AI failure loops in devalued work: The confluence of overconfidence in AI and underconfidence in worker expertise

BackgroundDespite the recognition that developing artificial intelligence (AI) that is trustworthy is necessary for public acceptability and the successful implementation of AI in healthcare contexts, perspectives from key stakeholders are often absent from discourse on the ethical design, development, and deployment of AI. This study explores the perspectives of birth parents and mothers on the introduction of AI-based cardiotocography (CTG) in the context of intrapartum care, focusing on issues pertaining to trust and trustworthiness.MethodsSeventeen semi-structured interviews were conducted with birth parents and mothers based on a speculative case study. Interviewees were based in England and were pregnant and/or had given birth in the last two years. Thematic analysis was used to analyze transcribed interviews with the use of NVivo. Major recurring themes acted as the basis for identifying the values most important to this population group for evaluating the trustworthiness of AI.ResultsThree themes pertaining to the perceived trustworthiness of AI emerged from interviews: (1) trustworthy AI-developing institutions, (2) trustworthy data from which AI is built, and (3) trustworthy decisions made with the assistance of AI. We found that birth parents and mothers trusted public institutions over private companies to develop AI, that they evaluated the trustworthiness of data by how representative it is of all population groups, and that they perceived trustworthy decisions as being mediated by humans even when supported by AI.ConclusionsThe ethical values that underscore birth parents and mothers’ perceptions of trustworthy AI include fairness and reliability, as well as practices like patient-centered care, the promotion of publicly funded healthcare, holistic care, and personalized medicine. Ultimately, these are also the ethical values that people want to protect in the healthcare system. Therefore, trustworthy AI is best understood not as a list of design features but in relation to how it undermines or promotes the ethical values that matter most to its end users. An ethical commitment to these values when creating AI in healthcare contexts opens up new challenges and possibilities for the design and deployment of AI.

Artificial intelligence dimensions and design features for the knowledge-based work of the future: a socio-technical perspective

The ethical development and deployment of artificial intelligence (AI) is a rapidly evolving field with significant implications for society. This paper delves into the multifaceted ethical considerations surrounding AI, emphasising the importance of transparency, accountability, and privacy. By conducting a comprehensive review of existing literature and case studies, it highlights key ethical issues such as bias in AI algorithms, privacy concerns, and the societal impact of AI technologies. The study underscores the necessity for robust governance frameworks and international collaboration to address these ethical challenges effectively. It explores the need for ongoing ethical evaluation as AI technologies advance, particularly in autonomous systems. The paper emphasises the importance of integrating ethical principles into AI design from the outset, fostering sustainable practices, and raising awareness through education. Furthermore, the paper examines current regulatory frameworks across various regions, comparing their effectiveness in promoting ethical AI practices. The findings suggest a global consensus on key ethical principles, though their implementation varies widely. By proposing strategies to ensure responsible AI innovation and mitigate risks, this research contributes to the ongoing discourse on the future of AI ethics, aiming to guide the development of AI technologies that uphold human dignity and contribute to the common good. Research the ethical considerations and societal impacts of AI, focusing on issues like bias in AI algorithms, privacy concerns, or the effect on employment. This can involve a comprehensive review of existing literature and case studies.

The more artificial intelligence (AI) becomes part of industries and societies, the more it has become necessary to think about ethics for its design and deployment. The purpose of this white paper is to explore the key ethical challenges of AI systems and data-centric ontology models with which decisions are made, as well as opportunities for realigning the development of AI with human values. Algorithms involved in making decisions in different settings are discussed, as well as ethical concerns such as algorithmic bias, transparency, privacy and accountability, as well as how improper use of AI can harm fairness, human autonomy and trust in automated systems. The paper then explores important challenges including AI paradoxes in consumer markets where AI systems increase as well as exploit consumers and the ethically problematic questions arising from use of AI in business decisions most notably in the financial sector. We will also analyze how these ethical concerns play out in real world use cases in the practical industry starting from AI applications related to customer service, healthcare etc. and financial services. The paper also shares actionable best practices for the ethical development and deployment of AI. There’s implementing fairness-aware algorithms, increasing the degree of transparency in the decision-making process, and having regulators help guide the responsible adoption of AI. The aim of this exploration is to provide actionable insights to practitioners, researchers and policymakers on developing an ethical roadmap for AI with a goal of maximizing its benefits and mitigating its risks

