Ten Principles

of Human-Centered AI

Modern wooden house with stone steps leading up to it, surrounded by grassy landscape and mountain range in the background at sunset.

As AI becomes a core part of everyday products and critical workflows, teams look for principles that can guide how these systems should behave. Almost every organization now publishes an AI ethics statement or trust framework, but most of these documents share the same issue: they are broad, abstract, and hard to apply to real product decisions.

These principles offer a practical philosophy for building LLM features, agentic workflows, and ai systems that behave in ways people can reason about. They focus on what actually matters in day-to-day product development: clarity, reliability, and predictable behavior.

They are written for AI PMs, engineers, and researchers who want to create systems that stay stable under pressure, communicate their intent, and remain grounded in real user needs.

A modern wooden house on a hillside overlooking a lake with mountains in the background during sunset.

1. Make systems explainable.

If people cannot understand why they got a particular output, they cannot trust it. Expose the key factors, constraints, and signals that shaped the result, including any limitations or biases in the underlying data or model.

A lone Joshua tree in a desert landscape with sparse dry bushes, under a clear blue sky.

2. Reduce the surface area for surprise.

AI should behave in stable, expected ways. Avoid unnecessary randomness, hidden states, or behavior that changes without clear cause.

Desert landscape with Joshua trees and mountains in the background during sunset or sunrise.

3. Favor clarity over capability.

A simpler model or workflow that’s understandable is often better than a more capable one that’s opaque or fragile.

Desert landscape with large rocks and boulders at sunset, with distant mountains and a colorful sky.

4. Constrain systems to well-defined boundaries.

Clear limits create predictable behavior. Define what the system should do, and just as importantly, what it should never do.

View of a beach seen from inside a cave, with a large rock formation in the water and other smaller rocks in the distance.

5. Evaluate continuously, not only at launch.

Behavior shifts over time. Evaluation should be continuous, using regression tests, structured prompts, and real user feedback. Assess for bias, uneven performance across groups, and drift that could introduce harmful patterns.

A person walking on a wooden trail through a grassy landscape with mountains and snow in the background.

6. Design features around real human expectations.

Technical correctness isn’t enough. The system’s behavior must match what users think it will do.

A lone, partially submerged tree in a calm lake with autumn-colored trees on the shoreline, mountains in the distance, and a cloudy sky overhead.

7. Make failures observable, recoverable, and safe.

Assume failures will occur. Design workflows where errors are visible, explanations are meaningful, and recovery is simple. Treat biased or uneven behavior as a system failure that is discoverable, correctable, and never silent.

A hiker stands on a ridge overlooking a large lake surrounded by mountains during sunset.

8. Prioritize meaningful user control.

Users should be able to steer, pause, override, or refine the system. Autonomy without control is not helpful.

A wooden dock extends into a calm lake surrounded by green mountainous terrain under an overcast sky.

9. Build with careful iteration, not leaps of faith.

Start with restricted capabilities, test assumptions early, expand carefully, and monitor the system as it grows.

An illustration of a rocket launching from a planet with moons, Earth, and a red planet in space.

10. Document decisions, not just code or models.

A human-centered system is as much about why choices were made as how they were implemented. Write down rationale, trade-offs, evaluation results, and constraints.

Scenic view of a large lake surrounded by rolling hills and mountains under a cloudy sky.

  • They address a core problem in modern AI products: systems that are powerful but unpredictable. Many AI ethics statements are high-level and hard to apply. These principles give teams a practical framework for building LLM features, retrieval systems, and agentic workflows that behave in clear, stable, and reliable ways.

  • They are written for AI product managers, engineers, designers, and researchers who build real AI features for real users. They focus on decisions people make every day: what the system should do, how to constrain it, how to evaluate it, and how users should experience it.

  • They are not positioned as an ethics manifesto. They are a product philosophy grounded in reliability, predictability, and user trust. However, they do incorporate fairness and bias awareness as practical system concerns that affect safety and user experience.

  • No. They complement them. Traditional AI safety frameworks handle broad societal questions. These principles focus on building day-to-day features that behave the way users expect and that teams can maintain over time.

  • No. They apply to LLMs, RAG systems, agents, classifiers, ranking systems, and any component that learns or reasons over data. They work especially well for products where behavior can drift, shift, or become opaque to users.iption

  • Explainability is not academic interpretability. It is about helping users form a workable mental model of how the system behaves. That includes surfacing key factors, constraints, limitations, or relevant data that shaped an output.

  • Bias is treated as a system failure. Continuous evaluation should include checks for uneven performance across contexts or groups, and your workflows should make these issues observable and correctable.

  • Constraints create predictable behavior. Without them, AI systems generate surprise, escalate errors, and reduce trust. Good constraints define both what the system should do and what it should never do.

  • Model behavior changes due to fine-tuning, prompt drift, new context windows, retrieval data updates, or user interactions. Evaluation must be continuous to ensure stability and prevent regressions or harmful behavior.