Algorithmic Transparency

Algorithmic Transparency — Cognition Strategy Group

Document Class: Technical Disclosure Instrument
Revision Cycle: Bi-Annual Mandatory Review
Governing Authority: Cognition Strategy Group Technical Standards Board

1.1 Transparency as Architectural Requirement

Cognition Strategy Group (hereinafter "the Firm") treats algorithmic transparency not as a disclosure obligation appended post-deployment, but as a first-order architectural requirement enforced at the system design stage. Opacity in AI systems deployed at enterprise scale is an operational liability. Transparency is engineered in, not audited in retrospectively.

This document defines the Firm's binding technical standards for model disclosure, inference auditability, invariant specification, and ongoing transparency obligations across all active Mandate Engagements.

1.2 Model Disclosure Standards

Prior to production deployment, the Firm provides the Client with a Model Disclosure Statement (MDS) for every primary and secondary model component in the deployed system. The MDS includes:

• Model provenance — foundation model identity, version, training cutoff date, and provider

• Fine-tuning documentation — training dataset composition, labelling methodology, and validation set construction where applicable

• Capability boundaries — documented performance envelope including known failure modes, out-of-distribution behaviour, and confidence degradation conditions

• Licence and usage restrictions — applicable model licences, provider terms of service, and any usage restrictions relevant to the Client's deployment context

• Carbon and compute disclosure — estimated training compute and inference energy consumption for environmentally regulated Client entities

1.3 Invariant Checking and Constraint Specification

The Firm requires that all production pipeline deployments include a formally specified Invariant Set — a documented collection of system behaviours that must hold unconditionally across all inputs and operating conditions. Invariants are enforced through:

• Pre-inference input validation — schema validation, range checking, and adversarial input pattern detection applied before model invocation

• Post-inference output validation — semantic constraint checking, factual consistency verification where ground truth is available, and confidence threshold enforcement

• Pipeline integrity assertions — cryptographically signed checkpoints at each DAG node confirming that data has not been corrupted, truncated, or adversarially modified in transit

• Automated invariant monitoring — continuous runtime verification against the specified invariant set with alert escalation upon violation

Invariant violations are logged to an immutable audit ledger and surfaced to the Client's designated Model Governance Owner within fifteen (15) minutes of detection.

1.4 Inference Auditability Requirements

Every production inference event in a Firm-deployed system is logged with the following minimum metadata:

• Timestamp — UTC, millisecond precision

• Input hash — SHA-256 hash of the pre-processed input payload

• Model version identifier — exact model checkpoint or API version string

• Prompt template version — version-controlled identifier for the prompt configuration used

• Output hash — SHA-256 hash of the raw model output prior to post-processing

• Confidence or log-probability — where accessible via model API

• Post-processing transformations applied — enumerated list of all output mutations between raw model response and system output

• Human escalation flag — boolean indicating whether the output was routed for human review

Audit logs are retained for a minimum of twenty-four (24) months, stored in tamper-evident append-only ledgers, and available for Client inspection within forty-eight (48) hours of written request.

1.5 Prompt Engineering Disclosure

The Firm acknowledges that system prompt configuration constitutes a critical determinant of model behaviour and is therefore subject to transparency obligations equivalent to model weight configuration. For all deployed systems, the Firm maintains version-controlled prompt registries accessible to the Client, including:

• System prompt versioning — all prompt modifications are tracked with author, timestamp, and stated rationale

• Prompt injection controls — documented input sanitisation measures preventing adversarial prompt injection

• Context window management documentation — methodology for context truncation, retrieval augmentation, and conversation history handling

1.6 Ongoing Transparency Obligations

Transparency is not a point-in-time disclosure. The Firm commits to:

• Monthly model performance reports — accuracy, latency, cost, and drift metrics against baseline for all active production systems

• Immediate notification of any foundation model update by a third-party provider that materially affects system behaviour

• Annual transparency audits — independent review of deployed system transparency compliance conducted by a qualified third party, findings disclosed to the Client in full

• Regulatory disclosure support — full cooperation with Client obligations to disclose AI system characteristics to regulators, auditors, or judicial authorities, including preparation of technical explanations suitable for non-specialist review