Ethical Guidelines Research Articles

Intellectual Property Rights in the Era of Artificial Intelligence

Purpose: The general objective of this study was to explore Intellectual Property Rights in the era of Artificial Intelligence. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to Intellectual Property Rights in the era of Artificial Intelligence. Preliminary empirical review revealed that the era of Artificial Intelligence (AI) has significantly transformed the landscape of Intellectual Property Rights (IPR), presenting both opportunities and challenges. It highlighted that traditional IP laws are increasingly inadequate to address the complexities introduced by AI-generated content, necessitating a rethinking of existing frameworks. The study emphasized the need for recognizing AI's role in the creation of new works and inventions and the importance of developing balanced approaches to protect both human and AI contributions. Ethical considerations, such as accountability, transparency, and fairness, were also deemed crucial in ensuring responsible AI use. Overall, the study called for a comprehensive and proactive approach to integrate AI into IPR, ensuring robust protections while fostering innovation. Unique Contribution to Theory, Practice and Policy: The Technological Determinism Theory, Innovation Diffusion Theory and Legal Realism Theory may be used to anchor future studies on Intellectual Property Rights in the era of Artificial Intelligence. The study recommended revising existing IP laws to explicitly include AI-generated content and inventions, clarifying criteria for authorship and inventorship. It suggested expanding theoretical frameworks to accommodate AI contributions, emphasizing the collaborative nature of human and AI creativity. Practical measures, such as enhanced cybersecurity and legal safeguards for AI-generated trade secrets, were advised. Policy-wise, the study advocated for international cooperation to harmonize IP laws concerning AI. Developing ethical guidelines for responsible AI use and implementing education programs to inform stakeholders about AI and IP implications were also recommended. These measures aimed to create a balanced IP framework supporting innovation while protecting the rights of all stakeholders.

Artificial intelligence in education – opportunities and threats

The article aims to present a comprehensive view of the current state and future of artificial intelligence (AI), with particular emphasis on its opportunities and threats in the context of education. The first section introduces the topic, highlighting the revolutionary nature of AI in today’s world, and discussing the complex definition of artificial intelligence. It then outlines the opportunities for the use of AI in education, such as individualization of the learning process, customization of teaching materials, and support for students with special educational needs. An analysis of potential threats is also conducted, including ethical issues, student data security, algorithmic biases, and the risk of students becoming dependent on AI-based tools. Finally, the article emphasizes the need for a responsible approach and the necessity of regulation, ethical guidelines, and supervision to effectively harness the potential of artificial intelligence in education. The article also encourages further research into the effectiveness of AI implementation in education, especially in the Polish context.

Artificial Intelligence in Head and Neck Surgery: Potentials, Challenges, and Ethical Considerations

The growing prominence of Artificial Intelligence (AI) in medicine introduces both transformative possibilities and potential challenges. Our study focuses on the current status and perceptions of AI in Head and Neck Surgery (HNS), examining its utilization, benefits, ethical concerns, and protective measures. The study aims to illuminate the existing landscape of AI in HNS in Germany. Conducted through a questionnaire, key aspects include its current usage, potential applications (e.g., diagnosis, surgical planning), anticipated benefits (e.g., improved patient care ), and significant ethical concerns (e.g., miscalculations by AI, data privacy). The survey reveals limited AI adoption in HNS, with substantial potential for improvement. Ethical considerations, especially miscalculations by AI and data privacy, emerge as central issues. The survey emphasizes the crucial role of physician control and the need for legal oversight to address concerns related to AI integration. While AI's presence in HNS is modest, the study identifies opportunities for enhancement. Ethical guidelines and practitioner-centric control are vital for discussions surrounding AI integration. This research underscores the significance of ethical considerations and practitioner control in the context of AI in surgical practices. It highlights the demand for targeted training to empower practitioners in navigating the complexities of AI technologies in healthcare, ensuring responsible and patient-centric implementation.

Ethical guidelines for human research on children and adolescents: A narrative review study.

