AI and quantum computing ethics- same but different? Towards a new sub-field of computing ethics

Artificial intelligence (AI) ethics is undergoing a practical shift towards putting principles into design practices in developing responsible AI. While this practical turn is essential, this paper highlights its potential risk of overly focusing on addressing issues at the level of individual artifacts, which can neglect more profound structural challenges and the need for significant systemic change. Such oversight makes AI ethics lose its strength in addressing some hidden, long-term harms within broader contexts. In this paper, we propose that the reflection on structural issues should be an integral part of AI ethics. To achieve this, we develop a multi-level framework to analyze socio-ethical issues of AI at both the artifact and broader structural levels. This framework can serve as a potentially transformative approach to uncover some unspoken assumptions in current AI ethics discourses and expose some blind spots in AI guidelines, policies, and regulations. Our paper paves the way to develop a practical approach that can effectively integrate this multi-level framework into real-world AI design and policymaking, ultimately bringing about transformative change.

Artificial Intelligence (AI) is rapidly changing every aspect of our society—including amplifying our biases. Fairness, trust and ethics are at the core of many of the issues underlying the implications of AI. Despite this, research on AI with relation to fairness, trust and ethics in the information systems (IS) field is still scarce. This panel brought together academia, business and government perspectives to discuss the challenges and identify potential solutions to address such challenges. This panel report presents eight themes based around the discussion of two questions: (1) What are the biggest challenges to designing, implementing and deploying fair, ethical and trustworthy AI?; and (2) What are the biggest challenges to policy and governance for fair, ethical and trustworthy AI? The eight themes are: (1) identifying AI biases; (2) drawing attention to AI biases; (3) addressing AI biases; (4) designing transparent and explainable AI; (5) AI fairness, trust, ethics: old wine in a new bottle?; (6) AI accountability; (7) AI laws, policies, regulations and standards; and (8) frameworks for fair, ethical and trustworthy AI. Based on the results of the panel discussion, we present research questions for each theme to guide future research in the area of human–computer interaction.

This paper argues that the field of artificial intelligence (AI) ethics needs to give more attention to the values and interests of nonhumans such as other biological species and the AI itself. It documents the extent of current attention to nonhumans in AI ethics as found in academic research, statements of ethics principles, and select projects to design, build, apply, and govern AI. It finds that the field of AI ethics gives limited and inconsistent attention to nonhumans, with the main activity being a line of research on the moral status of AI. The paper argues that nonhumans merit moral consideration, meaning that they should be actively valued for their own sake and not ignored or valued just for how they might benefit humans. Finally, it explains implications of moral consideration of nonhumans for AI ethics research and practice, including for the content of AI ethics principles, the selection of AI projects, the accounting of inadvertent effects of AI systems such as via their resource and energy consumption and potentially certain algorithmic biases, and the research challenge of incorporating nonhuman interests and values into AI system design. The paper does not take positions on which nonhumans to morally consider or how to balance the interests and values of humans vs. nonhumans. Instead, the paper makes the more basic argument that the field of AI ethics should move from its current state of affairs, in which nonhumans are usually ignored, to a state in which nonhumans are given more consistent and extensive moral consideration.

The rapid advancement of artificial intelligence (AI) has revolutionized the accounting profession, automating tasks, identifying patterns, and improving accuracy. However, the increasing reliance on AI raises ethical concerns regarding privacy, bias, transparency, and accountability. This research paper delves into the ethical considerations of AI implementation in accounting practices.Thepaper begins by examining the potential benefits of AI in accounting, highlighting its ability to streamline operations, enhance efficiency, and reduce errors. However, it also acknowledges the ethical risks associated with AI, including data privacy breaches, biased decision-making, lack of transparency, and accountability issues.The paper proposes a framework for responsible AI implementation in accounting to address these ethical concerns. The framework emphasizes establishing clear ethical guidelines,ensuring data privacy and security, mitigating AI algorithms' bias, promoting AI decisionmaking transparency, and establishing accountability mechanisms.The paper further explores the role of accountants in addressing AI ethics. Accountants are responsible for upholding ethical standards and ensuring that AI systems are used responsibly and ethically. They must be aware of the ethical implications of AI and have the knowledge and skills to mitigate ethical risks.In conclusion, the paper emphasizes the need for a proactive approach to AI ethics in accounting. By establishing clear ethical guidelines, promoting responsible AI implementation, and empowering accountants with ethical knowledge and skills, the accounting profession can harness the potential of AI while upholding ethical principles and safeguarding public trust.

