Artificial Intelligence Research Articles

Strategic Transformation of E-Commerce Big Data Classification and Mining Algorithms Based on Artificial Intelligence Era

Strategic Transformation of E-Commerce Big Data Classification and Mining Algorithms Based on Artificial Intelligence Era

A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?

Explainable artificial intelligence (XAI) has gained significant attention, especially in AI-powered autonomous and adaptive systems (AASs). However, a discernible disconnect exists among research efforts across different communities. The machine learning community often overlooks “explaining to whom,” while the human-computer interaction community has examined various stakeholders with diverse explanation needs without addressing which XAI methods meet these requirements. Currently, no clear guidance exists on which XAI methods suit which specific stakeholders and their distinct needs. This hinders the achievement of the goal of XAI: providing human users with understandable interpretations. To bridge this gap, this paper presents a comprehensive XAI roadmap. Based on an extensive literature review, the roadmap summarizes different stakeholders, their explanation needs at different stages of the AI system lifecycle, the questions they may pose, and existing XAI methods. Then, by utilizing stakeholders’ inquiries as a conduit, the roadmap connects their needs to prevailing XAI methods, providing a guideline to assist researchers and practitioners to determine more easily which XAI methodologies can meet the specific needs of stakeholders in AASs. Finally, the roadmap discusses the limitations of existing XAI methods and outlines directions for future research.

From Backlogs to Bots: Generative AI's Impact on Agile Role Evolution

ABSTRACTThis position paper investigates the transformative impact of generative artificial intelligence (GenAI) on Agile roles. Focusing on the product owner, developer, and scrum master, we analyze how GenAI redefines traditional tasks, encouraging a shift towards more strategic and creative functions. Through practical experience, we illustrate AI's role in enhancing Agile processes, its practices and emphasize the need for Agile practitioners to integrate AI understanding. These results highlight the balance between GenAI's efficiencies and Agile's human‐centric principles, proposing research directions for AI‐enriched Agile practices that promise to drive innovation and maintain the agility in a technologically progressive era.

Vapor–liquid phase equilibrium prediction for mixtures of binary systems using graph neural networks

AbstractVapor–liquid phase equilibrium (VLE) plays a crucial role in chemical process design, process equipment control, and experimental process simulation. However, experimental acquisition of VLE data is a challenging and complex task. As an alternative to experimentation, VLE data prediction offers great convenience and utility. In this article, an artificial intelligence network is proposed to predict the temperature and the vapor phase composition of binary mixtures. We constructed a graph neural network (GNN) and designed an uncertainty‐aware learning and inference mechanism (UALF) in the prediction process. The model was tested on both a self‐constructed dataset and a publicly available dataset. The results demonstrate that the proposed method effectively reveals the phase equilibrium properties of the target data. This work presents a novel approach for predicting vapor–liquid phase equilibrium in binary systems and proposes innovative ideas for investigating phase equilibrium mechanisms and principles.

Telesurgery: humanitarian and surgical benefits while navigating technologic and administrative challenges.

Telesurgery, the remote execution of surgical procedures through telecommunication and robotic systems, has witnessed substantial growth in recent years, promising to address global healthcare disparities and enhance surgical expertise. This paper explores the humanitarian and surgical benefits of telesurgery, emphasizing its potential to provide expert surgical care to underserved regions. Despite its transformative potential, telesurgery faces significant technologic challenges, including issues of data transmission, latency, and the need for advanced robotic platforms. The advent of 5G networks and innovative robotic systems provides a promising technological landscape, yet global disparities in 5G coverage remain a concern. Ethical considerations, ranging from preserving the surgeon-patient relationship to addressing patient vulnerability and conflicts of interest, are pivotal aspects that demand attention. The paper underscores the importance of clear regulatory frameworks and international collaboration to navigate legal complexities and ensure ethical standards. As telesurgery progresses, integrating artificial intelligence, augmented reality, and haptic feedback technologies holds promise for further advancements. Despite these challenges, telesurgery has the potential to achieve equitable access to expert surgical care; however, it requires a collective effort to overcome its intricate technologic and administrative hurdles.

