Governance, Audit and Trust
Incorporating human knowledge, auditing XNN outputs, Unique Verification Codes and privacy-preserving auditability.
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01
Incorporating Human Knowledge into XNNs
Human Knowledge Incorporation connects explicit expert or organisational knowledge with the learnt structure of an Explainable Neural Network. Rules, constraints, taxonomies and causal models can be represented symbolically, reviewed and managed alongside data-derived behaviour.
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02
Auditing XNN Outputs
Auditing an Explainable Neural Network output means reconstructing the relevant decision record: what was submitted, which model ran, what it predicted, which components contributed, what happened afterwards and whether the retained information still matches its integrity reference.
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03
Unique Verification Codes
Unique Verification Codes are cryptographic hashes calculated from selected information about an Explainable Neural Network model or query. They provide tamper-evident fingerprints that support later integrity checks.
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04
Privacy-Preserving Auditability
Auditability does not require every reviewer to see every raw field. A privacy-preserving design separates the evidence needed for integrity and lineage from the sensitive data needed only for authorised investigation.
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05
Trusted AI Infrastructure
Trusted AI infrastructure is not a single feature. It is the connected system that preserves lineage from data and knowledge through induction, deployment, runtime explanation, monitoring, version change and independent audit.
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06
Explainable AI in Finance and Compliance
Compliance duties for high-impact AI decisions are easier to meet when explainability, audit trails and model governance are part of the architecture rather than added afterwards. This article sets out what regulators ask for and what a defensible decision record contains.