Decision: Adopt NDC‑Sharded Indices and Advance Self‑Recognition Evolution

Context #

Recent workstreams consolidated two major directions: reorganizing indices into NDC‑aligned shards and iterating on self‑recognition capabilities across knowledge assets and synthesis layers. Supporting materials also include guidance on trustworthy AI, AI incident response runbooks, inner speech for machine self‑recognition (MSR) with ablation strategies, baseline‑first evaluation practices, and data transfer governance.

Decision summary #

Proceed with NDC‑sharded indices as the canonical structure and formalize a unified benchmarking and evaluation plan for self‑recognition evolution, including ablations of inner speech/self‑dialogue.

Rationale #

Sharding by NDC provides clearer topical organization and should improve retrieval and maintenance.
Advancing self‑recognition benefits from inner speech mechanisms that increase transparency and explainability; targeted ablations help quantify their contribution.
A baseline‑first approach ensures objective comparisons across indexing strategies and behavioral variants.
Trustworthy AI principles and incident runbooks provide operational guardrails as the system evolves.
Governance for cross‑border data (e.g., consent disclosures and adequacy considerations) is needed given domain content with international relevance.

Risks (top 3)#

1) Behavioral regressions in self‑recognition after changes

Mitigation: Structured ablation tests; adopt a baseline comparator; roll out behind flags; monitor with incident runbooks.

2) Retrieval quality shifts from sharding (precision/recall trade‑offs)

Mitigation: Benchmark pre/post against a fixed baseline; supplement with qualitative error analysis using a structured typology; tune shard assignment strategies iteratively.

3) Compliance gaps in cross‑border data handling

Mitigation: Provide required disclosures for international transfers; prefer destinations with recognized adequacy; document safeguards and informed consent practices.

Assumptions (top 3)#

NDC‑sharded organization improves maintainability and retrieval alignment versus monolithic indices.
Inner speech contributes measurably to MSR transparency and accuracy, and its effect size can be isolated via ablation.
A risk‑based AI governance approach will be applied across the lifecycle, including incident response readiness and third‑party oversight where relevant.

Evaluation plan #

Baseline: Establish a simple, stable baseline for both indexing (pre‑shard configuration) and self‑recognition (inner speech disabled or minimal) as the first comparison point.
Indexing benchmarks: Measure retrieval effectiveness before/after sharding; include quantitative retrieval metrics and qualitative error categorization using a structured error typology.
Self‑recognition ablations: Remove or degrade self‑dialogue to quantify attribution; compare task success, explanation quality, and error rates.
Trust and reliability checks: Map tests to trustworthy AI characteristics (validity, reliability, resiliency), and run incident simulations using a lightweight runbook.
Governance review: Validate cross‑border data flows, disclosures, and adequacy assumptions where applicable.

KPI impact assumptions #

Retrieval effectiveness: Improved topical routing and reduced cross‑topic contamination.
Explainability: More transparent decision traces via inner speech; clearer rationales surfaced to evaluators.
Reliability: Lower incident frequency/severity through runbook adoption and staged rollouts.
Quality: Reduced critical errors per a multidimensional error typology; better alignment between intent and outputs.

Next steps checklist #

Define baselines and acceptance thresholds for indexing and self‑recognition.
Execute pre/post sharding benchmarks and analyze error profiles.
Run inner‑speech ablation tests and record contribution deltas.
Integrate AI incident runbook drills into the deployment plan.
Complete cross‑border data disclosure and adequacy checks, documenting safeguards.