The implementation of human research involving children and adolescents necessitates a nuanced understanding of the distinct ethical complexities and sensitivities that arise. This study aimed to conduct a comprehensive review of ethical guidelines for research with these populations by extensively examining existing standards and applied studies. The review revealed a myriad of challenges inherent in the involvement of children and adolescents as research subjects. The most important ethical challenges relate to the principles of bioethics and their compliance with human studies involving children/adolescents, informed consent, and risk assessment in studies on children/adolescents. To facilitate appropriate participation of youth in research endeavors, meticulous planning is required, in conjunction with a re-examination of the definitions of ethical principles in pediatric research, close monitoring of potential risks and benefits, and the utilization of a combination of innovative and traditional approaches to obtain informed consent that adheres to ethical standards. Performing research with children and adolescents requires special considerations to address the unique ethical issues that can emerge. By adhering to ethical guidelines tailored specifically to these vulnerable populations, researchers can help ensure that studies are conducted in an appropriate and responsible manner.

Perceptions and attitudes of nurse practitioners toward artificial intelligence adoption in health care.

With the ever-increasing integration of artificial intelligence (AI) into health care, it becomes imperative to gain an in-depth understanding of how health care professionals, specifically nurse practitioners, perceive and approach this transformative technology. This study aimed to gain insights into nurse practitioners' perceptions and attitudes toward AI adoption in health care. This qualitative research employed a descriptive and phenomenological approach using in-depth interviews. Data were collected through a semi-structured questionnaire with 37 nurse practitioners selected through purposive sampling, specifically Maximum Variation Sampling and Expert Sampling techniques, to ensure diversity in characteristics. Trustworthiness of the research was maintained through member checking and peer debriefing. Thematic analysis was employed to uncover recurring themes and patterns in the data. The thematic analysis revealed nine main themes that encapsulated nurse practitioners' perceptions and attitudes toward AI adoption in health care. These included nurse practitioners' perceptions of AI implementation, attitudes toward AI adoption, patient-centered care and AI, quality of health care delivery and AI, ethical and regulatory aspects of AI, education and training needs, collaboration and interdisciplinary relationships, obstacles in integrating AI, and AI and health care policy. While this study found that nurse practitioners held a wide range of perspectives, with many viewings AI as a tool to enhance patient care. This research provides a valuable contribution to the evolving discourse surrounding AI adoption in health care. The findings underscore the necessity for comprehensive education and training in AI, accompanied by clear and robust ethical and regulatory guidelines to ensure the responsible integration of AI in health care practice. Furthermore, fostering collaboration and interdisciplinary relationships is pivotal for the successful incorporation of AI in health care. Policymakers should also address the challenges and opportunities that AI presents in the health care sector. This study enhances the ongoing conversation on AI adoption in health care by shedding light on the perspectives of nurses, thereby shaping future strategies for AI integration.

Assessment of the understanding and awareness of bioethics among healthcare professionals in all public and private hospitals of Haripur district, Pakistan: a multicentre cross-sectional study

ObjectiveTo assess the level of bioethics awareness among healthcare professionals in Pakistan, focusing on the associations with sociodemographic characteristics, training and teaching of ethics, medical ethics practice and specific ethical...

Artificial intelligence and nonoperating room anesthesia.

The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future. AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems. The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.

Metode Pendidikan Berdiferensiasi Bagi Generasi Z dalam Perspektif Hadis

An effective approach in educating Generation Z, cannot be ignored that religious and spiritual values have an important role in shaping their character and behavior. In Islam, hadith, as the second source of teaching after Al-Quran, presents ethical, morality, and pedagogy guidelines that have the potential to become the foundation for effective educational methods for Generation Z. This research explores concepts in hadith that are relevant to education, reviews its application in Generation Z education, as well as analyzes its impact on character formation and development of generation Z skills. The research method used is literatur review. The result of this research is that the hadith that is taught is a hadith related to the method of educating the Z generation effectively by means of differentiated education, namely paying attention to the characteristics of students and providing teaching according to the level of ability to receive learning. The study of the verses of the Qur'an and the views of several previous and contemporary scholars can be seen that the hadith is not contrary to the main teachings of Islam even appropriate and can be used as a reference in educating the Z generation. The need for differentiated education so that the learning taught can be conveyed and understood to good practice for generation Z. So that students get a learning experience that brings them a change towards a better direction and makes them a kamil person.