Purpose One of the major barriers to implementing artificial intelligence (AI) in fashion design is possibly higher consumer reluctance to accept AI-designed (vs. human-designed) products. How can brands alleviate the negative responses to AI-designed products? To answer this question, this research tests the role of product innovativeness in determining the levels of consumer resistance to AI designs. Design/methodology/approach The hypotheses were developed based on the literature on algorithm aversion and appreciation, fashion design evaluation, and mind perception theory. To test the hypotheses, we conducted three online experiments using Amazon Mturk through CloudResearch platform. Findings While a general preference for human designs over AI designs was found, the negative attitudes toward AI designs were stronger for low-innovative products but weaker for high-innovative products. This is because, according to the conditional process analysis, participants perceived AI-designed products as less original compared to human-designed products when innovativeness level was low. However, this pattern was not shown when innovativeness level was high. Practical implications The findings show the potential for overcoming aversion to AI-designed fashion products. Brands utilizing AI in design are recommended to aim for highly innovative designs, characterized by deconstruction fashion and avant-garde approaches and emphasize innovativeness values when promoting AI-designed products. Originality/value This research sheds light on how and why consumers' negative responses to AI designs vary depending on the final product design, contributing to the discourse on fashion creativity in the era of generative AI from consumers' perspectives.

PurposeThis research study aims to inquire into the technostress phenomenon at an organizational level from machine learning (ML) and artificial intelligence (AI) deployment. The authors investigated the role of ML and AI automation-augmentation paradox and the socio-technical systems as coping mechanisms for technostress management amongst managers.Design/methodology/approachThe authors applied an exploratory qualitative method and conducted in-depth interviews based on a semi-structured interview questionnaire. Data were collected from 26 subject matter experts. The data transcripts were analyzed using thematic content analysis.FindingsThe study results indicated that role ambiguity, job insecurity and the technology environment contributed to technostress because of ML and AI technologies deployment. Complexity, uncertainty, reliability and usefulness were primary technology environment-related stress. The novel integration of ML and AI automation-augmentation interdependence, along with socio-technical systems, could be effectively used for technostress management at the organizational level.Research limitations/implicationsThis research study contributed to theoretical discourse regarding the technostress in organizations because of increased ML and AI technologies deployment. This study identified the main techno stressors and contributed critical and novel insights regarding the theorization of coping mechanisms for technostress management in organizations from ML and AI deployment.Practical implicationsThe phenomenon of technostress because of ML and AI technologies could have restricting effects on organizational performance. Executives could follow the simultaneous deployment of ML and AI technologies-based automation-augmentation strategy along with socio-technical measures to cope with technostress. Managers could support the technical up-skilling of employees, the realization of ML and AI value, the implementation of technology-driven change management and strategic planning of ML and AI technologies deployment.Originality/valueThis research study was among the first few studies providing critical insights regarding the technostress at the organizational level because of ML and AI deployment. This research study integrated the novel theoretical paradigm of ML and AI automation-augmentation paradox and the socio-technical systems as coping mechanisms for technostress management.

The global focus on artificial intelligence (AI) in healthcare and medicine is on the rise. Despite remarkable progress in integrating AI into clinical workflows, gaps in regulation remain a prevalent issue within healthcare systems. Effective regulation of artificial intelligence in clinical practice is essential for managing medico-legal risk and ensuring patient safety. Numerous studies highlight the significant potential for medico-legal risk and the need for clear guidelines on the ethical and safe use of AI in clinical practice. Although there are various concerns that these guidelines must address, our work focused on researching best practices regarding patient-centered factors like patient autonomy, trust and transparency, privacy and security, equity and fairness, and ensuring human oversight. While challenges in AI workflow integration arise from many factors, including human interactions and system inadequacies, the focus on individuals rather than the system has fostered an unsuitable culture for enhancing patient-centered care. Key focus areas include risk stratification strategies and increasing transparency within this inherently complex system, as they play a crucial role in guiding clinical decisions in patient management. Proper integration of AI regulatory frameworks into clinical practice is essential for addressing gaps in the design, development, deployment, and long-term monitoring of AI solutions. Globally, the regulation of AI in clinical practice is continually evolving as governments and legal systems adapt to the rapid advances in AI as a medical device (AIaMD). In Canada, a strategic path forward prioritizes federal and provincial regulations; however, at this stage, they remain fragmented. We advocate for the establishment of uniform guidelines that address the risks, benefits, opportunities, and best practices as AI technologies are integrated into the clinical workflow. Achieving a national standard with clear guidance on the ethical and safe use of AI in clinical practice is recommended to move forward.

