Trustworthy AI with AGISystem2 Research

Home · Theory · Trustworthy AI Verifiable, explainable, auditable AI systems

Trustworthy AI isn't about making AI less capable. It's about making AI verifiably capable—capable in ways we can check, explain, and trust. AGISystem2 provides the formal foundations for building AI systems that meet this standard.

Status: Trustworthy AI patterns are research-level (DS08). They are not shipped as runnable Core/config theory sets.

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Agent Planning

Research pattern: formal tool semantics and plan validation (external planner/runtime required).

Compliance

Research pattern: encode policies/regulations as constraints with proof traces (audit logging/export is external).

Explainability

From proof traces to contrastive explanations and provenance.

Synthetic Data

Generate guaranteed-correct data from theories (System 2 supervision).

Bias Study

Root-cause bias at the rule/definition level, not just outcomes.

Research

Open problems: privacy, verification, LLM+formal hybrids, evaluation.

The Trust Problem in AI

Modern AI systems face a fundamental credibility gap:

Challenge LLM Approach AGISystem2 Approach
Explainability Plausible-sounding confabulation Actual proof traces with formal derivations
Consistency May contradict itself across responses Built-in contradiction detection
Auditability Black box decisions Every conclusion traceable to rules and facts
Verification Post-hoc human review Pre-execution formal checking (research pattern)
Uncertainty Confidently wrong Calibrated doubt with known limitations

How HDC Enables Trust

AGISystem2's Hyperdimensional Computing foundation provides unique properties for trustworthy AI:

Determinism

Same input always produces same output. No probabilistic surprises. Perfect reproducibility for debugging and auditing.

Transparency

Every reasoning step corresponds to explicit vector operations. The proof trace is the actual computation, not a post-hoc explanation.

Compositionality

Complex structures built from simple parts via BIND and BUNDLE. Novel combinations work automatically through algebraic composition.

Graceful Degradation

As knowledge bases grow, accuracy decreases smoothly rather than catastrophically. Systems can be designed to fail safely.

Trustworthy AI Patterns

AGISystem2 documents research-level patterns for common trustworthy AI requirements:

AI Agent Planning

Research pattern: tool semantics with preconditions/effects and plan validation. Requires external planner/runtime integration.

Key benefit: Plans can be checked before execution

Compliance & Verification

Research pattern: encode regulations (GDPR, HIPAA, internal policies) formally. Compliance checks + proof traces.

Key benefit: Violations can be prevented when integrated

Explainability

Multi-level explanations from full proof traces to natural language summaries. Contrastive and counterfactual explanations. Verifiable reasoning.

Key benefit: Real explanations, not confabulations

Synthetic Data Generation

Generate unlimited training data from formal theories. Train LLMs (System 1) with System 2 knowledge. Guaranteed correctness.

Key benefit: Bridge symbolic and neural AI

Bias Study

Systematic bias detection through definition impact analysis. Counterfactual fairness testing. Rule-level root cause identification.

Key benefit: Find and fix bias at the source

Research Directions

Open problems in formal verification, privacy-preserving reasoning, LLM+HDC hybrids, and continuous compliance monitoring.

Key benefit: Frontier research opportunities

The Trustworthy AI Stack

Building trustworthy AI systems with AGISystem2 follows a layered approach:


  +--------------------------------------------------+
  |              Application Layer                    |
  |    Agent Planning | Compliance | Scientific       |
  +--------------------------------------------------+
  |               Theory Layer                        |
  |    Domain rules, constraints, relationships       |
  +--------------------------------------------------+
  |              Reasoning Engine                     |
  |    prove() / query() / elaborate()                |
  +--------------------------------------------------+
  |               HDC Foundation                      |
  | BIND / BUNDLE / SIMILARITY                     |
  +--------------------------------------------------+
Key Insight: Trust emerges from the combination of formal foundations (HDC mathematics) and explicit domain knowledge (theories). Neither alone is sufficient—you need both rigorous operations AND clear semantics.

Common Elements Across Patterns

All trustworthy AI patterns in AGISystem2 share these characteristics:

  1. Encode domain knowledge as theories—explicit facts, rules, and constraints
  2. Express rules as formal constraints—obligations, prohibitions, permissions
  3. Use prove() for validation with explanation—get reasoning traces, not just yes/no
  4. Generate proof traces—every decision traceable (audit logging/export is external)
  5. Provide actionable remediation suggestions—not just "error" but "here's how to fix it"

Research Directions

AGISystem2's approach to trustworthy AI opens several research directions:

Full research directions document →

Getting Started

To build trustworthy AI applications with AGISystem2:

  1. Identify the trust requirements for your domain (explainability, auditability, compliance, etc.)
  2. Model your domain as a theory with facts, rules, and constraints
  3. Use the reasoning engine to validate actions before execution
  4. Generate explanations and proof traces for decisions (audit logging/export is external)
  5. Handle uncertainty explicitly—distinguish "unknown" from "false"

Related Documentation

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