Impact of regulatory and other changes on Australian unions’ strategies and campaigns, 2023–2024

This article reviews the challenges and opportunities facing Australian trade unions in 2023–2024. It covers industry challenges for specific unions and the changes introduced by the Federal Labor Government through its Closing the Loopholes Amendments to the Fair Work Act. It argues that the Australian Council of Trade Unions (ACTU) and individual unions need to take the opportunity provided by the Federal Government's legal changes to make gains for members so that they are difficult to overturn if the federal government changes. It analyses campaigns and strategies of unions around challenges specific to them: for example, the recruitment of gig workers by the Transport Workers’ Union and campaigns by the ACTU and the Media and Entertainment Arts Alliance around inappropriate use of artificial intelligence by employers seeking to replace workers and usurp employees’ ownership rights to their work. It considers amalgamations (mergers) and disamalgamations (demergers) of concern for some unions. It discusses allegations of inappropriate behaviour facing the Construction and General Division of the Construction, Forestry and Maritime Employees’ Union leading to its forced administration as a concern and possible impediment for the entire union movement if not resolved effectively.

The Effect of Ambient Artificial Intelligence Notes on Provider Burnout.

Healthcare provider burnout is a critical issue with significant implications for individual well-being, patient care, and healthcare system efficiency. Addressing burnout is essential for improving both provider well-being and the quality of patient care. Ambient artificial intelligence (AI) offers a novel approach to mitigating burnout by reducing the documentation burden through advanced speech recognition and natural language processing technologies that summarize the patient encounter into a clinical note to be reviewed by clinicians. To assess provider burnout and professional fulfilment associated with Ambient AI technology during a pilot study, assessed using the Stanford Professional Fulfillment Index (PFI). A pre-post observational study was conducted at University of Iowa Health Care with 38 volunteer physicians and advanced practice providers. Participants used a commercial ambient AI tool, over a 5-week trial in ambulatory environments. The AI tool transcribed patient-clinician conversations and generated preliminary clinical notes for review and entry into the electronic medical record. Burnout and professional fulfillment were assessed using the Stanford PFI at baseline and post-intervention. Pre-test and post-test surveys were completed by 35/38 participants (92% survey completion rate). Results showed a significant reduction in burnout scores, with the median burnout score improving from 4.16 to 3.16 (p=0.005), with validated Stanford PFI cutoff for overall burnout 3.33. Burnout rates decreased from 69% to 43%. There was a notable improvement in interpersonal disengagement scores (3.6 vs. 2.5, p<0.001), although work exhaustion scores did not significantly change. Professional fulfillment showed a modest, non-significant upward trend (6.1 vs. 6.5, p=0.10). Ambient AI significantly reduces healthcare provider burnout and may enhance professional fulfillment. By alleviating documentation burdens, ambient AI can improve operational efficiency and provider well-being. These findings suggest that broader implementation of ambient AI could be a strategic intervention to combat burnout in healthcare settings.

Smart agriculture as source of sustainability in intensive greenhouse production

Abstract The article addresses the interaction between smart agriculture and sustainability within the specific context of intensive greenhouse agriculture in southeast Spain. Combining a literature review and expert interviews, the methodology is based on a qualitative data analysis, whose main goal is to gain a comprehensive understanding of the phenomenon under study. The results identify three key areas of technology that strongly influence this type of agriculture. The first relates to soil, water savings, sensor utilization, and environmental improvement, while the second encompasses robot use, labor reduction, resulting cost improvement, and economic sustainability. Finally, the third group, albeit of lesser importance, links artificial intelligence with ethical issues such as data control, usage, and technological dependence. In summary, the application of smart agriculture is projected to have positive impacts on economic and environmental sustainability, relegating the social dimension to a more distant level. This imbalance suggests that social and ethical aspects could be subordinated to more immediate benefits. The results also suggest that the need for future investments could create polarization in the sector. Not all farmers and businesses can afford these investments, leading to progressive deterioration and even abandonment of agricultural activities in some cases.

