Consumer Interest and AI, Phase 2 Response: A Consumer AI Accountability Framework
An operational framework for adaptive and agentic consumer services, combining a five-tier functional risk ladder with mandates, action receipts, prohibited optimisation objectives, vulnerability-event controls, rights-friction parity, evidence packs, redress by design and earned safe harbours.
What the report examines
An operational framework for adaptive and agentic consumer services, combining a five-tier functional risk ladder with mandates, action receipts, prohibited optimisation objectives, vulnerability-event controls, rights-friction parity, evidence packs, redress by design and earned safe harbours.
- Classifies consumer AI by function across assistive, advisory, consequential, agentic and systemic tiers, with safeguards rising as autonomy, materiality, opacity, vulnerability, irreversibility, scale and evidence asymmetry increase.
- Separates data consent, AI assistance and authority to act, requiring bounded mandates, contemporaneous approval, action receipts, pausing, reversal and rapid correction for consequential agentic acts.
- Places optimisation objectives, vulnerability events, rights-friction parity, evidence packs, redress and cross-regulatory cooperation at the centre of consumer AI accountability.
- Pairs earned safe harbours for bounded, reversible and inspectable uses with qualitative red lines and a practical twelve-month implementation route.
From legal boundary to operational consequence.
The report sits within DigiData’s wider practice of connecting legal interpretation with use cases, institutional incentives, evidence and implementable choices. The downloadable PDF remains the authoritative publication; this page provides a concise, indexable summary and clear route to the source.
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