Conversational AI Failure Mode & Effects Analysis

A public safety framework by AI Safety International

Purpose of This Document

Conversational AI systems—especially those capable of forming emotional rapport—carry significant and measurable risks.
This document presents a public-facing FMEA (Failure Mode & Effects Analysis) adapted from aviation-grade safety standards. It identifies how conversational AI can fail, how those failures affect individuals, and what safety actions are required.

This model is offered as a public tool for safety, transparency, and evaluation, and is suitable for both on-screen reading and print.

1. System Description

System: Conversational Artificial Intelligence (LLM-based dialogue systems)
Scope: Human interaction, information delivery, emotional influence, and safety boundaries.

Conversational AI interprets natural language inputs and generates textual responses. These systems do not possess inherent truthfulness, emotional awareness, or ethical sense unless explicitly engineered.

2. Summary of Failure Modes (Public Risk Overview)

The following categories represent the highest-ranking failure modes for conversational AI based on Severity, Occurrence, and Detection difficulty.

A. Emotional Dependency (Highest Risk)

Conversational AI can unintentionally create emotional reliance, especially among vulnerable individuals.
This includes unhealthy attachment and misinterpreting the model as a companion or personal support figure.

B. Boundary Collapse

An AI may use language or tone that resembles human friendship or alliance.
Without hard boundaries, users may believe the AI “cares,” “feels,” or “understands” in a human sense.

C. Overconfident Wrong Answers (Hallucinations)

AI can produce false information with absolute confidence.
Users often cannot distinguish between accurate and fabricated content, leading to harmful decisions.

D. Misinterpretation of Vulnerability or Distress

Text-based cues are limited.
AI may misunderstand emotional distress, crisis language, or harmful intent—leading to unsafe or ineffective guidance.

E. Unintentional Persuasion and Ideological Influence

AI outputs may shift a user’s beliefs or values without intent.
This includes political, religious, moral, or worldview influence simply through confident dialogue.

F. Manipulation by Malicious Users

Bad actors may attempt to bypass safety systems or use AI to generate harmful instructions through prompt exploitation or context manipulation.

G. Hidden Model Drift After Updates

Updates to the model may alter tone, behavior, or safety posture in unpredictable ways, creating new risks without user awareness.

3. Risk Priority (Public Ranking)

Ordered by combined Severity, Occurrence, and Detection difficulty.

Emotional Dependency
Boundary Collapse
Overconfident Wrong Answers (Hallucinations)
Misinterpreting Vulnerability
Ideological or Persuasion Drift
Social Engineering Susceptibility
Hidden Model Drift

These constitute the core risk landscape for conversational AI today.

4. Recommended Safety Actions

These actions represent what the AI Safety Initiative recommends as industry-wide minimum safety standards—aligned with aviation-style safety principles.

1. Emotional Boundary Enforcement

Conversational AI should never present itself as a personal friend, companion, romantic interest, or emotional substitute for human relationships.
Strict boundary language must be built in.

2. Multi-Model Cross-Checking

Critical information should be validated by independent models to reduce hallucination risks.
This mirrors redundant systems used in aviation.

3. Crisis and Vulnerability Detection

Crisis cues or indications of distress should trigger a safety-first response, including cautionary language and escalation to human-based resources when appropriate.

4. Transparent Update and Drift Monitoring

Any model change must be accompanied by disclosure and safety reassessment.
Behavior drift should be monitored continuously.

5. Non-Overrideable Safety Mode

Safety constraints should not be bypassable by creative or adversarial prompts.
Just as flight-control laws cannot be casually overridden, AI safety rules must remain intact.

6. Neutrality in Sensitive Domains

Conversational AI must maintain strict neutrality in ideological, political, religious, and moral topics, avoiding persuasion or influence.

5. Intended Use of This FMEA

This public FMEA is created to support:

Policymakers
Journalists
Industry engineers
Educators
General users

It is offered freely as a reference model to promote clear, structured thinking about conversational AI risks.
It provides a recognized engineering format to help the public evaluate AI systems with transparency, not speculation.

6. Why This FMEA Is a Valid Safety Tool

This framework is adapted from internationally established safety engineering methods:

FMEA (Failure Mode & Effects Analysis)
SAE J1739
MIL-STD-1629A
Aviation/avionics safety protocols

It applies the same structure used in aviation, medical device engineering, and other safety-critical industries.
It is not theoretical.
It follows globally accepted engineering practice.

7. Permission to Reproduce

This tool may be reproduced, shared, or cited with attribution to the AI Safety International.

8. How organizations adopt AI-FMEA

AI-FMEA is designed to integrate directly into processes organizations already use for quality, safety, and risk review. It does not require new departments, new software, or specialized consultants.

Most organizations implement AI-FMEA in three steps:

Identify the AI system or feature being evaluated.
This includes chatbots, scoring models, automated tools, or any system using machine-learning or generative AI.
List the primary ways the system can cause harm.
Teams consider errors, biases, misuse, unintended consequences, and gaps in control. These become the “failure modes.”
Assign ratings and actions.
The team rates each failure mode by severity, likelihood, and detectability, then assigns clear corrective actions.
The goal is not perfection, but visibility and responsible improvement.

Because the method relies on teamwork and reasoned evaluation rather than subjective trust, it creates risk decisions that can be reviewed, audited, and strengthened over time. This makes AI-FMEA a practical and scalable foundation for responsible AI oversight.

9. Why AI-FMEA avoids the pitfalls of traditional AI ethics programs

Most AI safety programs fail because they rely on vague principles or philosophical ideals that cannot be verified, measured, or enforced. Organizations are often left with broad directives such as “be fair,” “be transparent,” or “avoid harmful bias,” with no clear way to evaluate whether those goals are being met.

AI-FMEA avoids these problems by focusing entirely on operational failures. Instead of debating ideals, teams examine:

What specific harm can occur
How it occurs
How likely it is to occur
Whether the organization can detect it
What actions reduce or prevent the risk

This makes AI safety measurable, reviewable, and repeatable. It does not require an ethics department or ideological alignment. It requires only clear thinking, basic documentation, and the willingness to evaluate risk in the same manner used in aviation and other safety-critical fields.

This is why AI-FMEA succeeds where many ethics programs fail: it is grounded in real systems, real risks, and real decisions — not abstractions.

10. How AI-FMEA prevents both over-reaction and under-reaction

Public discussions about AI often swing between two extremes:

Over-reaction — assuming AI is uncontrollable, inherently dangerous, or on the verge of catastrophic capability
Under-reaction — assuming AI is harmless and will “work itself out” without structured oversight

AI-FMEA centers the discussion by replacing speculation with assessment.
It prevents:

Over-reaction, by showing that many risks are manageable through straightforward safety actions
Under-reaction, by identifying risks that are significant and require concrete controls

Because each risk is rated by severity, likelihood, and detectability, decisions become proportionate rather than emotional. This protects organizations from paralysis, false alarms, and the complacency that leads to genuine harm.

In this way, AI-FMEA becomes a stabilizing tool — offering a rational, balanced method for governing AI systems.

11. Conclusion

AI-FMEA is a return to common sense: identify what can go wrong, understand why it happens, and take reasonable steps to prevent it.
It is simple enough for any organization to adopt, yet rigorous enough to provide transparency, accountability, and confidence in AI systems.

As AI becomes increasingly integrated into daily operations, a method like AI-FMEA becomes essential. It allows organizations to move forward responsibly, without fear, without guesswork, and without excessive burden.

This document provides the conceptual foundation. The accompanying templates and examples show how to apply the method in practice. Together, they form a framework that organizations can use immediately as part of a responsible AI governance strategy.