AI Ethics Research Articles

Harmonized Organizational Principles for Ethics in Artificial Intelligence: A Conceptual Framework

Harmonized Organizational Principles for Ethics in Artificial Intelligence: A Conceptual Framework

Navigating the AI horizon in hospitality: a novel classification and future research agenda

PurposeThis study aims to evaluate Artificial Intelligence (AI) research in the hospitality industry based on the service AI framework (mechanical-thinking-feeling) and highlight prospective avenues for future inquiry in this growing domain.Design/methodology/approachThis paper conceptualizes timely concepts supported by research spanning multiple domains.FindingsThis research introduces a novel classification for the domain of AI hospitality research. This classification encompasses prediction and pattern recognition, computer vision, NLP, behavioral research, and synthetic data generation. Based on this classification, this study identifies and elaborates upon five emerging research topics, each linked to a corresponding set of research questions. These focal points encompass the realms of interpretable AI, controllable AI, AI ethics, collaborative AI, and synthetic data generation.Originality/valueThis viewpoint provides a foundational framework and a directional compass for future research in AI within the hospitality industry. It pushes the industry forward with a balanced approach to leveraging AI to augment human potential and enrich customer experiences. Both the classification and the research agenda would contribute to the body of knowledge that will guide the industry toward a future where technology and human service coalesce to create unparalleled value for all stakeholders.

News bylines and perceived AI authorship: Effects on source and message credibility

With emerging abilities to generate content, artificial intelligence (AI) poses a challenge to identifying authorship of news content. This study focuses on source and message credibility evaluation as AI becomes incorporated into journalistic practices. An experiment (N = 269) explored the effects of news bylines and AI authorship on readers’ perceptions. The findings showed that perceived AI contribution, rather than the labeling of the AI role, predicted readers’ perceptions of the source and the content. When readers thought AI contributed more to a news article, they indicated lower message credibility and source credibility perceptions. Humanness perceptions fully mediated the relationships between perceived AI contribution and perceived message credibility and source credibility. This study yielded theoretical implications for understanding readers’ mental model of machine sourceness and practical implications for newsrooms toward ethical AI in news automation and production.

A starting framework for urban AI applications

There has been a growing concern about the ethical implications of Artificial Intelligence (AI) in urban environments. Scholars have highlighted the dual nature of AI, which can both enhance urban sustainability and pose significant privacy risks. The literature acknowledges that AI introduces specific challenges in urban contexts. However, there remains a gap in comprehensive sector-specific ethical frameworks tailored to the unique challenges posed by AI applications in cities. This study aims to fill this gap by proposing a starting framework for the ethics of Urban AI applications, providing principles to guide responsible AI development and deployment in urban settings. The study employed a two-phase methodology: a content analysis of 100 AI ethics documents to develop a preliminary framework, followed by a survey of experts and stakeholders to validate and expand this framework by identifying urban-specific risks and ethical considerations. The developed ethical framework for Urban AI applications provides a starting point to pave the way for ethical and sustainable urban AI systems that enhance the quality of life for all city residents.

Responsibility Gap(s) Due to the Introduction of AI in Healthcare: An Ubuntu-Inspired Approach

Due to its enormous potential, artificial intelligence (AI) can transform healthcare on a seemingly infinite scale. However, as we continue to explore the immense potential of AI, it is vital to consider the ethical concerns associated with its development and deployment. One specific concern that has been flagged in the literature is the responsibility gap (RG) due to the introduction of AI in healthcare. When the use of an AI algorithm or system results in a negative outcome for a patient(s), to whom can or should responsibility for that outcome be assigned? Although the concept of the RG was introduced in Anglo-American and European philosophy, this paper aims to broaden the debate by providing an Ubuntu-inspired perspective on the RG. Ubuntu, deeply rooted in African philosophy, calls for collective responsibility, and offers a uniquely forward-looking approach to address the alleged RG caused by AI in healthcare. An Ubuntu-inspired perspective can serve as a valuable guide and tool when addressing the alleged RG. Incorporating Ubuntu into the AI ethics discourse can contribute to a more ethical and responsible integration of AI in healthcare.

