AI Fails Where Reasoning
Goes Unexamined
Validity audits the reasoning behind AI-generated outputs before they are trusted, deployed, or acted upon.
Validity does not build, train, or align AI models. It evaluates whether the reasoning expressed in AI outputs is logically sound, appropriately bounded, and defensible.
Most AI Failures Are Not Model Failures
When AI systems produce harmful, misleading, or costly outcomes, the issue is rarely computation. It is unexamined reasoning.
- Conclusions presented without explicit assumptions
- Correlation mistaken for causation
- Confidence conveyed without evidentiary support
- Risks omitted as outputs scale across systems
- Human reviewers accepting fluent reasoning without scrutiny
AI systems do not fail because they reason badly. They fail because reasoning is trusted without being examined. Validity is designed to surface those failures.
A Reasoning Audit for AI Outputs
Validity analyses AI-generated reports, recommendations, summaries, and decisions to evaluate:
- Whether conclusions logically follow from stated inputs
- Where assumptions are implicit rather than explicit
- How causal claims are constructed or inferred
- Whether uncertainty and downside risk are appropriately represented
- Where fluency masks logical gaps or overreach
Validity does not assess correctness or truth. It evaluates whether the reasoning structure behind an AI output holds up under scrutiny.
The Missing Layer in AI Systems
Most AI governance focuses on models: training data, bias, performance, and alignment. Most AI operations focus on outputs: speed, accuracy, and scale.
Validity operates in between.
It evaluates how AI outputs are reasoned with — whether by humans, downstream systems, or decision-makers — making the logic explicit, reviewable, and auditable.
Before Deployment. Before Reliance. Before Automation.
Validity is used at three critical points in AI workflows:
Pre-Deployment Review
Audit AI-generated analyses or recommendations before they are integrated into decision processes.
Human-in-the-Loop Oversight
Support reviewers by highlighting assumption load, causal gaps, and overconfidence in AI outputs.
Post-Deployment Audit
Re-examine AI-driven decisions after incidents, errors, or drift to identify reasoning failures and systemic risk.
What It Detects
Validity flags reasoning patterns commonly associated with AI misuse and over-trust:
Implicit Assumptions
Critical premises embedded in outputs without being stated or examined.
Causal Hallucination
Causal relationships asserted where only correlation or coincidence is established.
Overconfident Framing
High-certainty language unsupported by evidence or bounded uncertainty.
Risk Omission
Downside scenarios, edge cases, or failure modes excluded from reasoning.
Fluency Bias
Persuasive structure and language masking weak or incomplete logic.
Sample Output
Illustrative example of a Validity AI reasoning audit
Implicit Assumptions
The recommendation assumes stable market conditions without stating or justifying this premise.
Causal Hallucination
The output links increased automation to cost reduction without identifying mechanisms or supporting evidence.
Overconfident Framing
Conclusions are presented with high certainty despite acknowledged data gaps.
What It Is Not
Validity is not:
- A prediction engine
- A model evaluation or benchmarking tool
- A bias detection or fairness audit
- A prompt optimisation system
- A replacement for human judgement
It Makes AI Trustworthy at Scale
Teams use Validity to:
- Prevent over-trust in fluent AI outputs
- Make assumptions and reasoning explicit
- Strengthen human oversight without slowing workflows
- Create defensible audit trails for AI-assisted decisions
- Reduce downstream risk from automated reasoning
Validity does not decide what AI should do. It ensures the reasoning behind AI-driven decisions can withstand scrutiny.
Who It's For
Better Reasoning, Before You Trust the Output
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