Domain 1 of 5 · Chapter 2 of 2

Responsible AI Principles

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Included in this chapter:

  • Microsoft's six responsible AI principles
  • How Azure tooling supports each principle
  • Exam-pattern recognition: scenario to principle

Microsoft responsible-AI principle → what it addresses → example consideration

PrincipleWhat it addressesExample consideration
FairnessEquitable treatment; similar people get similar outcomes, no group advantaged or disadvantagedAudit approval and error rates across gender, ethnicity, and age (Azure fairness assessment)
Reliability & safetyConsistent, safe behavior under expected and unexpected conditions; resists manipulationStress-test on rare inputs and find cohorts with high error rates (Azure error analysis)
Privacy & securityProtecting and lawfully governing the data the system uses and producesEncrypt data in transit and at rest, restrict access, give users control over their data
InclusivenessEmpowering and engaging people across the full range of ability, experience, and backgroundDesign and test so the system does not exclude users with disabilities or different backgrounds
TransparencyMaking AI decisions understandable; interpretability of model behaviorProvide global and local explanations of a prediction (Azure model interpretability, RAI scorecard)
AccountabilityHumans and organizations remain responsible and keep meaningful control over the systemKeep a human in the loop for high-stakes decisions and capture governance/lineage (Azure MLOps)

Decision tree

Scenario harms a group or excludes some users? Unequal outcomes Excludes people No Fairness similar people, similar outcomes Inclusiveness empower every ability/background Concern is about the data it uses or produces? Leaked / governed data No Privacy & security protect & lawfully govern data Behaves unpredictably or fails under odd inputs? Inconsistent / unsafe No Reliability & safety consistent, safe under all conditions Nobody can explain the decision? Unexplainable No human in control Transparency explain how it decided Accountability humans keep responsibility/control
Cheat sheet

Cheat sheet

  • Microsoft's Responsible AI Standard names exactly six principles
  • Fairness means similar people get similar outcomes
  • Reliability and safety means behaving predictably under odd inputs
  • Privacy and security means protecting and lawfully governing data
  • Inclusiveness means designing for every ability and background
  • Transparency means people can understand how the AI decides
  • Accountability keeps humans responsible and in control
  • Fairness is unequal outcomes; inclusiveness is being left out
  • Reliability/safety is behavior; privacy/security is data
  • Transparency is explaining; accountability is being responsible
  • Microsoft pairs fairness with inclusiveness, transparency with accountability
  • Most-accurate is not automatically responsible
  • Content Safety and Prompt Shields filter harmful content
  • Azure OpenAI's safety system blocks filtered prompts with HTTP 400
  • Azure AI Services encrypt by default; use CMK for key control
  • Allocation harm withholds opportunity; quality-of-service works worse
  • Fairlearn uses sensitive features to measure fairness disparities

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Also tested in

References

  1. What is Responsible AI - Azure Machine Learning
  2. What is Azure AI Content Safety?