Abstract The growing use of artificial intelligence (AI) in business decision-making is changing how people think about responsibility, accountability, and moral justification. As companies use AI systems for prediction, optimization, and automation, decisions are increasingly based on socio-technical systems in which data, models, organizational incentives, and human judgment work together to produce results. This article contends that traditional frameworks in business ethics and corporate governance are increasingly encountering conceptual challenges when applied to AI-mediated decisions, especially in contexts characterized by opacity, scale, and adaptive behavior that hinder traceability and contestability. The article constructs a comprehensive analysis of three ethical-responsibility domains: algorithmic decision-making (bias, opacity, and responsibility gaps), organizational governance (authority redistribution and oversight challenges), and stakeholder fairness (discrimination risks, trust, and legitimacy), drawing upon interdisciplinary research in business ethics, corporate governance, and AI ethics. The analysis takes place in a changing regulatory environment, including the EU Artificial Intelligence Act, the General Data Protection Regulation, and international standards and guidance from bodies such as the OECD and NIST. The article subsequently presents a governance-focused conceptual model for ethical AI in business, linking explainability, auditability, human oversight, and stakeholder participation as interdependent components. Instead of giving a set of rules to follow, the model is meant to be an analytical tool for corporate AI governance that goes beyond mere rule-following and supports long-term legitimacy and value creation.
Martin Pazdera (Mon,) studied this question.