Responsible AI 101
Responsible AI fundamentals
An introduction to core concepts, evidence, and terminology. For implementation work, use the controls and methods directory.
The essentials
What responsible AI means
Responsible AI means developing and using AI with attention to its benefits, risks, and effects on people.
That includes reliability, safety, privacy, fairness, transparency, and accountability. These concerns are connected, and the right questions depend on how a system will be used.
This introduction draws on the NIST AI Risk Management Framework and OECD AI Principles. The questions below are our editorial reading guide.
Who is affected?
Start with people. Ask who benefits, who could be harmed, and whose experience is missing.
Explore fairness & human impact2What is the evidence?
Look beyond a headline score. Ask what was tested, under which conditions, and what remains unknown.
Explore evaluation & assurance3Who can intervene?
Follow the responsibility. Ask who can approve an action, challenge an outcome, or stop the system.
Explore governance & accountabilityReading the evidence
How to read a claim against its evidence
Our briefings connect developments to cited sources. Here is how to keep your bearings as you read.
See the complete evidence standard- Find the original source.
A research paper, an official document, and a vendor announcement offer different kinds of evidence.
- Separate a finding from an interpretation.
What did the source demonstrate? What does the author think it means? How far does the conclusion extend?
- Read the qualifications.
Dates, test conditions, cautions, and conflicting results help explain how much confidence a claim deserves.
Vocabulary
Useful terms in everyday language
Open a term when you need it.
AI agent
An AI system that can take steps toward a goal, often by using tools. Its permissions and the consequences of its actions are part of what needs to be evaluated.
Evaluation
A structured test of a specific claim about a system. A useful result explains the task, conditions, measurements, and limitations.
Benchmark
A shared set of tasks used to compare systems. Performance on that set may not carry over to every real-world setting.
Red-teaming
Deliberately challenging a system to discover failures or misuse. It can reveal weaknesses; it cannot demonstrate the absence of all weaknesses.
Human oversight
People having the information, authority, and practical ability to review or intervene in a system's decisions and actions.
Assurance
A reasoned case, supported by evidence, that a system meets defined expectations in a specified context.
Next step
Where to go from here
Explore all six topics, or go directly to the detailed research.