Artificial intelligence (AI) design traditionally prioritizes full automation and broadest data coverage yet often overlooks the contextual realities of pre-existing biases in datasets and human agency in real-world applications. This article explores how cross-disciplinary collaboration can transform this paradigm by opening AI's ‘black box' of full automation to public accountability, human operation and real-world use. Based on anthropologists’ direct collaboration with computer scientists in a machine learning (ML) AI design project, this project proposes cross-disciplinary methodological innovations for algorithm design and takes up the fairness issue as an exemplary domain to experiment with our new ML model framework. We developed a new ML framework that moves beyond conventional accuracy-coverage trade-offs to incorporate a human-operable three-way balance between accuracy, fairness, and coverage. In this framework, we foreground the real-world contexts and human agency at multiple stages of AI design, from data training, decision-making, interface design to model testing. The results demonstrate both the promise and challenges of bridging academic disciplines and connecting lab-based AI development with real-world needs. While our collaboration has made AI design more accountable, it also highlights enduring tensions in balancing competing priorities like fairness, accuracy, and coverage.

SummaryThis paper presents the “CDAC AI life cycle,” a comprehensive life cycle for the design, development, and deployment of artificial intelligence (AI) systems and solutions. It addresses the void of a practical and inclusive approach that spans beyond the technical constructs to also focus on the challenges of risk analysis of AI adoption, transferability of prebuilt models, increasing importance of ethics and governance, and the composition, skills, and knowledge of an AI team required for successful completion. The life cycle is presented as the progression of an AI solution through its distinct phases—design, develop, and deploy—and 19 constituent stages from conception to production as applicable to any AI initiative. This life cycle addresses several critical gaps in the literature where related work on approaches and methodologies are adapted and not designed specifically for AI. A technical and organizational taxonomy that synthesizes the functional value of AI is a further contribution of this article.

Rapid integration of Artificial Intelligence (AI) into the military domain necessitates actionable strategies for translating high-level principles of responsible use into practical guidelines. However, there remains a problematic gap between these principles and the norms that govern the use of AI in military operations. Moreover, these norms are highly dependent on the particular context in which military AI is deployed. This leads to normative uncertainty; what is responsible use of AI in a specific military operation? Unclear practical guidelines pose challenges for technology developers and military operators involved in the deployment of military AI. This paper emphasises the need for a context-specific assessment of responsible use of military AI. Moving beyond a one-size-fits-all standard, we propose the Military AI Responsibility Contextualisation (MARC) framework; a structured approach that facilitates a context-specific assessment. In that way, this paper aims to contribute to bridging the gap between abstract principles and practical guidelines. We furthermore emphasise the need for interdisciplinary collaboration in further operationalizing responsible military AI to work towards the ethical and effective development and deployment of AI in military operations.

As artificial intelligence (AI) deployment is growing exponentially, questions have been raised whether the developed AI ethics discourse is apt to address the currently pressing questions in the field. Building on critical theory, this article aims to expand the scope of AI ethics by arguing that in addition to ethical principles and design, the organizational dimension (i.e. the background assumptions and values influencing design processes) plays a pivotal role in the operationalization of ethics in AI development and deployment contexts. Through the prism of critical theory, and the notions of underdetermination and technical code as developed by Feenberg in particular, the organizational dimension is related to two general challenges in operationalizing ethical principles in AI: (a) the challenge of ethical principles placing conflicting demands on an AI design that cannot be satisfied simultaneously, for which the term ‘inter-principle tension’ is coined, and (b) the challenge of translating an ethical principle to a technological form, constraint or demand, for which the term ‘intra-principle tension’ is coined. Rather than discussing principles, methods or metrics, the notion of technical code precipitates a discussion on the subsequent questions of value decisions, governance and procedural checks and balances. It is held that including and interrogating the organizational context in AI ethics approaches allows for a more in depth understanding of the current challenges concerning the formalization and implementation of ethical principles as well as of the ways in which these challenges could be met.