Natural Language Processing (NLP) for Sentiment Analysis in Social Media

Purpose: This study sought to analyze Natural Language Processing (NLP) for sentiment analysis in social media. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to Natural Language Processing (NLP) for sentiment analysis in social media. Preliminary empirical review revealed that advanced computational techniques significantly advanced the understanding of sentiments across diverse social media platforms. Methodologies such as machine learning algorithms and deep learning models like CNNs and RNNs demonstrated robust capabilities in categorizing sentiments accurately and capturing contextual nuances such as sarcasm and slang. The research highlighted the interdisciplinary nature of NLP applications, integrating linguistics with computer science and social sciences to develop effective frameworks for analyzing large-scale social media data. These findings contributed to enhancing decision-making in marketing, politics, and public opinion research, pointing towards future directions in hybrid NLP models for improved sentiment analysis across different languages and cultural contexts. Unique Contribution to Theory, Practice and Policy: The Social Constructionism, Cognitive Linguistics and Discourse Analysis Theory may be used to anchor future studies on Natural Language Processing (NLP). The recommendations aimed to advance theoretical foundations by exploring deep learning models and nuanced sentiment lexicons. Practical applications were enhanced through the development of scalable NLP tools for real-time data processing and integration into social media platforms. Policy implications focused on establishing ethical guidelines for data privacy and bias mitigation in sentiment analysis algorithms. Cross-disciplinary collaboration fostered innovation by integrating insights from computer science, linguistics, psychology, and social sciences. Education initiatives and international collaborations were prioritized to build capacity and standardize methodologies globally, ensuring advancements in both research and practical deployment of sentiment analysis technologies.

CRITICAL ANALYSIS OF CLAIMS IN DRUG PROMOTIONAL LITERATURE AVAILABLE TO MEDICAL PRACTITIONERS

Context: Objective: To ascertain whether the pharmaceutical companies are following the World Health Organisation (WHO) criteria for Ethical Medicinal Drug Promotion 2015 and Organisation of Pharmaceutical Producers of India (OPPI) Code of Pharmaceutical Marketing Practices, 2019 and to what extent. Also, to evaluate the therapeutic claims made by them in their drug promotional literature (DPL) and to classify them. Methods: DPL brochures were collected from different pharmaceutical companies from various medical practitioners and assessed for ethical promotion based on guidelines. Therapeutic claims made by them were classified as authentic, exaggerated, controversial, false, misinterpreted, and ambiguous. Results: A total of 100 DPLs were collected and critically analyzed for authenticity of the content. It was observed that the majority of the DPLs did not comply with the criteria mentioned in the WHO Criteria of Ethical Promotion 2015 and the OPPI Code of Pharmaceutical Practices 2019. International non-proprietary names and the brand name were mentioned by 100% of DPLs. The amount of active ingredients and approved therapeutic indications were mentioned by (99%) and (96%) respectively. The dosage form or regimen was mentioned by 87% of DPLs. Criteria like side effects, precautions/contraindications, and major interactions were mentioned in 28%, 29%, and 14% respectively. Based on the therapeutic claims 23% of the DPLs were found to be authentic and the remaining 77% were extravagant. Conclusions: This study enabled us to find out to what extent the pharmaceutical industries follow the standard criteria for DPL and evaluate the claims made by them. The majority of the DPLs did not follow ethical guidelines and were inadequate in terms of their adequacy, quality, and reliability.

Impact of Generative AI in Academic Integrity and Learning Outcomes: A Case Study in the Upper East Region

With the increasing use of Generative Artificial Intelligence (AI) tools like ChatGPT and Bard, universities face challenges in maintaining academic integrity. This research investigates the impact of these tools on learning outcomes (factual knowledge, comprehension, critical thinking) in selected universities of Ghana's Upper East Region during the 2023-2024 academic year. The study specifically analyzes changes in student comprehension and academic integrity concerns when using Generative AI for content generation, research assistance, and summarizing complex topics. A mixed-methods approach was employed, combining qualitative data from interviews and open-ended questions with quantitative analysis of survey data and academic records. The research focuses on three institutions: C. K. Tedam University of Technology and Applied Sciences, Bolgatanga Technical University, and Regentropfen University College. A purposive sampling technique recruited 150 participants (50 from each university) who had used Generative AI tools. Key findings show that 72% of students reported improved understanding of course material through Generative AI use, yet 75% cited academic integrity as a primary concern. Quantitative analysis revealed a weak to moderate positive correlation (r = 0.45) between AI tool usage and improved grades, with variations depending on the specific AI tasks performed. Qualitative data highlighted concerns about overreliance on AI and its impact on critical thinking skills. This research contributes to the ongoing debate on AI's role in education by providing valuable insights for educators and policymakers worldwide. The findings suggest that while AI tools can enhance comprehension, ethical considerations and potential drawbacks related to critical thinking require careful attention. The study concludes with recommendations for integrating AI literacy programs, developing ethical guidelines, and implementing advanced plagiarism detection systems to harness the benefits of Generative AI while mitigating risks to academic integrity. Although specific to the Upper East Region of Ghana, these insights may be applicable to other educational systems with similar characteristics.