Bridging the AI Divide: Human and Responsible AI in News and Media Industries

Artificial intelligence (AI) is revolutionizing media and journalism, reshaping processes, content, and audience relationship. This editorial report discusses the opportunities and challenges AI brings to these industries raised by this collection of studies, emphasizing the importance of integrating ethical principles and human-centric approaches to its development. These articles examine how AI reshapes content creation, distribution, and audience interaction while addressing the ethical, political, and economic implications of its integration. Key contributions include discussions on the ethical challenges posed by AI—such as bias, transparency, and accountability—and the need for robust governance structures to foster trust and fairness in AI-driven media systems. A recurring theme in this issue is the AI divide, highlighting the disparity in access to AI technologies between the Global North and South. The special issue offers insights into how this divide exacerbates existing inequalities in media production and consumption. It also advocates for interdisciplinary approaches and collaborative efforts to ensure AI's benefits are equitably distributed across geographical and socio-economic contexts. By exploring both the opportunities and perils of AI adoption in media and news industries, this special issue serves as a comprehensive examination of the evolving relationship between AI, media, and society, offering pathways for responsible innovation and inclusive AI adoption worldwide.

A Study on Functional Requirements and Inspection Items for AI System Change Management and Model Improvement on the Web Platform

The rapid adoption of artificial intelligence (AI) on the web platform across multiple sectors has highlighted not only its inherent technical hurdles, such as unpredictability and lack of transparency, but also significant societal concerns. These include the misuse of AI technology, invasions of privacy, discrimination fueled by biased data, and infringements of copyright. Such challenges jeopardize the sustainable growth of AI and risk the erosion of societal trust, industry adoption and financial investment. This analysis explores the AI system’s lifecycle, emphasizing the essential continuous monitoring and the need for creating trustworthy AI technologies. It advocates for an ethically oriented development process to mitigate adverse effects and support sustainable progress. The dynamic and unpredictable nature of AI, compounded by variable data inputs and evolving distributions, requires consistent model updates and retraining to preserve the integrity of services. Addressing the ethical aspects, this paper outlines specific guidelines and evaluation criteria for AI development, proposing an adaptable feedback loop for model improvement. This method aims to detect and rectify performance declines through prompt retraining, thereby cultivating robust, ethically sound AI systems. Such systems are expected to maintain performance while ensuring user trust and adhering to data science and web technology standards. Ultimately, the study seeks to balance AI’s technological advancements with societal ethics and values, ensuring its role as a positive, reliable force across different industries. This balance is crucial for harmonizing innovation with the ethical use of data and science, thereby facilitating a future where AI contributes significantly and responsibly to societal well-being.

A review of artificial intelligence in wound care

Our aging population, diabetes, and obesity have fueled the growth of chronic wounds seen throughout the world. Often, wounds are a marker of poor health that leads to increased mortality rates. However, the diagnosis and treatment of these wounds are challenging. Incorrectly differentiating between chronic wounds and other complex conditions can lead to adverse events. Artificial intelligence (AI) has been shown to offer some early benefits, and we hypothesized that it may enhance wound care but also carry some notable risks. We performed a detailed search using PubMed, Scopus, Cumulated Index in Nursing and Allied Health Literature, and Web of Science for AI applications in wound care. AI was found to be applied to wound diagnosis and characterization, wound monitoring for tissue change, daily therapy, and prevention and prognostics. AI made for more efficient and accurate wound assessments, less painful assessments of chronic wounds, more personalized treatment, and improved prognostic prediction capabilities. AI also allowed for more precise at-home observation and care, facilitating earlier wound treatment as needed. Challenges associated with AI included how to best allocate AI-assisted technologies equitably, how to safely maintain patient data, and how to diversify datasets for algorithm training. Because the algorithms are not transparent, validating findings may be challenging. AI presents a powerful tool in several aspects of advanced wound care and has the potential to improve diagnoses, accelerate healing, reduce pain, and improve the cost-effectiveness of wound care. More research needs to be done into how to best incorporate AI into daily clinical practice while keeping clinicians aware of the potential risks of using these evolving technologies.

