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AI can improve productivity, scientific work and public services, but those gains depend on the task and how a system is used. The same systems can also produce harmful decisions, spread errors or enable new forms of abuse. Regulation can set protections and responsibilities; it cannot make every system safe or guarantee that benefits will follow.
What can AI do well—and why are the benefits not automatic?
AI’s value is practical, not inherent: it depends on whether a system is suited to a particular task, the quality of its data and implementation, and whether people can check its results. A tool that helps with one kind of work may be unreliable or unsuitable for another.
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Productivity and scientific progress
The OECD’s 2024 analysis identifies productivity gains and accelerated scientific progress as potential benefits of AI. These are possibilities, not proof that every adoption raises productivity or produces a scientific breakthrough. Outcomes depend on the problem being addressed and the way the technology is integrated into work.
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In its 2025 report on government use, the OECD describes ways AI may help public bodies automate or tailor services, support decision-making, detect fraud, and enrich civil servants’ work and learning. Those applications can be useful when they improve a service without obscuring who is responsible for a decision or making it harder for people to challenge an error.
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What risks should people and institutions weigh?
Risks vary by system and context. The OECD’s reports identify potential harms and policy concerns; they do not claim that every risk will occur in every deployment.
Skewed data and harmful decisions
In government settings, skewed data can lead to harmful decisions. If the data or assumptions behind a system do not fit the people affected, an apparently consistent output can still be unfair or wrong.
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Opacity, accountability and overreliance
When people cannot understand how an AI-supported decision was reached, accountability can weaken. Overreliance creates a related danger: staff may accept an incorrect output, allowing errors to propagate. The OECD also warns that such reliance can widen digital divides, particularly when access to effective human assistance differs between groups.
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The OECD’s 2024 paper identifies more sophisticated cyberattacks, manipulation and disinformation, fraud, democratic harms, incidents in critical systems, concentration of power, and exacerbated inequality and poverty among its priority concerns. These are risks to assess, not predictions that each outcome is inevitable. A system’s likely impact depends on what it can access, who controls it, and the consequences if it fails or is misused.
Why regulate AI?
Regulation can establish protections and responsibilities where the consequences of AI use matter: safety, fundamental rights, transparency and risk management. The European Commission says the EU AI Act is intended to address risks to health, safety and fundamental rights while taking account of AI’s potential benefits and supporting innovation. That is the Commission’s stated aim, not a guarantee that harms will disappear or that innovation will continue unchanged.
The EU AI Act is an example of a legal framework for the European Union, not a law that applies everywhere. Which rules apply depends on jurisdiction and the circumstances of a system’s development or use. The Commission describes the Act as risk-based: obligations are linked to the level of risk rather than treating every AI use identically. See the European Commission’s AI Act FAQ and its official overview of the AI Act.
Rules have staged application dates
The Commission states that prohibitions on certain practices and AI literacy obligations began applying on 2 February 2025, while some high-risk rules have later transition dates. Application is staged, so a single date does not describe when every provision applies. The Commission’s overview is the appropriate place to check the latest dates and transition arrangements before relying on a specific requirement.
What can regulation do, and what can it not do?
| Regulation can | Regulation cannot, by itself |
|---|---|
| Set legal duties and protections around risks, safety, rights and transparency. | Ensure that every AI output is accurate, fair or useful in every context. |
| Clarify responsibilities and require safeguards appropriate to a system’s risk. | Replace sound data, competent implementation, human judgment or ongoing monitoring. |
| Support public trust by making expectations and accountability clearer. | Guarantee that trust is deserved or that people will never be harmed. |
| Leave room for beneficial uses while placing limits on some risks. | Make the trade-offs disappear: compliance can require work, and some uses may be restricted. |
Rules are one part of risk management. They can set a floor for responsible conduct, but a legal framework cannot anticipate every use or substitute for decisions made by the organizations deploying AI.
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What does responsible AI governance look like beyond the law?
The OECD’s 2025 guidance on trustworthy AI is policy guidance, not a binding statute. It groups practical conditions for responsible use into enabling foundations, safeguards and engagement. Those ideas apply to organizations deciding whether and how to deploy a system, including where legislation leaves room for judgment.
- Build capacity: establish governance, suitable data and infrastructure, staff skills, investment, procurement practices and partnerships.
- Put safeguards in place: provide transparency, manage risks, maintain appropriate oversight, and use impact assessments and audits where they help identify or correct problems.
- Engage affected people: involve citizens, civil servants or other relevant groups in decisions about systems that affect them, and make channels for questions or concerns meaningful.
These elements come from the OECD’s 2025 framework on enablers, guardrails and engagement. Its wider report, Governing with Artificial Intelligence, focuses on public-sector use.
How should an organization decide whether an AI use is acceptable?
A useful decision starts with the real-world task and the people affected, not with a general claim that AI is either beneficial or dangerous. The following questions turn the policy principles into a practical review:
- Define the decision or service. What will the system do, and what changes for the person receiving the service or subject to a decision?
- Assess the consequences of error. Could a wrong, biased or manipulated output affect someone’s rights, safety, access to a service or financial position?
- Check data and performance in context. Are the data and evaluation suitable for the people and conditions in which the system will be used?
- Set clear responsibility and oversight. Who reviews outputs, can intervene, and remains accountable for the resulting decision?
- Make the process understandable and contestable. Can affected people get an explanation appropriate to the situation and raise a concern or seek review?
- Monitor after deployment. Are there processes to detect errors, security problems or changing conditions, and to pause or change use when safeguards fail?
- Check applicable obligations. Identify the relevant jurisdiction, the organization’s role and the current application date of any legal requirements.
For a lower-consequence task, safeguards may be simpler; for a consequential decision, a stronger case for testing, documentation, oversight and recourse is needed. The exact obligations depend on applicable law and context, so this checklist is not a substitute for legal advice.
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