Decolonizing global AI governance: assessment of the state of decolonized AI governance in Sub-Saharan Africa.

Global artificial intelligence (AI) governance must prioritize equity, embrace a decolonial mindset, and provide the Global South countries the authority to spearhead solution creation. Decolonization is crucial for dismantling Western-centric cognitive frameworks and mitigating biases. Integrating a decolonial approach to AI governance involves recognizing persistent colonial repercussions, leading to biases in AI solutions and disparities in AI access based on gender, race, geography, income and societal factors. This paradigm shift necessitates deliberate efforts to deconstruct imperial structures governing knowledge production, perpetuating global unequal resource access and biases. This research evaluates Sub-Saharan African progress in AI governance decolonization, focusing on indicators like AI governance institutions, national strategies, sovereignty prioritization, data protection regulations, and adherence to local data usage requirements. Results show limited progress, with only Rwanda notably responsive to decolonization among the ten countries evaluated; 80% are 'decolonization-aware', and one is 'decolonization-blind'. The paper provides a detailed analysis of each nation, offering recommendations for fostering decolonization, including stakeholder involvement, addressing inequalities, promoting ethical AI, supporting local innovation, building regional partnerships, capacity building, public awareness, and inclusive governance. This paper contributes to elucidating the challenges and opportunities associated with decolonization in SSA countries, thereby enriching the ongoing discourse on global AI governance.

Psychological and Brain Responses to Artificial Intelligence's Violation of Community Ethics.

Human moral reactions to artificial intelligence (AI) agents' behavior constitute an important aspect of modern-day human-AI relationships. Although previous studies have mainly focused on autonomy ethics, this study investigates how individuals judge AI agents' violations of community ethics (including betrayals and subversions) compared with human violations. Participants' behavioral responses, event-related potentials (ERPs), and individual differences were assessed. Behavioral findings reveal that participants rated AI agents' community-violating actions less morally negative than human transgressions, possibly because AI agents are commonly perceived as having less agency than human adults. The ERP N1 component showed the same pattern with moral rating scores, indicating the modulation effect of human-AI differences on initial moral intuitions. Moreover, the level of social withdrawal correlated with a smaller N1 in the human condition but not in the AI condition. The N2 and P2 components were sensitive to the difference between the loyalty/betrayal and authority/subversion domains but not human/AI differences. Individual levels of moral sense and autistic traits also influenced behavioral data, especially on the loyalty/betrayal domain. In our opinion, these findings offer insights for predicting moral responses to AI agents and guiding ethical AI development aligned with human moral values.

A PERSPECTIVE INTO THE FUTURE OF TEACHING AND LEARNING IN THE CONTEXT OF THE RISING INTEREST IN ARTIFICIAL INTELLIGENCE IN EDUCATION. OPPORTUNITIES AND ETHICAL CHALLENGES

: The paper aims to provide an image of the future of teaching and learning in the context of artificial intelligence transforming various industries, including sports, education, and construction. Its place in education is a frequently discussed topic. While some argue that artificial intelligence will revolutionize education, others worry that it will take over to the harm of educators and students. Though robotics in the classroom is still a ways off, artificial intelligence is finding its way into the classroom. AI has the power to improve teaching and learning methods, solve some of the largest issues facing education today, and hasten the achievement of inclusive and equitable quality education. In addition to delivering artificial intelligence courses, EdTech businesses are increasingly using eLearning solutions to personalize learning experiences, pinpoint knowledge gaps, and give focused feedback. Also, AI-driven education is upending conventional teaching methods and influencing how this field will use technology in the future. With the use of complex algorithms and massive data sets, artificial intelligence solutions for education may provide a lot of advantages, yet as with the use of artificial intelligence in any context, there are significant ethical considerations which are a hot topic of discussion in the technology world and beyond, and the majority of university degree programs are including courses on AI ethics in their curricula. Therefore, the paper presents the benefits of AI in the classroom, such as engagement and assistance for students, assessment and evaluation, and individualized learning but also about the difficulties and worries associated with AI in education, including prejudice and privacy issues, as well as the moral issues raised by AI-powered learning. It also discusses the possible effects on the educational system and how students are trained for the workforce of the future as potential applications of AI in education are explored.

