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OAIC

ADM transparency under the Privacy Act.

Output: ADM disclosure log per request

Per-decision explanation record with reviewable reasons and human-review pathway.

The scenario

Who: A consumer lender using AI-assisted credit decisioning.

The problem: Declined applicants appeal; the lender can’t reconstruct what features drove the decision two months later; OAIC asks for evidence of the human-review pathway.

Without Fydis

Model logs without context, no consumer-facing reason record, contested decisions handled ad hoc.

With Fydis

Per-decision explanation with top reasons, reviewer queue, OAIC report pack ready on demand.

What it produces

From 10 December 2026, new APP 1.7-1.9 obligations require organisations using ADM in customer-facing decisions to disclose how those decisions are made in their privacy policy. The underlying records the disclosure summarises are the per-decision rationale Fydis captures: input features, model version, decision, and reasons. Contested decisions route to a human review queue. The OAIC report pack regenerates on demand from the same trace.

Verified output · OAIC

Why was application #58931 declined?

Declined

3 reasons · reviewablePrivacy Act APP 1.7 (commences 10 Dec 2026)
RetrieveModel v2.3 · 7 input features · all auditable
VerifyPlaid · Yoti · Equifax · all logged
EvidenceHuman review · 48h appeal window
EvidenceOAIC report pack · on demand

The analysis, step by step

  1. 01RetrieveInput features captured at decision time with provenance
  2. 02VerifyModel decision recorded with version hash
  3. 03VerifyTop reasons extracted (model-agnostic; SHAP or rule-based)
  4. 04EvidenceConsumer notice generated · plain-English reasons
  5. 05EvidenceContested decisions queued for human review · reviewer sign-off captured
  6. 06EvidenceOAIC report pack regenerated on demand from the trace

Frequently asked

Does this work with any model?

Yes. Fydis is model-agnostic, we capture the inputs, decision, and reasons regardless of whether the model is rule-based, gradient-boosted, or LLM-driven.

What about derived features?

On roadmap. We currently trace raw inputs and top-feature attributions; full pipeline lineage for derived features is in flight.

Run this on your data.

The live demo runs this exact analysis on sandboxed data. Book a 30-minute briefing and we’ll show you the same chain on your data, your approval policy, and your regulator clause map.