Artificial Intelligence (AI) is increasingly central in the industry 5.0 revolution, impacting a range of organizational ecosystems. This research explores the dynamics of participation in organizational transformation and aimed to distil critical assurance principles for ideal AI design and deployment. The assurance principles, rooted in participatory insights, provide a valuable lens for transformation teams, fostering a comprehensive perspective on AI implementation. This research underscores the essential role of participatory methodologies in shaping ethical, effective, and inclusive AI strategies, acknowledging the diverse voices within the organizational context. Furthermore, by involving end-users in setting the assurance criteria, extending formalized standards, the study enhances the relevance and applicability of these principles. Drawing on previous research, the application of these assurance principles across nine projects demonstrates their practical relevance and effectiveness in guiding AI deployment. As AI evolves, guided by a participatory ethos and formalized standards co-created with end-users, organizations are equipped to navigate the intricate landscape of technological integration with heightened responsibility and foresight.

Smile design software increasingly relies on artificial intelligence (AI). However, using AI for smile design raises numerous technical and ethical concerns. This study aimed to evaluate these ethical issues. An international consortium of experts specialized in AI, dentistry, and smile design was engaged to emulate and assess the ethical challenges raised by the use of AI for smile design. An e-Delphi protocol was used to seek the agreement of the ITU-WHO group on well-established ethical principles regarding the use of AI (wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency). Each principle included examples of ethical challenges that users might encounter when using AI for smile design. On the first round of the e-Delphi exercise, participants agreed that seven items should be considered in smile design (diversity, transparency, wellness, privacy protection, prudence, law and governance, and sustainable development), but the remaining four items (equity, accountability and responsibility, solidarity, and respect of autonomy) were rejected and had to be reformulated. After a second round, participants agreed to all items that should be considered while using AI for smile design. AI development and deployment for smile design should abide by the ethical principles of wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency.

Purpose This study has proposed a socially responsible approach towards artificial intelligence (AI) deployment and tested a revised AI device use acceptance (RAIDUA)-based conceptual model involving the potential indirect moderating role of consumers’ privacy concerns (PRCO) regarding AI-enabled devices. Design/methodology/approach The newly introduced RAIDUA model was verified with empirical data gathered from 280 respondents. A partial least square-driven methodology was used in the present study. Further, a simple slope analysis was also performed to assess the PRCO for their plausible moderating role. Findings Results confirmed a negative moderation impact of consumers’ PRCO in the interaction between perceived usefulness expectancy and consumers’ perceived emotions. Further moderation analysis confirmed that PRCO weakens the positive interaction between emotions and consumers’ expected willingness and/or desire to practically accept the use of AI-enabled devices, whereas there is a positive moderation effect of consumers’ PRCO in the negative interaction between perceived emotions and objection to the practical use of AI-enabled devices. Social implications This study has proposed AI usefulness expectancy as a socially responsible approach towards ethical AI deployment and tested the RAIDUA model for acceptance and integration of AI-enabled devices. Originality/value This study uniquely integrates AI usefulness expectancy along with perceived AI PRCO as a moderating factor in the RAIDUA model for AI adoption. Both aspects had been overlooked by frequently used Farooq et al.’s (2017) UTAUT-3, Gursoy et al.’s (2019) AIDUA and Salam et al.’s (2025) unified MOOC utilization model frameworks in AI literature. Therefore, we submit that the RAIDUA model offers a novel and original theoretical contribution in technology integration literature and AI adoption-related body of knowledge.

As artificial intelligence (AI)-enabled systems become increasingly prevalent on contemporary battlefields, global discussions on AI are receiving more attention. Without clear norms and accountability mechanisms, the deployment of AI in military systems risks escalating tensions and undermining humanitarian and legal safeguards. Yet, despite growing concern, the regulatory landscape remains uncertain, with geopolitical considerations and intensifying great-power rivalry focusing on the defence applications of AI. Thus far, Canada’s position on autonomous weapons has fluctuated from supporting the prohibition of systems that lack sufficient human control, to adopting more permissive views alongside the United States and other allies, such as the United Kingdom. Amidst continued geopolitical challenges and the desire to align with allies, Canada recognizes that its role in the global regulation of military AI hangs in the balance. What are the best pathways forward for middle powers such as Canada to be seen as international players in this realm, considering various legal and ethical aspects in the deployment of military AI?