BAT Rapid
AI Human Review Reality Check. A fixed-scope review of one AI-supported workflow to test whether human review is meaningful, realistic, and evidenced.
Evidence for AI-assisted decisions
I assess whether AI-assisted decisions and workflows remain reviewable, reconstructable, and defensible under scrutiny.
Using BAT (Backward Accountability Traceability).
The evidence gap
In regulated and accountability-sensitive environments, the question is not only whether an AI tool is useful. It is whether the surrounding workflow can explain what happened, who reviewed it, what evidence was relied on, and why the final decision was reasonable.
What I do
AI Human Review Reality Check. A fixed-scope review of one AI-supported workflow to test whether human review is meaningful, realistic, and evidenced.
Focused AI Operational Defensibility Assessment. A deeper single-workflow assessment with targeted evidence review and reconstruction testing.
Full structured BAT assessment of consequential AI-supported operations, for one consequential workflow or a small workflow cluster.
The lens
It is the assessment method used by Accountable AI to examine whether AI-assisted workflows remain reviewable, reconstructable, and defensible after the point where AI output begins to influence real operational outcomes.
The work focuses on whether an AI-assisted workflow can be understood later by someone who was not in the room: an auditor, regulator, complaints handler, legal reviewer, risk lead, or senior accountable owner.
Can a qualified person inspect the workflow and see where judgement was applied?
Can the team recreate the material context, evidence, and handoffs behind a decision?
Can the organisation explain why the decision process was reasonable and controlled?
Assessment logic
Identify where AI output enters the workflow, who owns the decision, and which operational outcome may be influenced.
Test whether review is realistic in the live process, not just required in policy or implied by a sign-off field.
Examine whether the organisation can show the source evidence, AI contribution, human intervention, and decision rationale.
Translate evidence gaps into practical controls, ownership, and time-bound remediation.
Best fit
Consequential workflows include operations where AI-supported outputs influence decisions, records, actions, recommendations, escalation pathways, or customer outcomes.
Operational teams embedding AI into casework, triage, review, monitoring, or advisory processes.
Risk, compliance, legal, audit, and governance leaders who need practical evidence of control.
Product and transformation teams moving from AI pilots into governed operational use.
The work draws on experience across regulated operational environments, including clinical research and NHS settings, where accountability, review integrity, and defensible decision-making already carry real operational consequences.
Example Assessment Outputs
Where AI output begins to influence the operational record.
What shows that human review was meaningful, timely, and recorded.
What would be difficult to explain later under audit, complaint, or regulatory scrutiny.
Start with one workflow
Share the workflow, the decision type, and the scrutiny it may need to withstand. I will help identify the evidence trail that should exist around it.
Discuss a workflow