Assessing dual use risks in AI research: necessity, challenges and mitigation strategies

This article argues that due to the difficulty in governing AI, it is essential to develop measures implemented early in the AI research process. The goal of dual use considerations is to create robust strategies that uphold AI’s integrity while protecting societal interests. The challenges of applying dual use frameworks to AI research are examined and dual use and dual use research of concern (DURC) are defined while highlighting the difficulties in balancing the technology’s benefits and risks. AI’s dual use potential is discussed, particularly in areas like NLP and LLMs, and the need for early consideration of dual use risks to ensure ethical and secure development is underscored. In the section on shared responsibilities in AI research and avenues for mitigation strategies the importance of early-stage risk assessments and ethical guidelines to mitigate misuse is emphasized, accentuating self-governance within scientific communities and structured measures like checklists and pre-registration to promote responsible research practices. The final section argues that research ethics committees play a crucial role in evaluating the dual use implications of AI technologies within the research pipeline. The need for tailored ethics review processes is articulated, drawing parallels with medical research ethics committees.

ONTOLOGY OF DIGITAL OBJECTS AND TECHNOLOGICAL NORMATIVITY: NEW PERSPECTIVES FOR DIGITAL ETHICS

Purpose: Development of conceptual approaches to the understanding of ethical aspects of digital technologies, based on the ontological turn in the understanding of technological normativity.Design / Method / Approach: The research is based on an interdisciplinary approach combining philosophy of technology, ethics, social philosophy, science and technology research, computer science and cognitive psychology. Methods of conceptual analysis, phenomenological description, critical discourse analysis and comparative analysis are used.Findings: The ontological status of digital objects and their role in the formation of ethical experience is analyzed. The forms of normativity of digital technologies are revealed: interface, algorithmic and infrastructural.Theoretical implications: The research contributes to the development of conceptual approaches to the ethical aspects of digital technologies, creating a basis for the integration of various philosophical traditions and expanding the possibilities of ethical analysis in the digital era.Practical implications: The proposed approach can be used to develop strategies for ethical design and management of digital technologies, including creating ethical guidelines, conducting ethical audits, designing interfaces, and developing educational programs on digital ethics.Originality / Value: A comprehensive approach to the consideration of the ethics of digital technologies is proposed, taking into account the ontological turn in the understanding of technological normativity. The novelty lies in the integration of the ideas of postphenomenology, object-oriented ontology and philosophy of information for the analysis of the ontology of digital objects and their normative force.Research limitations / Future research: Prospects include empirical studies of various forms of technological normativity, in-depth study of the ontological status of specific types of digital objects, and analysis of the relationship between the ontology of digital objects and their normative force in various contexts. The limitations are related to the theoretical nature of the study, which requires further empirical verification.Paper type: Theoretical.

Beyond Cognition: Are Party-Appointed Arbitrators Truly Impartial?

The present paper examines the principle of impartiality and its significance in the context of party-appointed arbitrators in international investment and commercial arbitration. It reflects on how cognitive biases influence decision-making in arbitral disputes and how such biases can be mitigated to enhance the credibility and fairness of international dispute resolution procedures. Arbitration is a common mechanism for resolving disputes in international investment law, with parties often appointing tribunal members. However, the impartiality of these arbitrators is frequently questioned. Cognitive biases, such as confirmation bias and anchoring bias, can significantly impact the fairness of arbitration by affecting how evidence is perceived and evaluated. This study explores the relationship between cognitive biases and arbitrator impartiality, considering the role that such biases may play in the outcome of an arbitration process. Integrating doctrinal legal analysis with empirical insights from psychology, the paper aims to provide a comprehensive understanding of impartiality challenges faced by party-appointed arbitrators. It analyzes institutional measures like the duty of disclosure and ethical guidelines, alongside the theoretical framework of the ‘devil’s advocate’ approach, to propose effective strategies for mitigating biases. By enhancing understanding of these issues, the paper aims to contribute to the ongoing debate on the fairness of arbitration in international investment and commercial law.