Artificial intelligence in management education: transformative potential and challenges

Purpose The purpose of this paper is to define artificial intelligence (AI) and examine its history, positive and negative impacts, ethical and social implications and implementation within management education. This paper offers various suggestions for the use of AI, as well as context surrounding the current AI landscape. Design/methodology/approach The paper uses a narrative review (Sylvester et al., 2013). Findings This paper identifies several areas of AI innovation, including AI tutoring systems, feedback systems for student papers, utilization of AI for innovative lesson plans and the use of AI to predict potential student dropout from a course or institution. In addition, there are significant concerns regarding the lack of ethical guidelines with current AI. Practical implications Practical implications include the ability to immediately use certain AI tools to enhance lesson plans as well as enhance student work using AI as a tool. Originality/value This paper was originally created as a conference presentation and presented at the society for advancement of management (SAM) International Business Conference before being reworked to be submitted to the journal. All content in this paper is original in their creation.

Made with Artificial Intelligence: The Effect of Artificial Intelligence Disclosures in Instagram Advertisements on Consumer Attitudes

Artificial intelligence (AI) has the potential to disrupt the advertising industry as marketers and brands can leverage its power to create highly engaging personalized content. However, the usage of AI is prone to bias and misinformation and can be used to manipulate. Therefore, various lawmakers such as the European Union aim to enforce AI disclosure messages to protect consumers. But the implications of such disclosures have not yet been studied. This paper draws on existing theories in persuasion knowledge, disclosure theory, inferences of manipulative intent, and AI aversion to develop a model to understand consumer attitudes toward AI disclosures in Instagram advertisements. A three-condition between-subjects online experiment ( Nfinal = 161) was conducted to test the model. The data were analyzed using a moderated mediation model. AI disclosures lead to a direct decrease in advertising attitude. In addition, AI disclosures lead to a decrease in brand attitude only when consumers have high AI aversion. There were no effects of AI disclosures on source credibility. These effects were mediated by inferences of manipulative intent. However, participants who viewed the AI disclosure had lower inferences of manipulative intent and then participants who did not view the AI disclosure. Furthermore, no differences were found between AI disclosures pertaining to the use of AI in the creation of the image or the text. Implications are discussed from both theoretical and managerial viewpoints and highlight why the use of AI on social media for advertising purposes should be limited as it will become more transparent in the future.

Accuracy of Prospective Assessments of 4 Large Language Model Chatbot Responses to Patient Questions About Emergency Care: Experimental Comparative Study.

Recent surveys indicate that 48% of consumers actively use generative artificial intelligence (AI) for health-related inquiries. Despite widespread adoption and the potential to improve health care access, scant research examines the performance of AI chatbot responses regarding emergency care advice. We assessed the quality of AI chatbot responses to common emergency care questions. We sought to determine qualitative differences in responses from 4 free-access AI chatbots, for 10 different serious and benign emergency conditions. We created 10 emergency care questions that we fed into the free-access versions of ChatGPT 3.5 (OpenAI), Google Bard, Bing AI Chat (Microsoft), and Claude AI (Anthropic) on November 26, 2023. Each response was graded by 5 board-certified emergency medicine (EM) faculty for 8 domains of percentage accuracy, presence of dangerous information, factual accuracy, clarity, completeness, understandability, source reliability, and source relevancy. We determined the correct, complete response to the 10 questions from reputable and scholarly emergency medical references. These were compiled by an EM resident physician. For the readability of the chatbot responses, we used the Flesch-Kincaid Grade Level of each response from readability statistics embedded in Microsoft Word. Differences between chatbots were determined by the chi-square test. Each of the 4 chatbots' responses to the 10 clinical questions were scored across 8 domains by 5 EM faculty, for 400 assessments for each chatbot. Together, the 4 chatbots had the best performance in clarity and understandability (both 85%), intermediate performance in accuracy and completeness (both 50%), and poor performance (10%) for source relevance and reliability (mostly unreported). Chatbots contained dangerous information in 5% to 35% of responses, with no statistical difference between chatbots on this metric (P=.24). ChatGPT, Google Bard, and Claud AI had similar performances across 6 out of 8 domains. Only Bing AI performed better with more identified or relevant sources (40%; the others had 0%-10%). Flesch-Kincaid Reading level was 7.7-8.9 grade for all chatbots, except ChatGPT at 10.8, which were all too advanced for average emergency patients. Responses included both dangerous (eg, starting cardiopulmonary resuscitation with no pulse check) and generally inappropriate advice (eg, loosening the collar to improve breathing without evidence of airway compromise). AI chatbots, though ubiquitous, have significant deficiencies in EM patient advice, despite relatively consistent performance. Information for when to seek urgent or emergent care is frequently incomplete and inaccurate, and patients may be unaware of misinformation. Sources are not generally provided. Patients who use AI to guide health care decisions assume potential risks. AI chatbots for health should be subject to further research, refinement, and regulation. We strongly recommend proper medical consultation to prevent potential adverse outcomes.

