Oliver Patel distinguishes between three commonly conflated terms in the AI space: AI ethics, responsible AI, and AI governance. He frames them as a layered hierarchy. AI ethics is the most foundational layer — a moral framework for determining right and wrong in AI-related activities, drawing on existing philosophical traditions. Responsible AI sits above it, defining specific principles like transparency, fairness, and accountability that operationalize ethical ideals and form the basis of many AI laws and standards. AI governance is the practical implementation layer — the policies, processes, organizational structures, and training that ensure responsible AI principles are actually adhered to in practice.

Patel emphasizes that professionals focused on governance and compliance should not lose sight of the foundational ethical questions, since without an evolving understanding of AI ethics, governance structures lack a meaningful basis. The post generated significant discussion, with commenters offering alternative framings — notably Carl Gahnberg suggesting the hierarchy could be inverted from a societal perspective, and Emmanuel Goffi critiquing the treatment of ethics as overly simplified. Patel acknowledged feedback and noted plans to add a fourth layer focused on responsible AI engineering and technical AI governance in a future update.

Margaret Mitchell, a prominent AI researcher, shares her concise distinction between "Ethical AI" and "Responsible AI": Ethical AI addresses the entire pipeline of AI development, starting with fundamental questions like who gets a seat at the table, while Responsible AI operates within existing systemic structures and focuses more narrowly on technology rather than society at large. Mitchell describes Responsible AI as a subset of the broader Ethical AI framework, centered on organizational and technical concerns.

Mitchell highlights and endorses an overview by Mia Shah-Dand of Women in AI Ethics™, which elaborates on the distinction in more detail. Shah-Dand frames Responsible AI as centered on individual organizations and technology developers — covering safety guardrails, privacy, governance, compliance, and bias mitigation. AI Ethics, by contrast, centers the needs of society and humanity broadly, encompassing protection of rights (human, civil, labor), independent algorithmic audits, environmental impact assessments, literacy programs, and governance at national and global levels.

The key takeaway is that Responsible AI is a "smaller, more tightly focused version of AI Ethics at an organizational level," while AI Ethics is concerned with issues impacting the vast majority of humanity. Both are important and complementary, but neither is meaningful without diversity and a human-centered approach.