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If AI is shaped by its environment and experiences, who is raising it—and what does responsible “parenting” mean? The metaphor is useful for thinking about stewardship, but AI systems are engineered artifacts, not children. Raising one means choosing its data, objectives, feedback, safeguards and operating context, then monitoring how it behaves.
What does it mean to “raise” an AI?
AI does not grow up through human childhood. Its behavior is shaped by human decisions: what material it is trained on, what objectives its developers set, how people give it feedback, which constraints are applied and where it is deployed. The Federal Data Prospector’s exact-title item puts the environmental metaphor this way: “Like humans, AI can be a product of their environment and experiences and shape the way they perceive the world through their innate learning capabilities.” That is a framing, not evidence that AI has human perception or development.
In practical terms, raising AI is stewardship across a system’s lifecycle. Developers and deployers select and evaluate data, define intended uses, test for failure modes and establish routes for human review, correction or shutdown. Users and institutions also shape outcomes through the contexts in which they put a system to work.
What values should AI learn first?
There is no universal list of values that can simply be installed in a model and assumed to work in every setting. International guidance instead emphasizes human rights, safety, fairness and accountability, with oversight that remains meaningful throughout design and use.
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- Human rights and dignity: UNESCO’s 2021 Recommendation on the Ethics of AI centers human rights and human dignity, and includes education and research among its policy areas. It is a global standard adopted by UNESCO member states.
- Democratic values and trustworthy behavior: The OECD AI Principles, adopted in 2019 and updated in 2024, call for AI that respects human rights and democratic values.
- Safety throughout the lifecycle: The OECD says systems should remain robust, secure and safe in normal and foreseeable use, misuse and other adverse conditions, without unreasonable safety or security risks.
- Fairness, privacy and accountability: NIST’s voluntary AI Risk Management Framework identifies validity, safety, security, accountability, transparency, explainability, privacy and fairness as trustworthiness characteristics.
- Human oversight: People need enough information, authority and practical ability to question or override a system when its outputs can affect them.
These principles are not a guarantee that any particular AI system is safe or fair. They are criteria to apply to a specific system, its purpose and the people affected by it.
How can people put those principles into practice?
The OECD, NIST and UNESCO frameworks point toward a lifecycle approach rather than a one-time training choice. A useful review asks who is affected, what could go wrong, who can intervene and how the system will be monitored or retired.
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- Define purpose and boundaries. State what the system is intended to do, who will use it, who may be affected, and which uses are out of scope. Set conditions for human review and override before deployment.
- Choose and govern data. Examine whether training and evaluation data are appropriate for the intended use, and assess privacy and potential bias. Document important limitations rather than treating data quantity as proof of quality.
- Set objectives and feedback carefully. Specify the behavior the system should support and the constraints it must respect. Test whether feedback or optimization creates unwanted trade-offs, such as confident but unreliable answers.
- Test realistic conditions. Evaluate validity, safety, security, fairness and robustness under normal use, foreseeable use, misuse and adverse conditions. NIST’s voluntary framework is designed to help organizations incorporate trustworthiness into AI design, development, use and evaluation.
- Make accountability traceable. Identify the people and institution responsible for the system, keep records that support review, and make relevant limitations understandable to users and decision-makers.
- Monitor, repair or decommission. Check performance after deployment, respond to incidents and changed conditions, and maintain the ability to restrict, correct or retire a system when risks cannot be controlled.
NIST released its AI Risk Management Framework on January 26, 2023, and a generative-AI profile on July 26, 2024. The framework is voluntary guidance, not a certification or a substitute for applicable law. The OECD reported in 2023 that more than 1,000 policy initiatives across more than 70 jurisdictions followed its AI Principles, illustrating their reach without proving that every initiative produces effective oversight.
Can AI learn like a child?
Not in a one-to-one sense. Machine-learning systems can change their behavior through training and feedback, but that does not make their process equivalent to a child’s learning, experience or understanding.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Developmental psychologist Alison Gopnik contrasts language models’ ability to summarize known information with children’s capacity, in her experiments, to infer novel causal relationships. She says: “But I think the summary is that even these really powerful AI systems that depend a lot on getting lots and lots of information, can’t do things that even very little children are very good at doing.” The point is not that children outperform AI at every task; it is that success at pattern-based tasks should not be mistaken for human-like learning.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Who is responsible for an AI system’s behavior?
People and institutions are. A model does not take responsibility for the data, objectives, safeguards or deployment choices that shape its use. Developers, deployers and organizations that rely on AI need clear accountability, ongoing evaluation and the ability to intervene when the system causes harm or no longer behaves acceptably.
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