Decision tree C4.5 algorithm for generative AI technology ethics--Based on the results of the questionnaire

With the development of AI technology, generative AI has gradually entered the life of the public, for example, the explosion of CHAT-GPT has allowed more people to see the huge potential and obvious advantages of generative AI. However, in the process of generative AI operation, events that violate social responsibility and ethics often occur, which makes the research on the scientific and technological ethics of generative AI more urgent. In the past literature and research, many industry experts have analysed the impact of generative AI on specific industries, but everyone is or will be a user of generative AI, so we should pay attention to the study of the people's scientific and technological ethical issues of generative AI after putting aside the industry background, so this paper collects primary data by means of questionnaire surveys to find out the public's awareness of generative AI and their perception of generative AI. and attitudes towards generative AI, and using the decision tree C4.5 algorithm with Python as the tool, it is used to respond to people's awareness of generative AI and the public's perception of the relationship between the various factors of the ethical issues of

Promoting Ethical Use of Generative AI in Education

Generative artificial intelligence (AI) represents a crucial subset of AI models characterized by their ability to generate new content based on user input, showing vast potential to transform learning and teaching. However, educators have raised ethical concerns, particularly regarding the adverse effect on students' learning if students simply parrot generative AI-generated content without engaging in critical analysis or original thought. Moreover, there exists the potential of generative AI to perpetuate existing biases in training data. This editorial discusses three major concerns in generative AI use in education and proposes questions (on task-AI fit and people-AI fit) and approaches to address the ethical considerations by adopting five principles of AI ethics. The editorial also discusses developing a classroom AI use policy as one governance mechanism for promoting ethical use of AI. As generative AI technology continues to evolve, so must our educational practices. The editorial ends with a call for readers (educators) to collaboratively define the terms of engagement with generative AI in educational settings and to begin this discourse by sharing insights and experiences with promoting ethical use of generative AI.

Correction: Decolonizing AI Ethics: Relational Autonomy as a Means to Counter AI Harms

Correction: Decolonizing AI Ethics: Relational Autonomy as a Means to Counter AI Harms

Fairness, Bias, and Ethics in AI: Exploring the Factors Affecting Student Performance

The use of artificial intelligence (AI) as a data science tool for education has enormous potential for increasing student performance and course outcomes. However, the growing concern about fairness, bias, and ethics in AI systems requires a careful examination of these issues in an educational context. Using AI and predictive modelling tools, this paper explores the aspects influencing student performance and course success. The Open University Learning Analytics Dataset (OULAD) is analysed using several AI techniques (logistic regression and random forest) in this study to reveal insights about fairness, ethics, and potential biases. This dataset has been used by hundreds of studies to explore how educational data mining can provide information on students. However, potential bias or unfairness in that dataset could undermine the results and any conclusions made from them. To gain insights into the dataset's properties, this was analysed using a typical data science methodology, which included data collecting, cleaning, and exploratory data analysis using Python. By applying AI-based predictive models, this study aims to detect potential biases and their impact on student outcomes. Fairness and ethical considerations are central to the analysis as the representation of various demographic groups and any disparities are evaluated in course results. The goal is to provide useful insights on the proper use of AI in education, while also maintaining equitable and transparent decision-making procedures. The findings shed light on the complicated interplay between artificial intelligence, fairness, and ethics in the context of student performance and course success. As artificial intelligence continues to influence the educational landscape, this study will provide useful ideas for encouraging fairness and minimising biases, resulting in a more inclusive and equal learning environment.

