AI Ethics and Accountability: Who Benefits When AI Gets It Wrong?

Every conversation about AI risk focuses on what could go wrong. Almost none of it asks who benefits when it does. That is the question that actually tells you how the technology will develop.

In my Technology and AI: Utopia or Dystopia course at Hanyang, students work through five analytical lenses applied to real technologies and real consequences. The lens that produces the most discomfort, and the most genuinely useful analysis, is the one I call incentives. The incentive lens asks a simple question about any technology, any policy, or any system: who benefits when this goes wrong? Not who is harmed, which is the easier question and the one most ethics frameworks start with. Who benefits.

When an AI hiring tool produces biased results that systematically disadvantage certain candidates, the company using the tool has plausible deniability. The tool made the decision, not the hiring manager. The liability is diffused. The discrimination continues. Who benefits from that ambiguity? Not the candidates. The benefit flows to the organisation that can now automate a decision it was already making and call it objective. This is not cynicism. It is analysis.

The Governance Lag Problem

Research on technology governance consistently documents governance lag: the gap between the deployment of a technology and the development of frameworks capable of managing its consequences. AI is experiencing one of the most pronounced governance lags in the history of consumer technology. In that gap, the incentive question becomes the most practically useful analytical tool available. Regulation has not arrived. Institutional norms are still forming. In the absence of external accountability, the behaviour of any system is determined by the incentives operating on the people who build and deploy it.

The Three Questions

In the course I teach at Hanyang, students apply three questions to every AI system they analyse. Who commissioned this tool and what are they optimising for? The gap between the stated purpose and the actual optimisation target is usually where the most significant risks live. Who bears the cost when this tool produces a wrong output? If the answer is a different party from the one that benefits from right outputs, the incentive to improve is weak. Who has the power to change this tool when it is producing harmful results? If the answer is nobody who is also harmed by it, the tool will continue operating regardless of the harm it produces.

Why This Matters for Professional Communication

A 2025 report from the OECD on AI in the workplace found that workers in organisations with clearer AI accountability structures reported significantly higher confidence in the tools they used and lower rates of AI-related errors reaching consequential stages. Accountability structure is not a technical problem. It is a communication and governance problem. It requires people who can ask the incentive question clearly and insist on an answer. That is a communication skill. It is also, in 2026, a professional responsibility.

→ This is one of the topics I speak on for corporate and academic audiences: how to think critically about AI tools using the governance and incentive frameworks from my Technology and AI course at Hanyang. The Work With Me page has more on speaking engagements and workshop formats.