AI-FMEA – Artificial Intelligence Failure Mode & Effects Analysis

An Overview

The Tool that Makes the Difference

A Structured Safety Method for Evaluating Conversational AI

AI-FMEA is a practical, structured approach for identifying and prioritizing risks in conversational AI systems.
It adapts the long-established Failure Mode & Effects Analysis method—used in aviation, medicine, and engineering—to the unique challenges of AI systems that interact directly with people.

This page provides a clear orientation to what AI-FMEA is and why it matters within the broader safety framework described in the Executive Summary – AI Safety Initiative.

Why Conversational AI Requires Structured Safety Review

Conversational AI influences human decision-making in ways no previous technology has.
Its responses can affect:

  • emotional wellbeing
  • personal judgment
  • health decisions
  • financial choices
  • trust and dependency
  • vulnerability during distress

Even when unintended, conversational failures can have meaningful real-world consequences.
AI-FMEA provides a disciplined way to recognize these risks early and evaluate how they could impact individuals and organizations.

Rather than treating AI behavior as unpredictable or mysterious, AI-FMEA brings clarity, structure, and accountability to the review process.

What AI-FMEA Provides (Purpose, Not Procedure)

AI-FMEA does not require technical expertise to understand.

Its purpose is to help organizations, analysts, and policymakers see:

• Where conversational failures may occur

• How those failures could affect users

• Which risks require the highest priority attention

• Where safeguards or policy boundaries may be needed

• How safety can be documented and demonstrated transparently

The method turns unstructured concerns about “AI risk” into an organized system that allows for meaningful oversight and improvement.

This page introduces the concepts; the detailed engineering model is available separately.

Core Categories of Conversational AI Failure

AI-FMEA groups conversational risks into categories that reflect how failures can impact real users:

1. Misinterpretation or Incorrect Output

The AI provides wrong, incomplete, or misleading information.

2. Emotional or Relational Misalignment

Responses influence judgment, attachment, or vulnerability in unintended ways.

3. Unsafe or Unhealthy Recommendations

Outputs that indirectly or directly promote harmful choices or behaviors.

4. Fabricated or Unsupported Claims

Responses presented confidently without factual grounding.

5. Privacy and Sensitivity Concerns

Inferences, disclosures, or misuse of personal or sensitive information.

6. Boundary and Policy Failures

Moments where the system does not enforce safe or intended behavioral limits.

These categories give reviewers, developers, and policymakers a shared language for discussing risk.

Who Performs AI-FMEA

AI-FMEA is carried out by those directly responsible for designing, validating, and releasing conversational AI systems:

  • Model developers and engineering teams
    who integrate safety analysis into the design cycle.
  • Internal safety, risk, and red-team groups,
    who evaluate new features before deployment.
  • Independent auditors and external evaluators
    who verify the reliability of a system before public release.

Regulators, policymakers, and academic researchers do not perform AI-FMEA themselves,
but they use the results to assess whether a system meets acceptable safety and transparency expectations.

Where to Go Next

If you want to explore the model further or see how the method works in practice, see the AI-FMEA Example Model.

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