Investigating Ethical Attitudes Towards AI Decision-Making in Professional Settings

Thesis Abstract

The rapid integration of artificial intelligence (AI) systems into various professional domains has raised critical ethical concerns regarding decision-making processes, prompting the need to understand stakeholders' moral attitudes towards AI-driven decisions. This study investigates the ethical attitudes of professionals across sectors such as healthcare, finance, and engineering towards the deployment and reliance on AI for decision-making. Specifically, it aims to assess the extent to which ethical principles influence the acceptance, trust, and perceived moral legitimacy of AI systems. The research objectives include identifying key ethical considerations shaping professionals’ perspectives, examining demographic and professional factors influencing attitudes, and evaluating the relationship between ethical beliefs and acceptance of AI decision-making. Employing a descriptive cross-sectional survey research design, this study targets a population of 1,200 professionals from three sectors healthcare practitioners, financial analysts, and engineering managers within a metropolitan region. A stratified random sampling technique will be used to select a representative sample of 300 participants, ensuring proportionality across sectors. Data collection involves a structured questionnaire developed based on existing ethical frameworks and theories, including deontological and utilitarian perspectives, as well as a semi-structured interview guide for qualitative insights. The questionnaire will measure variables such as ethical awareness, trust in AI, perceived moral acceptability, and adoption intention. To enhance instrument validity and reliability, the questionnaire will undergo content validation by experts and pilot testing with a subset of 30 professionals, with internal consistency assessed via Cronbach’s alpha coefficient. Data analysis will involve both quantitative and qualitative methods. Descriptive statistics, such as frequencies and means, will summarize demographic data and ethical attitude indicators. Inferential analysis will incorporate multiple regression analysis to examine the predictors of ethical attitudes, while ANOVA tests will compare attitudes across sectors and experience levels. Thematic analysis will be employed to interpret qualitative responses, providing nuanced understanding of underlying moral considerations. The study will also employ structural equation modeling (SEM) to explore the relationships between ethical awareness, trust, and acceptance of AI decision-making, guided by the normative ethical theories of deontology and utilitarianism. Expected findings include identifying significant differences in ethical attitudes among professionals based on sector, age, and level of AI familiarity, with a general trend showing cautious acceptance driven by concerns over transparency, accountability, and moral integrity. It is anticipated that stronger alignment with deontological principles correlates with skepticism towards autonomous AI decisions, whereas utilitarian considerations may enhance acceptance where AI is perceived to maximize societal benefits. The findings will contribute to the existing literature by providing empirical data on the complex interplay between ethics and technology adoption in professional settings, addressing gaps related to sector-specific ethical perceptions and the influence of moral frameworks on technology acceptance. The study’s main contribution lies in elucidating how ethical attitudes shape professionals’ trust and acceptance of AI, offering actionable insights for policymakers, AI developers, and organizational leaders. It will inform the development of ethically aligned AI systems and guide ethical training programs tailored to professional values. The research concludes that fostering transparency, accountability, and ethical literacy is vital for increasing acceptance and responsible implementation of AI technologies. It is recommended that organizations integrate ethical assessments into AI deployment strategies, emphasizing stakeholder engagement and ethical training to mitigate resistance driven by moral concerns. Future research should explore longitudinal changes in ethical attitudes as AI technology advances and becomes more pervasive across different cultural contexts, thereby enhancing the global understanding of AI ethics in professional environments.