Workplace AI ethics through a multi-stakeholder lens: a qualitative inquiry into responsibility, trust, and the limits of principles

Abstract

This study examines how a heterogeneous group of workplace stakeholders interprets the ethical risks, responsibilities, and opportunities associated with the adoption of artificial intelligence (AI) at work, and considers how their lived accounts align with—and depart from—the principle-based frameworks that currently dominate AI ethics policy. Seven semi-structured interviews were conducted in October and November 2024 with stakeholders spanning seven distinct workplace roles (ethicist, AI expert, entrepreneur, social scientist, politician, employee, artist). Interviews were conducted in German, translated, transcribed, member-checked, and coded in MaxQDA through an abductive, three-stage coding procedure (open, axial, and selective coding), in which an a priori topic structure derived from the interview guide was placed in dialogue with data-driven codes. Coded themes were subsequently mapped against the five-principle AI ethics framework that has emerged in international policy discourse (transparency, justice and fairness, non-maleficence, responsibility, privacy). Six recurring concerns structured the data: responsibility and control; impact on employment and work; societal power structures; cultural and creative dimensions; trust and transparency; and ethics and values. Across roles, participants converged on the irreducibility of human responsibility and on the demand for verifiable transparency, but diverged sharply on questions of cultural homogenisation, the locus of accountability between developers and deployers, and the appropriate regulatory response. The pattern is consistent with what the literature describes as the gap between principle and practice in AI ethics: high-level principles enjoy broad assent, but their operational interpretation is contested role by role. The study contributes an exploratory, multi-stakeholder qualitative account of workplace AI ethics in which seven deliberately heterogeneous roles speak to a common set of themes. Findings are positioned as hypothesis-generating for subsequent, properly powered work, and connect the workplace-AI-ethics literature to the principle-based debate within AI governance scholarship. For managers, the data point to the need for verifiable transparency mechanisms (audits, logs, explanations) and to a redistribution of accountability across developer, deployer, and operator. For regulators, the findings illustrate why the high-risk classification of workplace AI under the EU AI Act is welcomed by frontline stakeholders but also why its meaningful implementation requires going beyond principles toward enforceable practice.