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AI can be different from traditional analytics because it can help people explore possibilities beyond patterns in historical data. But it cannot decide what society should value. People and institutions must set the goals, weigh the trade-offs and govern how AI is used.
What makes AI different from traditional analytics?
Traditional analytics often use existing data to describe what has happened, identify patterns or estimate what is likely to happen next. Because that data reflects past conditions, it can also reflect past exclusions, constraints and biases. A future-oriented approach asks a different question: what outcomes do people want, and what changes might help achieve them?
Bill Schmarzo’s essay, published in DataScienceCentral’s ethics archive on April 29, 2024, is summarized there as contrasting analytics that inherit past realities with AI that can focus attention on future aspirations and the learning needed to reach them. That framing is useful as a way to think about AI’s potential, not as proof that AI independently understands or chooses a desirable future.
Can AI guide society without setting its values?
Yes, if “guide” means helping people examine options, anticipate consequences or inform decisions. The OECD defines an AI system as a machine-based system that infers from inputs how to generate outputs such as predictions, content, recommendations or decisions, which may influence physical or virtual environments. Influence is not moral authority: an output can shape a choice without being entitled to determine what the choice ought to be.
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For example, an AI-supported policy analysis might help decision-makers compare possible interventions against a goal they have already chosen. The system can help organize evidence or generate scenarios; people still need to decide whether the goal is legitimate, whose interests count and what risks are acceptable. Those judgments call for human responsibility and, where public interests are at stake, legitimate public processes.
What should a society’s AI aspirations include?
Broad aspirations become more useful when they are expressed as values and safeguards that can guide real decisions. UNESCO’s Recommendation on the Ethics of Artificial Intelligence, adopted by its 193 Member States in November 2021, treats human dignity and rights, just and peaceful societies, diversity and inclusion, and environmental flourishing as core values. Its principles include privacy, fairness, transparency, human oversight, accountability, safety, sustainability and AI literacy.
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UNESCO summarizes the foundation of its Recommendation this way: “At its core, it states that AI must respect human rights and human dignity.” The OECD’s AI Principles, first adopted in 2019 and updated in May 2024, similarly connect trustworthy AI with inclusive growth and well-being, human rights and democratic values, transparency, safety and accountability. Together, these principles help turn “a better future” into questions about who benefits, who may be harmed and what protections are required.
How to assess a future-oriented AI proposal
When comparing an AI proposal with the status quo or another policy option, use questions that make the goal and its consequences explicit. The following questions synthesize UNESCO and OECD principles; they are an assessment aid, not a verbatim checklist issued by either organization.
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- Objective and beneficiaries: What social outcome is being pursued, and who is expected to benefit?
- Evidence and uncertainty: What supports the expected outcome, and what important uncertainties remain?
- Rights and inclusion: How might the proposal affect rights, privacy, fairness and participation, including for people who are often underserved?
- Oversight and accountability: Who can review the system’s outputs, challenge a decision and take responsibility when something goes wrong?
- Safety and resilience: What measures address safety and security risks, and can the decision or system be corrected or reversed if it causes harm?
- Sustainability: Have environmental and broader social effects been considered alongside the intended benefit?
Why principles need practical risk management
High-level values do not, by themselves, show how to identify or manage risks during design, development, use and evaluation. The U.S. National Institute of Standards and Technology’s AI Risk Management Framework (AI RMF) is voluntary guidance intended to help organizations incorporate trustworthiness considerations across those activities. NIST released the framework on January 26, 2023. Its page says version 1.0 is being revised as part of the White House AI Action Plan; that wording means a revision is under way, not that a new version has been completed.
A framework can support more consistent risk work, but it does not select society’s priorities or replace accountability. Organizations and public bodies still have to define what they are trying to achieve, decide which risks are unacceptable and explain how affected people can seek review or redress.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this means for AI literacy
If AI is to inform choices about shared futures, people need enough understanding to question its outputs rather than treat them as neutral facts. AI literacy includes recognizing what a system is being asked to do, asking what evidence and assumptions shape its output, understanding its limits and knowing who is accountable for its use. UNESCO includes AI literacy among its principles and emphasizes accessible education and public understanding.
That literacy matters for decision-makers and the public alike. It helps people distinguish a forecast from a recommendation, a recommendation from a decision, and technical capability from a justified social choice.
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