Explainer
What Is Responsible AI?
Understand responsible AI as a practical commitment to useful, lawful, fair, reliable and accountable outcomes across an AI system's lifecycle.
2 min readRESPONSIBLE AI & AI GOVERNANCE
Govern the use. Keep people accountable.
From Experimentation to Safe, Accountable AI Adoption
A BUSINESS AI GOVERNANCE FRAMEWORK
Move from isolated experiments to an accountable operating system.
Identify where AI is used, assess the actual use and consequence, assign decision rights, test evidence, control deployment and keep monitoring connected to action.
INVENTORY → ASSESS → GOVERN → TEST → DEPLOY → MONITOR → IMPROVE.
01 / Understand
What makes AI use responsible and governable?
Use proportionate evidence and controls, with a named person responsible for the decision and its consequences.
Explainer
What Is AI Governance?
Learn how AI governance assigns decisions, evidence, controls and accountability across the lifecycle without turning governance into paperwork alone.
2 min read02 / Inventory
Where is AI already being proposed, tested or used?
Use proportionate evidence and controls, with a named person responsible for the decision and its consequences.
Practical guide
How to Build an AI Use Inventory
Create a living inventory of AI systems, embedded features, owners, data, affected people, vendors, permissions and lifecycle status.
2 min read03 / Assess Risk
Who could be affected, what could fail and how reversible is the outcome?
Use proportionate evidence and controls, with a named person responsible for the decision and its consequences.
Practical guide
How to Assess Risk for an AI Use Case
Assess an AI use case through purpose, data, affected people, failure, reversibility, oversight, accountability and monitoring.
2 min readPractical guide
AI Bias & Fairness: What Should a Business Check?
Assess AI fairness by defining affected groups and outcomes, testing error patterns, examining process and providing challenge and correction.
2 min read04 / Govern
Which policy, ownership, data and decision controls should apply?
Use proportionate evidence and controls, with a named person responsible for the decision and its consequences.
Practical guide
How to Create a Business AI Use Policy
Create a usable business AI policy covering approved uses, data boundaries, human review, procurement, disclosure and incident reporting.
2 min readPractical guide
AI, Privacy & Confidential Data
Control the personal, confidential and proprietary information used by AI through purpose, minimization, access, retention and vendor review.
2 min readPractical guide
How to Build an AI Governance Framework
Build a proportionate AI governance system that connects inventory, risk decisions, policy, testing, deployment, monitoring and improvement.
2 min read05 / Test
What evidence is needed before a system can proceed?
Use proportionate evidence and controls, with a named person responsible for the decision and its consequences.
Practical guide
AI Reliability & Quality Control
Define fit-for-purpose AI quality, evaluate representative failures and build verification, fallback and change control into the workflow.
2 min readPractical guide
How to Test AI Before Production
Build a pre-production evaluation that covers representative tasks, difficult cases, misuse, human review, operations and release decisions.
2 min read06 / Deploy
How should an organization evaluate providers and retain human control?
Use proportionate evidence and controls, with a named person responsible for the decision and its consequences.
Practical guide
How to Evaluate AI Vendors & Models
Evaluate AI vendors and models through fit, evidence, data handling, security, controls, change management, portability and accountability.
2 min readPractical guide
What Does Human Oversight of AI Require?
Design meaningful human oversight with authority, information, time, competence, independence and a clear path to stop or correct AI outcomes.
2 min read07 / Monitor
How will the organization know whether the approved use still works?
Use proportionate evidence and controls, with a named person responsible for the decision and its consequences.
Practical guide
How to Monitor AI Systems After Launch
Monitor AI systems for performance, harm, drift, overrides, security, cost and incidents with thresholds, owners and response actions.
1 min read08 / AI Agents
Which permissions may an agent receive, and who answers for its actions?
Use proportionate evidence and controls, with a named person responsible for the decision and its consequences.
Practical guide
AI Agent Permissions & Accountability
Control AI agents through scoped identity, least privilege, approval gates, budgets, logs, monitoring, revocation and named accountability.
2 min read09 / Incidents & Accountability
How will harmful behavior be contained, corrected and reviewed?
Use proportionate evidence and controls, with a named person responsible for the decision and its consequences.
Practical guide
How to Respond to an AI Incident
Prepare to contain harmful AI behavior, preserve evidence, support affected people, communicate, correct systems and govern restart.
2 min read10 / Perspective
Should every technically possible use become an operating system?
Use proportionate evidence and controls, with a named person responsible for the decision and its consequences.
Perspective
Just Because AI Can Do It Does Not Mean AI Should Do It
Why technical capability is only one input to a decision about purpose, legitimacy, dignity, accountability and acceptable consequence.
2 min readConnect governance to the wider OpenSkool system.
Use AI fundamentals to understand system limits, Human Judgment to test claims and decisions, AI Agents to control delegated action and Cybersecurity to protect access and data.