This study is motivated by the growing discourse on artificial intelligence (AI) ethics that has not yet been fully operationalized in practice, particularly in the health and education sectors in the era of generative AI. It aims to synthesize the literature on the operationalization of AI ethics in order to propose a new governance model that is more adaptive and context-sensitive. A structured thematic synthesis was conducted on peer-reviewed studies published between 2020 and 2025. The analysis reveals a central paradox: a strong global consensus on the principles of justice, transparency, accountability, and privacy stands in stark contrast to significant gaps in their implementation. The emerging challenges are highly context-dependent: in the health sector, they center on technical safety and relational accountability, whereas in the education sector they are more closely related to epistemic integrity and equitable access. The advent of generative AI exacerbates these issues by introducing new systemic risks, such as large-scale hallucinations and an increasingly profound crisis of clarity. In response to the limitations of static compliance-based approaches, this study proposes the Adaptive and Layered AI Governance Model (ALAIGoM), a model of adaptive and layered AI governance that integrates operationalized ethical principles, multi-level governance structures, and a continuous ethics life cycle to enable context-sensitive, adaptive oversight. The model is grounded in proactive trust-building, human-centered design, and mechanisms that ensure acceptability. Overall, this study offers a coherent roadmap for reconfiguring AI ethics from a mere normative checklist into an embedded, adaptive, and responsive governance process.

Exploring the trade-off between computational power and energy efficiency: An analysis of the evolution of quantum computing and its relation to classical computing

As artificial intelligence (AI) technologies advance and become increasingly integrated into medicine and healthcare services, there is a growing consensus that it is necessary to prepare medical students to understand and utilize AI in medical education. Research and discussions are ongoing regarding the AI competencies that healthcare professionals should have. There are diverse opinions on how to integrate the necessary AI competencies for medical graduates into existing curricula. However, wide agreement exists regarding the importance of providing sufficient and appropriate education on the ethical aspects of using AI in clinical practice and research. In this paper, the authors aim to introduce practical educational principles, strategies, and methods for educators interested in teaching AI ethics in medicine. To achieve this, the paper (1) introduces the AI competencies and medical ethics competencies that medical school graduates should possess; (2) explains the necessity of fostering AI ethics competencies in medical education; (3) discusses the principles of developing AI ethics education and considerations for implementing such curricula; and (4) presents educational modules that can be utilized to cultivate AI ethics competencies in medical students and young physicians. The authors of the paper hope that the case-based module on medical AI ethics we have developed may contribute to prepare medical graduates who are familiar with the principles and guidelines of medical AI ethics, enabling them to make the best ethical choices in any given environment.

In response to the COVID-19 crisis, Artificial Intelligence (AI) has been applied to a range of applications in healthcare and public health such as case identification or monitoring of the population. The urgency of the situation should not be to the detriment of considering the ethical implications of such apps. Implementing ethics in medical AI is a complex issue calling for a systems thinking approach engaging diverse representatives of the stakeholders in a consultative process. The participatory engagement aims to gather the different perspectives of the stakeholders about the app in a transparent and inclusive way. In this study, we engaged a group of clinicians, patients, and AI developers in conversations about a fictitious app which was an aggregate of actual COVID-19 apps. The app featured a COVID-19 symptoms monitoring function for both the patient and the clinician, as well as infection clusters tracking for health agencies. Anchored in Soft Systems Methodology and Critical Systems Thinking, participants were asked to map the flow of knowledge between the clinician, the patient, and the AI app system and answer questions about the ethical boundaries of the system. Because data and information are the resource and the product of the AI app, understanding the nature of the information and knowledge exchanged between the different agents of the system can reveal ethical issues. In this study, not only the output of the participatory process was analysed, but the process of the stakeholders’ engagement itself was studied as well. To establish a strong foundation for the implementation of ethics in the AI app, the conversations among stakeholders need to be inclusive, respectful and allow for free and candid dialogues ensuring that the process is transparent for which a systemic intervention is well suited.

Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation

Background: Artificial intelligence (AI) has experienced remarkable progress, becoming an indispensable tool for solving a wide range of technological and economic challenges. Problem: This article explores the current state of AI, with a special focus on machine learning techniques, including artificial neural networks. It discusses the current state of these areas, the challenges they face, and the research opportunities they present; in addition, it highlights concerns related to the social impacts and ethical issues of AI. Objectives: To present problems and issues related to AI, assessing the impact on society and presenting possible ways to mitigate the risks of AI. Methods: A critical-narrative review was carried out based on scientific texts and documents. Results: AI is a promising technology with the potential to generate significant benefits for society. However, AI also presents risks and challenges that need to be considered. Studies on the social impacts and ethical issues of AI are important to ensure that this technology is developed and used responsibly. Conclusions: AI is a technology with the potential for positive impact, but it also presents risks that need to be mitigated. It is important that studies on the social impacts and ethical issues of AI are carried out continuously and that they address the following aspects: (1) technical: a) prejudice and discrimination; b) lack of transparency; c) lack of control; (2) social: d) loss of jobs; e) social inequality; f) environmental impacts; (3) ethical: g) autonomy; h) responsibility; i) privacy.

