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AIFA

AI-Fact Assist Modules

1. Overview

AI-Fact Assist Modules (AIFA) are assistive calculation engines that help corroborate, challenge, or contextualize factual claims using AI reasoning, external knowledge sources, or logic models.

AIFA engines do not assert truth. They provide confidence-weighted assessments to support human or policy decisions.

2. Design Principles

  1. Advisory, Not Decisive
    AIFA outputs inform decisions; they do not make them.

  2. Source Transparency
    External sources and reasoning paths must be recorded.

  3. Confidence-Weighted Outputs
    All confirmations or challenges include uncertainty indicators.

3. Scope of Responsibility

3.1. What AIFA Engines Do

  • Check factual consistency across sources
  • Corroborate claims using external datasets or AI reasoning
  • Identify contradictions or weakly supported statements
  • Provide confidence-weighted fact assessments

Typical use cases:

  • Checking reported figures against benchmarks
  • Validating narrative claims against public data
  • Supporting verifier or reviewer workflows

4. What AIFA Engines Do Not Do

  • ❌ Override authoritative data
  • ❌ Emit canonical USO signals
  • ❌ Perform primary computation
  • ❌ Enforce policy or compliance rules

5. Inputs

AIFA engines consume:

  • Claims or statements to assess
  • Reference datasets or sources
  • Reasoning or verification rules

6. Outputs

AIFA engines produce:

  • Fact assessment results (confirm / challenge / uncertain)
  • Confidence scores
  • Source and reasoning provenance

Outputs are typically reviewed or gated by:

  • CFIL
  • ZARA
  • Human reviewers

7. Governance & Trust

AIFA engines operate under strict governance:

  • Outputs are always labeled assistive
  • External dependencies are disclosed
  • Results are never silently applied

8. Canonical Identification

  • Engine Type: AIFA
  • USO Code: AIFA
  • Category: Assistive Calculation Engine
  • Layer: Computation Hub
  • NLPI — NLP Interpretation Engines
  • CFIL — Confidence Filter Engines
  • ZARA — Orchestration & Explainability

Status: Stable
Owner: Intelligence Governance / ZARA

GitHub RepoRequest for Change (RFC)