Ethics you can audit, not admire.
Principles that live in a PDF protect nobody. Ours are properties of the software — each one checkable in the audit record of any script we've ever marked.
01
A learner can always ask why.
Every grade, referral and integrity signal carries verbatim evidence from the learner's own work and written reasoning. 'The model decided' is never the answer — the record is.
02
Machines never punish.
Integrity outputs — AI-usage indicators, plagiarism signals, reference-credibility flags — are evidence for a qualified human's judgement. No automatic penalty exists anywhere in the system.
03
The same script gets the same grade.
Fairness starts with consistency. Determinism by architecture means a learner's grade doesn't depend on the time of day, the queue position, or a sampling seed.
04
Humans hold the posts that matter.
Assessor-in-the-loop modes — sampling, review-before-release, approval — are first-class features, not workarounds. Centres choose where human judgement sits, and it always sits somewhere.
05
Learner data is minimised and bounded.
The pipeline stores decisions and quoted evidence, not profiles. No learner data goes to consumer AI services, retention follows centre instruction, and on-premise or in-region deployment keeps data where the law expects it.
06
Changes are owned and reversible in the record.
Every behavioural change is a numbered amendment with rationale, replay-tested before release. A release is blocked outright if any calibration script flips between Refer and a clear Pass.
Because a grade is someone's next step.
Behind every script is a promotion case, a visa condition, a career change. That's why we treat consistency, evidence and human oversight as engineering requirements — the same way aviation treats checklists.