Ethical Decision-Making in the Workplace: Which Factors Influence Decision-Makers' Willingness to Revise Their Decisions due to AI-Generated Suggestions?
Jul 2026· Human Factors· pp.
187208261461812
· 0 citations· 88 references
Medicine
TL;DR
Human factors play a more prominent role than AI certifications when it comes to trust-building in AI suggestions for ethical decision-making in the workplace, suggesting that overall, AI attitude may overshadow perceived certifier trustworthiness.
Abstract
ObjectiveTo examine how moral intensity and perceived trustworthiness of an AI certifier influence individuals' willingness to change their decision due to AI-generated suggestions.BackgroundAI-supported decision-making is increasingly used in professional contexts and situations with a high moral intensity. Yet it is unclear how different factors influence the willingness to base decisions on AI-generated suggestions in such situations. Perceived trustworthiness in the AI system is key for effective and ethically sound deployment. Investigating the interplay of these factors is critical for understanding AI-supported ethical decision-making and designing AI systems that support responsible decision-making in morally complex workplace environments.MethodWe conducted a 2 × 2 vignette-based online experiment with a representative US sample (n = 546), manipulating moral intensity and AI certifier trustworthiness. Participants made an initial workplace decision (whom to let go), received a deviating AI suggestion, and then made a second decision. Effects of independent and control variables on decision changes were analyzed using hierarchical logistic regression and χ2-tests.ResultsDecision makers were more likely to change decisions in high moral intensity scenarios due to AI suggestions. Certifier trustworthiness had no significant effect, whereas a positive general attitude towards AI increased the likelihood of changing decisions, suggesting that overall, AI attitude may overshadow perceived certifier trustworthiness.ConclusionHuman factors play a more prominent role than AI certifications when it comes to trust-building in AI suggestions for ethical decision-making in the workplace. Further research is needed to clarify how these factors interact with perceived certifier trustworthiness and other contextual factors.ApplicationEncouraging reliance on AI-based recommendations via certifications alone is challenging. Organizations should focus on their employees' general attitudes towards AI to support AI-based ethical decision-making in the workplace. AI systems could play a significant supporting role particularly in decision situations with high moral intensity.
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.
Guangming Cao, Yanqing Duan, John S. Edwards· Journal of Business Ethics· 0 citations
This study aims to explore the ethical challenges of integrating Artificial Intelligence (AI) into leadership decision-making, focusing on the shift from emotion-based to data-driven approaches. It examines the impact of AI on ethical considerations in leadership, with an emphasis on the role of emotional sensitivity as a moderator.
A quantitative approach was employed, utilizing a survey of 300 leaders and managers across various industries in China. The study introduced two novel scales: “AI-supported leadership decisions (AISLDS)” and “emotional sensitivity of leadership decision contexts (ESLDC).” Data was analyzed using exploratory and confirmatory factor analyses, along with regression analysis to test relationships between AI decision-making, emotional sensitivity and ethical considerations.
The study found a significant positive relationship between AISLDS and ethical considerations. Emotional sensitivity was found to moderate this relationship, highlighting that AI struggles with ethically consistent decisions in emotionally charged situations, suggesting that human judgment is crucial in these contexts.
The findings offer guidance for organizations on when AI is suitable for decision-making and when human empathy is essential. It suggests the need for leadership training that emphasizes the integration of AI with human judgment, ensuring that AI complements rather than replaces human compassion and moral reasoning. Additionally, the study informs the development of policies for ethically integrating AI into leadership.
This study advances AI leadership ethics by demonstrating that emotional sensitivity moderates the relationship between AI-supported decisions and ethical outcomes. Through two newly developed and validated scales (AISLDS and ESLDC), it provides a context-sensitive framework that clarifies when AI can be used ethically and when human empathy is indispensable.
Abid Hussain, Muhammad Ehsan, Jing Song· Management Research Review· 0 citations
Groups navigating dynamic and hazardous mountain environments must continuously engage in collective decision-making. However, the assumption that groups naturally make better decisions than individuals is misleading. Groupthink, group polarisation, and other social dynamics can impair judgement and lead to sub-optimal or even dangerous outcomes, making group decision-making a critical factor for safety in mountaineering. This scoping review maps the existing research on group decision-making in mountaineering using a systematic literature search complemented by reflexive thematic analysis. From 107 identified articles, we derived 14 themes, subsumed within six layers of influence, including factors benefitting group-decision making, authenticity, social influence, external pressure, expertise and contextual dynamics. Together, these themes underscore the complexity of group decision- making in the high-stakes environment. Our findings highlight its central role in mountaineering and provide useful directions for future research and practice.
Anna E. Bergauer, Svenja A. Wolf, A. Schittenhelm et al.· Psychology of Sport And Exer...· 0 citations
It is suggested in the paper that a methodology of ethical governance based on principles of responsible AI should be structured, fairness-by-design, transparency, human-in-the-loop oversight, and constant impact assessment, which underscores the fact that AI systems have ethical failures that are seldom technical but rather socio-technical, which necessitate interventions at the policy, organizational governance, and technical design levels.
Fatou Diop· International Journal of Inn...· 0 citations
The results indicate that AI system quality, AI system transparency and AI familiarity significantly enhance AI trust, while AI beliefs have a non-significant effect, and suggest that trust is the main mechanism through which AI-related social and technical factors contribute to improved decision-making outcomes.
Zhaotong Li, Ting-Ong Yan, Kum Fai Yuen· International Trade, Politic...· 0 citations
The findings advance academic integrity research by shifting attention from attitudes to scenario-based decision quality and clarifying the internalization mechanism through moral cognition.
Hao Deng, Minli Yang, Li-Ling Huang et al.· Frontiers in Psychology· 0 citations
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