AI Accountability, Evidence & Responsibility
Most organisations cannot.
They have policies, governance statements, and process documents. But when a specific decision is questioned, the evidence often breaks down. No one can clearly show what the system was authorised to do, who had authority over it, what controls were active at the time, or what records exist to reconstruct what happened.
The AI Accountability Library is a structured professional reference built around that problem.
It is designed to help legal teams, insurers, risk professionals, governance leads, and directors understand what must exist to prove accountability after an automated or AI-driven outcome is challenged.
This is not advisory content and not a consultancy service.
It is a linked evidence reference covering the conditions required to show:
- what the system was authorised to do
- who had authority to approve, restrict, or override it
- what controls constrained behaviour at the point of execution
- what records and evidence must exist
- where legal and liability exposure may arise
The library is built as a graph-mapped reference, linking governance conditions, legal obligations, evidentiary requirements, and failure points across automated decision systems.
It is designed for real-world questions such as:
- Who was responsible for this decision?
- What evidence should exist?
- Can the organisation prove control?
- Where does liability attach if harm occurred?
Inside the Library
- Governance
- Scope & Limits
- Authority & Responsibility
- Control & Monitoring
- Evidence & Records
- Legal Exposure
Designed for
- Legal teams
- Insurers
- Governance professionals
- Risk leads
- Directors
- Public bodies