Jun 2026· VINE Journal of Information and Knowledge Management Systems· 0 citations· 50 references
TL;DR
Preliminary empirical support for ethical vigilance as a relevant construct in AI governance and organizational decision-making is provided and useful insights are provided into how AI systems can be designed to be explainable, accountable and ethically sound to counter rising organizational risks.
Abstract
The application of artificial intelligence (AI) is increasingly being used in organizational decision-making. Concerns about its “dark side” are mounting and warrant attention. This paper aims to examine the impact of these aspects on ethical vigilance and on the emergence of dysfunctional results in AI-enabled environments.
The study uses a mixed-methods approach based on the socio-technical systems theory and the technology acceptance model by incorporating theme analysis of interviews with experts to determine gaps in the research and using them to structure equation modeling to analyze survey data consisting of 369 professionals working with Indian tech-enabled companies.
Algorithmic opacity was found to be positively associated with organizational overreliance and negatively associated with trust in AI systems. Organizational overreliance was found to statistically mediate the association between algorithmic opacity and ethical vigilance.
The paper calls on policymakers to leave behind the discourse of efficiency at the cost of AI and take a holistic, governance-focused approach to harmonize the use of AI with transparency, confidence and moral responsibility. This alignment should be achieved not only by law but also by a strategy for long-term organizational integrity and stakeholder trust.
The study provides preliminary empirical support for ethical vigilance as a relevant construct in AI governance and organizational decision-making. It provides useful insights into how AI systems can be designed to be explainable, accountable and ethically sound to counter rising organizational risks.
Abstract The rapid adoption of artificial intelligence (AI) technologies within organizations has significantly changed the way they operate. While much of the existing discourse emphasizes the benefits of AI, significantly less attention has been paid to its negative implications for organizations. This paper addresses this gap by critically examining the negative effects of adopting AI at the organizational level. In this regard, this paper adopts a qualitative research methodology based on a systematic review of recent literature in the field, coupled with an exploratory analysis of real examples and case studies from international organizations, documented and analyzed by the author. The findings show that the adoption of AI can exacerbate organizational vulnerabilities by reducing human oversight, reinforcing existing inequalities and creating new forms of operational and ethical risk. Furthermore, the results indicate that inadequate governance frameworks and limited AI literacy among managers intensify these negative outcomes. The paper discusses the implications of these findings for organizational theory and practice, highlighting the need for balanced strategies for integrating AI. This paper makes an important contribution to the field of using new digital tools, especially AI technologies, within organizations, by providing a relevant analysis of the risks associated with them. In doing so, it promotes a more critical and nuanced understanding of the risks that come with adopting AI in organizations.
Mirela-Andreea Tudose· Proceedings of the Internati...· 0 citations
This paper reconceptualizes managerial rationality in artificial intelligence (AI)-augmented decision-making through the notion of algorithmic-bounded rationality (ABR). It argues that AI does not remove boundedness but relocates it into algorithmic constraints related to data volatility, model opacity and governance maturity.
Building on bounded rationality and socio-technical systems theory, this conceptual study develops an ABR framework linking three decision modes (AI-led, human-first and collaborative) to mechanisms of algorithmic boundedness. The framework is further extended through propositions on mode–task fit and governance conditions for sustaining hybrid decision architectures.
The analysis shows that human–AI collaboration represents a distinct rationality configuration rather than a midpoint between automation and human judgment. Under ABR, each decision mode becomes effective under different combinations of data intensity, contextual ambiguity and accountability demands.
Managers should treat AI integration as a redesign of decision governance rather than a technological upgrade, emphasizing appropriate authority allocation and oversight mechanisms.
The study reframes rationality in the AI era by showing how boundedness shifts from human cognition to socio-technical decision infrastructures. It contributes a mechanism-based framework linking decision modes, task conditions and governance arrangements in AI-augmented decision systems.
This study investigates the efficiency paradox of AI, emphasizing the impact of technical optimization on human values, social cohesiveness, and workplace culture and indicates that the ethical and human-centric integration of AI is crucial to ensure that efficiency gains align with inclusivity, justice, and the maintenance of social well-being in contemporary workplaces.
