact through disruptive events

Large language models (LLMs) and dialogue agents represent a significant shift in artificial intelligence (AI) research, particularly with the recent release of the GPT family of models. ChatGPT's generative capabilities and versatility across technical and creative domains led to its widespread adoption, marking a departure from more limited deployments of previous AI systems. While society grapples with the emerging cultural impacts of this new societal-scale technology, critiques of ChatGPT's impact within machine learning research communities have coalesced around its performance or other conventional safety evaluations relating to bias, toxicity, and “hallucination.” We argue that these critiques draw heavily on a particular conceptualization of the “human-centered” framework, which tends to cast atomized individuals as the key recipients of technology's benefits and detriments. In this article, we direct attention to another dimension of LLMs and dialogue agents’ impact: their effects on social groups, institutions, and accompanying norms and practices. By analyzing ChatGPT's social impact through a social-centered framework, we challenge individualistic approaches in AI development and contribute to ongoing debates around the ethical and responsible deployment of AI systems. We hope this effort will call attention to more comprehensive and longitudinal evaluation tools (e.g., including more ethnographic analyses and participatory approaches) and compel technologists to complement human-centered thinking with social-centered approaches.

Research Progress on Food-Derived Bioactive Peptides: An Overview of the 3rd International Symposium on Bioactive Peptides.

Interest in food-derived bioactive peptides is on the rise. In 2023, the 3rd International Symposium on Bioactive Peptides (ISBP) was held in Niagara Falls, Canada, to provide a platform for knowledge exchange, networking, and collaboration among researchers in this field. This article aims to provide a high-level overview of the key progress and emerging trends in bioactive peptides based on the 3rd ISBP. This review highlights the production of bioactive peptides from sustainable sources through the integration of artificial intelligence and wet-lab research, the emerging roles of bioactive peptides in cognitive function, and the ability of peptides to act as taste modifiers. The emerging research trend in bioactive peptides focuses on utilizing novel processing technologies, understanding peptide-receptor interactions, applying omics in mechanistic studies, conducting clinical trials, and facilitating product development and commercialization.

How Is the Insight Overview of Artificial Intelligence Research in High School?

How Is the Insight Overview of Artificial Intelligence Research in High School?

PACE: Participatory AI for Community Engagement

Public sector leverages artificial intelligence (AI) to enhance the efficiency, transparency, and accountability of civic operations and public services. This includes initiatives such as predictive waste management, facial recognition for identification, and advanced tools in the criminal justice system. While public-sector AI can improve efficiency and accountability, it also has the potential to perpetuate biases, infringe on privacy, and marginalize vulnerable groups. Responsible AI (RAI) research aims to address these concerns by focusing on fairness and equity through participatory AI. We invite researchers, community members, and public sector workers to collaborate on designing, developing, and deploying RAI systems that enhance public sector accountability and transparency. Key topics include raising awareness of AI's impact on the public sector, improving access to AI auditing tools, building public engagement capacity, fostering early community involvement to align AI innovations with public needs, and promoting accessible and inclusive participation in AI development. The workshop will feature two keynotes, two short paper sessions, and three discussion-oriented activities. Our goal is to create a platform for exchanging ideas and developing strategies to design community-engaged RAI systems while mitigating the potential harms of AI and maximizing its benefits in the public sector.

Virtual Laboratory and Artificial Intelligence in Science Education: Bibliometric Analysis Based on Scopus Source

This study examines the development of research related to virtual laboratories and artificial intelligence in education. With the assistance of VOSviewer, this study aims to conduct computational mapping on bibliometric data of articles on the keywords: artificial intelligence, virtual laboratories, and education. The method used in this research is bibliometric analysis using VOSviewer. Article data obtained from Scopus-based data, article titles, and abstracts are used to guide the search process by referring to the keywords "laboratorium virtual" and "artificial intelligence in education". It found 359 allegedly relevant articles. The study period used as study material in this study was for the last 5.5 years, namely from 2018 to June 2023. The results of laboratorium virtual and artificial intelligence research in education can be separated into 3 terms, namely artificial intelligence, laboratorium virtual, and education. The term artificial intelligence has 304 links with a total link strength of 12065, the term Laboratorium virtual has 416 links with a total link strength of 5451 links with an accuracy of 212 terms, and the term education has 399 links with a total link strength of 3142 accuracies of 113. The analysis results indicate a significant increase in research related to virtual laboratories and artificial intelligence in education over the past four years. However, the relationship between virtual laboratories and artificial intelligence in education is still relatively rare. In fact, there is no existing relationship between virtual laboratories and artificial intelligence. Based on this information, the opportunity for developing research on virtual laboratories based on artificial intelligence in the field of education is substantial.