Exploring Artificial Intelligence as a Remedy to the Heavy Teaching Workloads Caused by Massification of Ugandan Public Universities

Universities worldwide, particularly public universities in Uganda are facing a dilemma in which their massification has far outstripped the growth of their academic service delivery capacity, especially their actual teaching staff size. Consequently, most lecturers are struggling with heavy teaching workloads resulting from large class sizes of 100 to 300 or more students created by massification per course unit, especially at the undergraduate level. These workloads have overstretched most lecturers’ ability to teach effectively and limited their career growth by keeping them too busy to conduct research and participate in community service. The dilemma is faced at the time when Industry 4.0 has developed Artificial Intelligence (AI), which can execute different tasks, including teaching tasks in much the same way as human beings perform them. Drawing on the AI job replacement theory complemented by UTAUT and TOE, this study employed a cross-sectional questionnaire survey involving 325 respondents (deans, heads of department [HODs] and lecturers) randomly selected from five randomly selected public universities to analyse awareness of the teaching tasks AI can execute to reduce faculty members’ workload without replacing them, acceptance of AI to perform these tasks, and hindrances to its adoption. Findings from the descriptive analysis indicate that at least 74% of the deans, HODs, and lecturers were highly aware of the teaching tasks AI can perform. Most of these respondents accept AI to perform such teaching tasks that do not involve a human touch as an online search for research and lecture content, lecture dictation, student assessment and evaluation, and grading of marks. They, however, did not accept AI to execute teaching tasks that involve the human touch such as lecture planning, facilitating tutorials and discussions, assessing students’ interpersonal weaknesses that affect learning, and feedback provision. These findings allude to a need to adopt AI to execute only the teaching tasks it is accepted to perform and leave to the lecturers all the tasks they do not accept to perform. Adopting AI this way is bound to relieve the teaching workload allocated to lecturers as massification intensifies. The findings indicate, however, that AI adoption is hindered by different factors, including lack of strategic, ethical, and policy guidelines, and lack of funds and skills required to operate it. These findings point to a need for the management of Uganda’s public universities to adopt AI by lobbying the government for more funding, mobilizing necessary funds internally, training faculty members in using AI, and encouraging all of them to accept it by explaining the role it is capable of playing in reducing workloads and erasing their fear that AI could replace them

An Inquiry Into the Use of Generative AI and Its Implications in Education

The emergence of generative AI technologies has provoked considerable debate among educators regarding their role in education. This study is an investigation of the benefits, disadvantages, and potential strategies for integrating generative AI in educational settings by analyzing societal impacts based on a literature review. We have surveyed the influence of generative AI in education through sources from peer-reviewed journals. The main findings show that generative AI can enhance accessibility and customization in learning for individual learners' needs and pacing. But there are problems with algorithmic biases, discrimination, and data privacy issues, too. This study advocates for mitigating bias, data transparency, and promotion and evaluation of AI policy and research. Generative AI in education hinges on how it is responsibly integrated and observes ethical guidelines, by way of the constant assessment that will guarantee its potential in revolutionizing learning.

Ethics in quantitative sport management research: the impact of AI

PurposeDespite the use of plagiarism-checking software and current ethical guidelines in sport management journals, raising awareness of ethical concerns and potential risks of artificial intelligence (AI) applications is necessary. This paper discusses how AI affects ethical research and publishing and provides guidelines for sport management scholars to ensure quality and integrity of their research.Design/methodology/approachA comprehensive review and critical analysis of literature was performed to evaluate research ethics, potential risks, and guiding principles for the use of AI in research.FindingsEthical research guidelines for quantitative sport management research were proposed. The guidelines encompass seven principles for the proper use of AI and ethical conduct specific to the research methods, data analysis, and results, which would be challenging for AI to accurately replicate.Originality/valueThis study provides an original contribution to the field of sport management because numerous questions concerning ethics and AI have not been addressed until now. The guidelines are suitable for use by sport management scholars, concerning the accuracy, validity, and quality of research while mitigating ethical risks in AI-generated content.

Prevalence of Cognitive Defects Among Senior Inmates of Old-Age Homes in a District of South Kerala.