Machine Learning in Medical Triage: A Predictive Model for Emergency Department Disposition

The study explores the application of automated machine learning (AutoML) using the MIMIC-IV-ED database to enhance decision-making in emergency department (ED) triage. We developed a predictive model that utilizes triage data to forecast hospital admissions, aiming to support medical staff by providing an advanced decision-support system. The model, powered by H2O.ai’s AutoML platform, was trained on approximately 280,000 preprocessed records from the Beth Israel Deaconess Medical Center collected between 2011 and 2019. The selected Gradient Boosting Machine (GBM) model demonstrated an AUC ROC of 0.8256, indicating its efficacy in predicting patient dispositions. Key variables such as acuity and waiting hours were identified as significant predictors, emphasizing the model’s capability to integrate critical triage metrics into its predictions. However, challenges related to the complexity and heterogeneity of medical data, privacy concerns, and the need for model interpretability were addressed through the incorporation of Explainable AI (XAI) techniques. These techniques ensure the transparency of the predictive processes, fostering trust and facilitating ethical AI use in clinical settings. Future work will focus on external validation and expanding the model to include a broader array of variables from diverse healthcare environments, enhancing the model’s utility and applicability in global emergency care contexts.

Decisional value scores: A new family of metrics for ethical AI-ML

AbstractResearch in ethical AI has made strides in quantitative expression of ethical values such as fairness, transparency, and privacy. Here we contribute to this effort by proposing a new family of metrics called “decisional value scores” (DVS). DVSs are scores assigned to a system based on whether the decisions it makes meet or fail to meet a particular standard (either individually, in total, or as a ratio or average over decisions made). Advantages of DVS include greater discrimination capacity between types of ethically relevant decisions and facilitation of ethical comparisons between decisions and decision-making systems, including across different modalities (for instance: human, machine, or coupled human–machine systems). After clarifying ambiguities in the concept of “decision” itself, including the question of how to individuate the decisions made by a system, we discuss the role and meaning of “decision” in common AI and machine learning approaches such as decision trees, neural networks, SVMs, and unsupervised classifiers. We then show how DVSs may be defined for several ethical values of interest, with an extended discussion of transparency. Finally, we explore how such metrics can be applied to real decision-making systems through two case studies: evaluations of LLMs for transparency; and evaluations of criminal risk assessment tools for utility, rights violations, fairness, and transparency.

AI for learning unleashed: Pioneering generative AI in education at the University of Miami

The University of Miami (UM) is at the forefront of integrating generative AI into education, spearheaded by Ann M. Olazábal, Interim Dean of the Miami Herbert Business School. This case study explores the dynamic landscape of AI adoption at UM, highlighting both the innovative potential and ethical challenges. The university has embraced AI tools like Adobe Firefly and Copilot, aiming to enhance learning experiences and internal processes. However, the misuse of AI for academic dishonesty poses significant dilemmas. The case contrasts two MBA students' approaches to AI: Alex, who uses AI to shortcut his studies, and Jordan, who leverages AI to deepen her understanding and enhance her learning. The narrative underscores the need for ethical AI use and the importance of fostering critical thinking and creativity. Ann’s strategic vision includes forming an AI task force to explore AI’s organizational impacts and potential use cases, such as a smart assistant for student curriculum planning and an AI-powered entrepreneurship lab. The case concludes with a discussion on balancing innovation with academic integrity and the challenges of centralized IT governance. The case serves as a foundation for classroom discussion including a comprehensive examination of the opportunities and risks associated with generative AI in higher education.

Exploring ethical considerations in AI-driven cataloguing and classification with ChatGPT

Purpose. This study investigates the ethical dimensions surrounding the use of AI-driven cataloguing and classification systems, exemplified by ChatGPT, in library sciences. The research aims to fill a gap in the existing literature by exploring privacy, accountability, transparency, and bias issues associated with these technologies. Design/Methodology/Approach. The study employs a comprehensive literature review to analyze the current landscape of AI applications in libraries, emphasizing ethical considerations. It also assesses existing guidelines from associations like ALA and IFLA, and explores privacy concerns, biases, and the impact on job satisfaction. Findings. The study identifies gaps in current ethical frameworks and emphasizes the need for guidelines to evolve with technological advancements. Privacy concerns, biases in AI outputs, and challenges related to user trust and transparency are highlighted. The impact of AI on job satisfaction for librarians is discussed, acknowledging both opportunities and challenges. Originality/Value. This study contributes to the broader discussion on ethical AI by specifically addressing the underexplored area of AI-driven cataloguing and classification systems. It underscores the importance of aligning AI use with ethical standards, proposing strategies for mitigating biases and fostering user trust.