Nandini Aquila Lukito, Nazwa Sabila· International Journal Admini...· 0 citations
The article examines organizational readiness for responsible artificial intelligence by analysing employees’ attitudes toward key dimensions of transparency and accountability in algorithmic decision-making. The study was designed to explore how workers perceive human responsibility for AI-generated outcomes, the need for disclosing the use of algorithms in organizational processes, the role of ethical training, and the importance of algorithmic audit mechanisms. A quantitative survey conducted in 2025 on a sample of 325 respondents was complemented with a multiple correspondence analysis, which made it possible to identify underlying structures in the distribution of responses and to capture differences in the intensity and certainty of opinions. The findings reveal a clear acceptance of fundamental ethical norms such as human oversight and transparency, contrasted with less decisive attitudes toward more advanced organizational practices including systematic ethical training and audit procedures. These results indicate that while employees support foundational principles of responsible AI, organizations still face challenges in implementing procedural and technical mechanisms that ensure full accountability. The study offers practical insights for managers and policymakers by identifying areas where competence-building, communication and institutional frameworks require strengthening. Its originality lies in combining attitudinal data with MCA to provide a nuanced picture of how transparency and accountability are understood by employees in everyday organizational contexts, contributing to the broader debate on ethical and trustworthy AI deployment.
M. Kocot, Janusz Soboń, Krzysztof Bondyra et al.· Journal for Perspectives of...· 0 citations
Modern law firms face the imperative to implement innovative technologies, primarily artificial intelligence (AI), generating both management opportunities and systemic risks. International evidence demonstrates the practical efficacy of AI in the following zones:automated contract analysis and compliance checking (Harvey AI, Luminance, Kira Systems); legal forecasting and judicial decision analytics (Lex Machina, ROSS Intelligence); e-discovery automation and large-scale document processing; standard legal document builders and client-service chatbots. According to the LawPay/MyCase Report (2024), the adoption of specialised AI tools increased work efficiency for 53% of respondents and “significantly” for 24%. At the same time, risks remain: de-intellectualisation, replacement of ethical judgement with algorithmic outputs, and displacement of vulnerable professional groups. This article examines the role of management sociology in balancing automation and the preservation of the human dimension of legal practice. Strategies are proposed for managing human capital, fostering a culture of collaboration, and integrating ethical norms, enabling law firms to adapt to digitalisation while preserving the unique values of the legal profession.
Nikita S. Apanitsin, Svetlana P. Paramonova· Sociopolitical sciences· 0 citations
Although the importance of artificial intelligence (AI) in managerial decision-making has attracted growing attention, understanding of how perceived AI trustworthiness is associated with the adoption of different AI technologies and their perceived impact on decision efficacy (quality, speed, and integrity) remains underexplored. In particular, limited attention has been paid to how ethical conditions are associated with AI adoption and its impact on decision-making. This study examines the relationships among managers’ perceptions of AI trustworthiness, the adoption of predictive AI (PAI) and generative AI (GAI), and perceived decision efficacy. Perceived AI trustworthiness is conceptualized as a multidimensional construct comprising ethics, transparency, and explainability. Using survey data from UK managers analyzed via structural equation modeling, the findings indicate that perceived AI trustworthiness is positively associated with the adoption of both PAI and GAI, which in turn are positively associated with perceived decision efficacy. PAI shows a stronger, more consistent association. Decision complexity weakens the positive association between GAI and perceived decision efficacy but does not moderate the PAI-perceived decision efficacy relationship. To enrich the interpretation, illustrative cases from BYD, Netflix, and HSBC provide contextual insight into how ethical, transparent, and explainable AI practices correspond with managerial AI use and perceived decision-making outcomes. Collectively, the findings suggest that perceived AI trustworthiness is positively associated with responsible AI adoption and higher perceived decision efficacy, while decision complexity is an important boundary condition associated with the perceived efficacy of GAI in managerial decision processes. The findings advance understanding of how perceived AI trustworthiness is associated with the adoption and performance of distinct AI types in managerial decision-making, contributing to debates on responsible AI, managerial accountability, and ethical technology use in organizations.
Guangming Cao, Yanqing Duan, John S. Edwards· Journal of Business Ethics· 0 citations