In the world of computation, the invention of Computers has reduced human effort for accomplishing task. Depending on the type, size and nature of the work the computers are manufactured in different magnitudes. As the technology grows, it facilitated to manufacture high performing, high throughput, and high memory Classical computers. Classical computers store any information in the form of bits in the memory device, which can be either 1 or 0 (binary system) and all the processing will be done in the same logic i.e., high voltage/low voltage. Because of this logic the classical computers solve some problems by using more resources. Quantum computer are the next level computers which uses quantum theory principles for solving problems. The quantum computer uses Qubit as basic unit which uses principles of quantum computing such as superposition and entanglement, which gives qubit, the efficiency of exhibiting many logical states at a time. This paper shows a comparative study on classical and quantum computers and illustrates various problems in classical computers and respective solutions with use of quantum computers.

This paper will review the basic building blocks of quantum computing and discuss the main applications in artificial intelligence that can be addressed more efficiently using the quantum computers of today. Artificial intelligence and quantum computing have many features in common. Quantum computing can provide artificial intelligence and machine learning algorithms with speed of training and computational power in less price. Artificial intelligence on the other hand can provide quantum computers with the necessary error correction algorithms. Some of the algorithms in AI that have been successfully implemented on a quantum computer, that we will present in this paper, are both unsupervised learning algorithms (clustering and Principal component analysis) and also supervised learning classification, such as support vector machines.

The increasing deployment of artificial intelligence (AI) in e-commerce has enabled the emergence of delegated commerce, wherein consumers authorize AI agents to autonomously execute purchase and payment decisions. While such systems promise convenience, efficiency, and personalization, their widespread adoption remains constrained by unresolved issues of consumer trust, particularly in financially consequential contexts. Prior research has largely examined general AI acceptance or human-AI interaction, offering limited insight into the specific conditions under which consumers are willing to delegate economic decision authority to AI agents. This paper proposes a multidimensional conceptual framework for understanding consumer trust in delegated commerce. Drawing on an interdisciplinary synthesis of trust theory, consumer behavior, AI ethics, and regulatory scholarship, the framework articulates trust as a conditional and context-dependent construct shaped by perceived risk, transparency, control mechanisms, system infrastructure, personalization, and liability arrangements. To ground and refine the framework, the study incorporates illustrative insights from expert interviews spanning AI development, e-commerce, psychology, ethics, and user experience. Rather than empirically validating trust determinants, the study aims to clarify core constructs, delineate boundary conditions across transaction risk and delegation levels, and surface key governance challenges specific to AI-mediated purchase and payment decisions. The proposed framework offers a structured foundation for future empirical research and provides practical guidance for designers and policymakers seeking to support responsible, trustworthy deployment of autonomous commerce systems.

The study assessed undergraduates’ knowledge of the use and ethics of Artificial Intelligence (AI) in Ogun State public universities. The researchers employed a descriptive survey research design. The study population comprised all final-year students at universities in Ogun State, specifically Tai Solarin University of Education (TASUED) and Olabisi Onabanjo University (OOU), with an estimated total of approximately 7,300 final-year students (TASUED: 2,770; OOU: 4,530). A multistage sampling approach was adopted: the universities were purposively selected, after which simple random sampling was used to select 10% of the final-year undergraduates from each institution. This led to the selection of 730 students (277 undergraduates from TASUED and 453 undergraduates from OOU, respectively). Data were collected using a questionnaire on AI usage and ethics, and the data collected were analyzed using mean ratings and t-tests. Findings indicated that undergraduates had high knowledge of AI usage but low knowledge of AI ethics, with no significant gender differences in either area. The study concludes that while undergraduates demonstrate strong awareness of AI applications, their limited understanding of AI ethics, regardless of gender, highlights a critical need for targeted ethical education in AI. It was recommended that universities implement compulsory AI ethics courses across all disciplines, not just in computer science or engineering.

Technological improvements are crucial for achieving decarbonisation targets and addressing the impacts of climate change in the built environment via mitigation and adaptation measures. Data-driven methods for building performance prediction are particularly important in this regard. Nevertheless, the deployment of these technologies faces challenges, particularly in the domains of artificial intelligence (AI) ethics, interpretability and explainability of machine learning (ML) algorithms. The challenges encountered in applications for the built environment are amplified, particularly when data-driven solutions need to be applied throughout all the stages of the building life cycle and to address problems from a socio-technical perspective, where human behaviour needs to be considered. This requires a consistent use of analytics to assess the performance of a building, ideally by employing a digital twin (DT) approach, which involves the creation of a digital counterpart of the building for continuous analysis and improvement. This paper presents an in-depth review of the critical connections between data-driven methods, AI ethics, interpretability and their implementation in the built environment, acknowledging the complex and interconnected nature of these topics. The review is organised into three distinct analytical levels: The first level explores key issues of the current research on the interpretability of machine learning methods. The second level considers the adoption of interpretable data-driven methods for building energy modelling and the problem of establishing a link with the third level, which examines physics-driven grey-box modelling techniques, in order to provide integrated modelling solutions. The review’s findings highlight how the interpretability concept is relevant in multiple contexts pertaining to energy and the built environment and how some of the current knowledge gaps can be addressed by further research in the broad area of data-driven methods.