Background Cognitive decline, including mild cognitive impairment and major neurocognitive disorder, is a growing concern among aging populations. The prevalence of these disorders is especially relevant in regions experiencing a surge in elderly care infrastructure, such as Kerala, India. Within the context of sociocultural shifts and a rising elderly population, old-age homes are increasingly becoming the focal point of care and support for cognitive health. This study aims to examine the prevalence of cognitive defects among elderly individuals residing in old-age homes in the Kottayam district of Kerala, India. Additionally, it investigates the relationship between cognitive disorders and sociodemographic variables of the inhabitants in these institutionalized settings. Methodology In this study, 535 elderly subjects were selected from 85 registered old-age homes in Kottayam, Kerala, through systematic cluster sampling. The response rate was 89.6%. Data collection involved a sociodemographic questionnaire, the Picture Memory Impairment Screen (PMIS), and the Mini-Mental Status Examination (MMSE) translated into Malayalam. Data were gathered either from participants, caretakers, or medical records. Ethical guidelines were strictly followed. Statistical analysis was conducted using SPSS Statistics Version 25.0 (IBM Corp., Armonk, NY, USA), employing the chi-square test, t-test, and logistic regression, with a p-value less than 0.05 deemed significant. Results In our study of 535 elderly individuals in Kottayam's old-age homes, age, gender, and education were significantly associated with cognitive impairment, with p-values <0.001, 0.049, and <0.001, respectively. Behavioral factors such as smoking and alcohol consumption showed no significant association. The mean MMSE and PMIS scores were 25.24 and 5.57, respectively. The prevalence of cognitive defects was 170 (31.80%) as per MMSE and 182 (34.00%) according to PMIS. Given the wider acceptance of MMSE, the study established a cognitive defect prevalence of 170 (31.80%) among senior inmates. Conclusions This study reveals a high prevalence (170, 31.80%) of cognitive defects among elderly residents in Kottayam's old-age homes. Age and education were significant predictors, while behavioral factors such as smoking and alcohol were not. These findings underscore the need for targeted healthcare strategies to address cognitive decline in aging populations.

Ethical deployment of cognitive biases in marketing a framework for responsible influence

This paper examines the intersection of cognitive biases and ethical marketing practices, highlighting how psychological principles can influence consumer behavior within ethical boundaries. Cognitive biases such as scarcity bias, authority bias, the halo effect, and confirmation bias significantly shape consumer perceptions and decisions. However, their application in marketing raises complex ethical concerns, particularly regarding consumer autonomy and the potential for manipulation. This study proposes a conceptual framework that integrates ethical guidelines with marketing strategies that utilize cognitive biases. Through a comprehensive literature review and theoretical analysis, this paper outlines the implications of these biases in marketing, develops a set of ethical guidelines, and discusses the broader impacts on consumer trust and brand integrity. The findings give marketers practical insights for ethically harnessing cognitive biases, ensuring that marketing practices drive business success and maintain consumer respect and loyalty.

The Impact of Artificial Intelligence on Corporate Governance

The advent of artificial intelligence (AI) marks a pivotal shift in the landscape of corporate governance, catalyzing a reevaluation of traditional frameworks and necessitating a forward-looking approach to decision-making, risk management, and ethical considerations. This study explores the multifaceted impact of AI on corporate governance, offering a nuanced analysis of how AI technologies are transforming the operational, strategic, and ethical dimensions of organizations. The research underscores the potential of AI to enhance decision-making processes, optimize operational efficiencies, and foster innovation by providing advanced analytical capabilities and predictive insights. However, it concurrently highlights the emergence of unprecedented challenges, including data privacy concerns, algorithmic bias, and the need for robust regulatory frameworks to mitigate risks associated with AI deployment. The article advocates for a proactive stance in redefining corporate governance models to accommodate the disruptive nature of AI, emphasizing the integration of ethical considerations and transparency in AI applications. It calls for a collaborative effort among corporate leaders, policymakers, and stakeholders to develop governance structures that not only leverage AI’s potential but also safeguard against its inherent risks. The study’s recommendations include the establishment of ethical AI guidelines, the adoption of transparent AI practices, and the continuous monitoring of AI systems to ensure their alignment with corporate governance objectives and societal values. However, it is important to note that the approach and methods used in this study are based on a qualitativeliterature review and, therefore, the generalization of the findings across different sectors and corporate governance frameworks may be limited. Additionally, the rapidly evolving nature of AI technologies poses inherent challenges to keeping up with emerging trends and potential risks.