ChatGPT in Higher Education: Practical Ideas for Addressing Artificial Intelligence in Nursing Education.

As the use of artificial intelligence (AI) becomes more prevalent in academic settings, there is a growing concern about maintaining a culture of integrity. This article explores the role of academic institutions and programs in fostering a culture of integrity in relation to AI. By implementing specific policies, integrating tools, and utilizing software for AI detection, academic institutions can establish a culture of integrity in relation to AI. These collective efforts foster an environment where ethical AI practices are upheld and reinforce the importance of academic honesty, particularly in the nursing profession. Academic institutions have the capacity to establish integrity-focused policies and integrate anti-AI agent tools in courses to mitigate unethical AI usage, while software advancements assist faculty in identifying AI presence during assessments. Emphasizing the interplay between academic and professional integrity strengthens nurses' dedication to academic honesty. [J Nurs Educ. 2024;63(X):XXX-XXX.].

Command responsibility in military AI contexts: balancing theory and practicality

AbstractArtificial intelligence (AI) has found extensive applications to varying degrees across diverse domains, including the possibility of using it within military contexts for making decisions that can have moral consequences. A recurring challenge in this area concerns the allocation of moral responsibility in the case of negative AI-induced outcomes. Some scholars posit the existence of an insurmountable “responsibility gap”, wherein neither the AI system nor the human agents involved can or should be held responsible. Conversely, other scholars dispute the presence of such gaps or propose potential solutions. One solution that frequently emerges in the literature on AI ethics is the concept of command responsibility, wherein human agents may be held responsible because they perform a supervisory role over the (subordinate) AI. In the article we examine the compatibility of command responsibility in light of recent empirical studies and psychological evidence, aiming to anchor discussions in empirical realities rather than relying exclusively on normative arguments. Our argument can be succinctly summarized as follows: (1) while the theoretical foundation of command responsibility appears robust (2) its practical implementation raises significant concerns, (3) yet these concerns alone should not entirely preclude its application (4) they underscore the importance of considering and integrating empirical evidence into ethical discussions.

A Lifecycle Approach for Artificial Intelligence Ethics in Energy Systems

Despite the increasing prevalence of artificial intelligence (AI) ethics frameworks, the practical application of these frameworks in industrial settings remains limited. This limitation is further augmented in energy systems by the complexity of systems composition and systems operation for energy generation, distribution, and supply. The primary reason for this limitation is the gap between the conceptual notion of ethics principles and the technical performance of AI applications in energy systems. For instance, trust is featured prominently in ethics frameworks but pertains to limited relevance for the robust operation of a smart grid. In this paper, we propose a lifecycle approach for AI ethics that aims to address this gap. The proposed approach consists of four phases: design, development, operation, and evaluation. All four phases are supported by a central AI ethics repository that gathers and integrates the primary and secondary dimensions of ethical practice, including reliability, safety, and trustworthiness, from design through to evaluation. This lifecycle approach is closely aligned with the operational lifecycle of energy systems, from design and production through to use, maintenance, repair, and overhaul, followed by shutdown, recycling, and replacement. Across these lifecycle stages, an energy system engages with numerous human stakeholders, directly with designers, engineers, users, trainers, operators, and maintenance technicians, as well as indirectly with managers, owners, policymakers, and community groups. This lifecycle approach is empirically evaluated in the complex energy system of a multi-campus tertiary education institution where the alignment between ethics and technical performance, as well as the human-centric application of AI, are demonstrated.

Information Ethics in Light of Bibliometric Analyses: Discovering a Shift to Ethics of Artificial Intelligence

Information Ethics in Light of Bibliometric Analyses: Discovering a Shift to Ethics of Artificial Intelligence