// Learn · Responsible AI Hub
Responsible AI, made practical.
This is our trust center for government and enterprise. It explains how we think about fair, safe, transparent, and accountable AI, and it gives you guidance you can use today. The goal is simple: adopt AI in a way that earns and keeps public trust.
Our principles
Four commitments that guide every recommendation
These principles shape our work. They shape the templates we publish, the guidance we give, and the guardrails we recommend.
Fair
AI should treat people equitably. We test for bias, watch for unequal outcomes, and correct problems before they reach the people a system affects.
Safe
AI should do what it is meant to do and no more. We favor low risk, high value uses first, and we set guardrails before scaling.
Transparent
People deserve to know when AI is used and how a result was reached. We document data sources, limits, and the reasons behind decisions.
Accountable
A person, not a system, owns every outcome. We keep humans in the loop for decisions that affect people and record who is responsible.
A recognized framework
Guidance aligned to the NIST AI Risk Management Framework
You do not have to invent your own standard. We align our guidance to a framework that public agencies and enterprises already trust, so your work maps to something recognized.
Definition
NIST AI Risk Management Framework. A voluntary framework from the National Institute of Standards and Technology that helps organizations map, measure, manage, and govern the risks of AI systems across their life cycle.
We translate that framework into steps a small team can actually take. Start with a policy, tier your risks, keep a human in the loop for decisions that affect people, and review results on a schedule. Our policy templates and governance checklist give you a running start.
Public sector trust
Guidance public agencies already recognize
You do not have to invent your own standard. We align our guidance to the NIST AI Risk Management Framework, a framework that public agencies and enterprises already trust, so your work maps to something recognized.
We translate that framework into steps a small team can actually take. Start with a policy, tier your risks, keep a human in the loop for decisions that affect people, and review results on a schedule. That is how you adopt AI in a way that earns and keeps public trust.
Bias
Understanding and reducing bias
Definition
AI bias. A pattern where an AI system produces outcomes that are systematically less fair for some groups of people, often because the data it learned from carried that pattern.
Bias is not always obvious. It hides in the data a model learned from and in the way a task is framed. The fix is discipline, not luck. Define what fair means for your use case, test outcomes across groups, involve the people a system affects, and keep a human reviewing high-stakes results. When you spot a problem, correct it before you scale.
Privacy
Protecting the data people trust you with
Good AI practice starts with good data practice. Do not paste sensitive or personal information into consumer tools. Know where your data goes, who can see it, and how long it is kept. Collect only what you need, and be clear with people about how their information is used. Our Data Privacy quick-reference covers the questions to ask before you share anything with an AI assistant.
Human oversight
Keep a person in the loop
AI is a force multiplier, not a decision maker. For any decision that affects a person, such as benefits, hiring, or enforcement, a qualified human must review and own the outcome. Design your process so it is clear who checks the work, when they check it, and what happens if the AI is wrong. That accountability is what turns a helpful tool into a trustworthy one.
Governance
Set the rules before you scale
Governance is how good intentions become durable practice. Put a simple AI use policy in place, assign clear roles, tier your uses by risk, and review results on a regular schedule. You do not need a large program to start. You need a written policy, a named owner, and a habit of checking outcomes. Our AI Governance Checklist and policy templates give you the starting structure.
Take the next step
Build responsible AI into your organization
Start with a policy, a checklist, and a plan. All free.