Legal Challenges in Regulating Artificial Intelligence: A Comparative Study of Privacy and Data Protection Laws

This article explores the legal challenges in regulating artificial intelligence (AI) with a focus on privacy and data protection laws through a qualitative, comparative study. By employing a library research methodology, the study analyzes existing literature and legal frameworks across multiple jurisdictions to identify common trends, gaps, and inconsistencies in AI regulation. It investigates how various countries, including the European Union, the United States, and emerging economies, have developed or are developing legal instruments to address privacy concerns raised by AI technologies. The study highlights key issues such as the tension between technological innovation and regulatory constraints, the adequacy of current legal standards in addressing AI-specific risks, and the role of international cooperation in harmonizing data protection laws. The comparative analysis reveals significant divergences in how privacy is protected across different regions, particularly in the application of principles like consent, transparency, and accountability. Moreover, the article identifies critical gaps in existing frameworks, including the lack of clarity in AI accountability, data sovereignty, and enforcement mechanisms. The findings underscore the need for a more coherent and globally coordinated approach to regulating AI that balances privacy protection with technological advancement. Ultimately, the study calls for reforms that ensure robust privacy safeguards while fostering innovation in AI.

Assessing the Impact of Generative Pre-Trained Transformers on AI Literacy and Public Awareness in India

Generative Pre-Trained Transformers (GPTs), a class of Artificial Intelligence (AI) models capable of producing human-like responses, have become widely adopted since their release. In India, this development raises questions about whether GPTs have influenced public awareness and interest in AI. Understanding this influence is crucial, particularly in the context of AI literacy. This study examines the impact of GPT adoption on creating increased awareness and public interest in AI in India. AI literacy is assessed using the four-factor framework proposed by Ng et al., (2021): knowledge and understanding of AI, uses and applications, evaluation and creation of new solutions, and ethical considerations. Information-seeking behavior serves as the theoretical foundation for examining changes in public interest across these dimensions. Google search volumes from 2020 to 2024 are analyzed to measure public engagement, with data divided into two periods: 2.5 years prior to and 1.5 years following the initial public release of ChatGPT in November 2022. Welch’s t-test is applied to assess changes in search volumes across these time periods. The results indicate a statistically significant increase in interest across all four aspects of AI literacy. The adoption of GPTs has significantly boosted public engagement with AI literacy in India, enhancing awareness and interest across all facets of AI knowledge and application.

Generative artificial intelligence (GAI): From large language models (LLMs) to multimodal applications towards fine tuning of models, implications, investigations

This research explores the transformative integration of artificial intelligence (AI), robotics, and language models, with a particular emphasis on the PaLM-E model. The exploration aims to assess PaLM-E’s decision-making processes and adaptability across various robotic environments, demonstrating its capacity to convert textual prompts into very precise robotic actions. In addition, the research investigates Parameter-Efficient Fine-Tuning (PEFT) techniques, such as Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), providing a historical overview of PEFT and highlighting their significance in enhancing task performance while reducing the number of trainable parameters. The broader scope of Generative AI is examined through an analysis of influential models like GPT-3, GPT-4, Copilot, Bard, LLaMA, Stable Diffusion, Midjourney, and DALL-E. These models’ abilities to process natural language prompts and generate a wide range of outputs are thoroughly investigated. The research traces the historical evolution of AI, from its roots in science fiction to its practical applications today, with a focus on the rise of Generative AI in the 21st century. Furthermore, the research delves into the various modalities of Generative AI, covering applications in text, code, images, and more, and assesses their real-world impact on robotics, planning, and business intelligence. The implications of synthetic data generation for business analytics are also explored. The research inspects within both software and hardware landscapes, comparing local deployment on consumer-grade hardware along with cloud-based services, and underscores the benefits of local model deployment in terms of privacy protection, intellectual property security, and censorship resistance. Ethical considerations are central to this research, addressing concerns related to privacy, security, societal impact, biases, and misinformation. The research proposes ethical guidelines for the responsible development and deployment of AI technologies. Ultimately, this work reveals the deep interconnections between vision, language, and robotics, pushing the boundaries of AI capabilities and providing crucial insights for future AI model development and technological innovation. These findings are intended to guide the field through the emerging challenges of the rapidly evolving Generative AI landscape.

A Bird’s Eye View on the Integration of Artificial Intelligence (AI) In Ayurveda

The integration of Artificial Intelligence (AI) into Ayurveda marks a significant advancement in traditional medical practices. This review article explores AI's multifaceted uses and relevance in the conventional Healthcare industry and Ayurveda. It also highlights the transformative potential of AI in Ayurveda while acknowledging the importance of maintaining the holistic essence of traditional practices. By embracing AI, Ayurveda can evolve to meet contemporary healthcare demands, offering a synergistic approach that combines ancient wisdom with cutting-edge technology. The article concludes with a discussion about the future of Ayurveda in the era of Artificial Intelligence.

Artificial intelligence-aided colonoscopic differential diagnosis between Crohn's disease and gastrointestinal tuberculosis.

Differentiating between Crohn's disease (CD) and gastrointestinal tuberculosis (GITB) is challenging. We aimed to evaluate the clinical applicability of an artificial intelligence (AI) model for this purpose. The AI model was developed and assessed using an internal dataset comprising 1,132 colonoscopy images of CD and 1,045 colonoscopy images of GITB at a tertiary referral center. Its stand-alone performance was further evaluated in an external dataset comprising 67 colonoscopy images of 17 CD patients and 63 colonoscopy images of 14 GITB patients from other institutions. Additionally, a crossover trial involving three expert endoscopists and three trainee endoscopists compared AI-assisted and unassisted human interpretations. In the internal dataset, the sensitivity, specificity, and accuracy of the AI model in distinguishing between CD and GITB were 95.3%, 100.0%, and 97.7%, respectively, with an area under the ROC curve of 0.997. In the external dataset, the AI model exhibited a sensitivity, specificity, and accuracy of 77.8%, 85.1%, and 81.5%, respectively, with an area under the ROC curve of 0.877. In the human endoscopist trial, AI assistance increased the pooled accuracy of the six endoscopists from 86.2% to 88.8% (P=0.010). While AI did not significantly enhance diagnostic accuracy for the experts (96.7% with AI vs 95.6% without, P=0.360), it significantly improved accuracy for the trainees (81.0% vs 76.7%, P=0.002). This AI model shows potential in aiding the accurate differential diagnosis between CD and GITB, particularly benefiting less experienced endoscopists.

ARTIFICIAL INTELLIGENCE IN CRIMINAL JUSTICE MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW

This systematic review, based on 37 articles, explores the role of artificial intelligence (AI) in criminal justice, focusing on its applications in predictive policing, judicial risk assessments, and surveillance, as well as the associated ethical and regulatory challenges. AI has demonstrated substantial potential for improving efficiency and accuracy in criminal justice systems, from optimizing law enforcement resource allocation to providing data-driven risk assessments that support judicial decisions. However, the review identifies significant ethical issues, especially related to algorithmic bias, which can perpetuate existing societal inequalities and disproportionately affect marginalized communities. Concerns around transparency and accountability are prevalent, as the "black-box" nature of many AI algorithms complicates public understanding and trust in AI-driven outcomes. Surveillance tools, including facial recognition and behavioral analysis, enhance real-time threat detection but raise privacy and civil rights concerns, highlighting the need for regulatory oversight. Gaps in legal frameworks suggest the urgency for standardized policies that address data privacy, algorithmic fairness, and accountability in AI applications. The findings underscore that interdisciplinary collaboration, transparent practices, and comprehensive regulatory measures are essential to responsibly integrate AI into criminal justice, balancing technological advancements with justice, equity, and public trust.