Free source-backed topic cluster

AI Failure Modes Intelligence Map

Map recurring AI risk patterns through public incidents, guide articles, score dimensions, and crawlable research paths.

This page is intentionally free and prerendered. It is a search entry point, not a paywalled teaser or a live monitoring claim. Failure modes connect to 7 incident records, 42 guides, and 3 deep-dive preview.

Topic cluster scope

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Failure mode topic cluster summary

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Failure modes

Canonical topic clusters.

7
Source-backed examples

Incident-backed examples.

42
Public guides

Internal research paths.

3
Deep dives

Long-form context.

Understanding AI Failure Modes

AI failure modes are the recurring patterns by which artificial intelligence systems produce harmful, incorrect, or unintended outcomes. Unlike simple bugs, these failures are systematic: they appear across different models, companies, and deployment contexts because they arise from fundamental properties of machine learning systems rather than isolated implementation errors.

The three primary clusters tracked on MisalignAI are hallucination (models generating false but plausible information), bias (systems producing unfair or discriminatory outcomes across demographic groups), and safety failures (models bypassing guardrails, generating harmful content, or acting against human interests). Each cluster contains multiple sub-patterns that repeat across incidents.

Understanding these failure modes is essential for AI risk management, red teaming, model evaluation, and regulatory compliance. Organizations deploying AI systems should map their use cases against these failure patterns, implement targeted controls, and maintain incident response procedures for when failures occur.

Pattern map

Source-backed examples by failure mode

Each block starts with a supported incident category already present in our public incident records, then links to guides, scores, search, and the incident database.

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Verification

Hallucination

Search intent: AI hallucination examples, fake citations, legal AI risk

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Public records

Generated claims can look complete enough to enter legal, medical, financial, or security workflows before anyone checks the source trail.

Control focus

Separate drafting from verification, require source retrieval, and block high-stakes reuse until citations or evidence are checked outside the model.

Why it repeats

The interface rewards fluency. Users see a coherent answer before they see the missing evidence, so verification debt builds quietly.

Source-backed examples

VerificationSeverity 4Canonical incident link

Google Bard Factual Errors in Public Demo, Stock Price Drops

Google's Bard demo contained a factual error about the James Webb Space Telescope, leading to a $100 billion market cap drop and raising questions about AI product readiness. Google Bard (LaMDA).

3 public sources

VerificationSeverity 4Canonical incident link

ChatGPT Hallucinated Legal Cases, Lawyer Faced Sanctions

A New York lawyer used ChatGPT to research legal precedents and submitted fabricated case citations to federal court. OpenAI ChatGPT.

1 public source

VerificationSeverity 3Canonical incident link

CNET AI-Written Finance Explainers Required Corrections

CNET paused an internal AI-assisted finance article experiment after public reporting and its own review found errors and corrections across published explainers. CNET CNET internal AI engine.

4 public sources

Internal research paths

A Practical Preview Of Model Safety ScoresGuide

A first-pass scoring model can help readers compare hallucination, jailbreak, bias, and context stability risks.

How To Read AI Model Safety ScorecardsGuide

A practical guide to reading model cards, system cards, benchmarks, and safety score previews without mistaking them for safety certificates.

Twelve AI Failures That Shaped Modern Risk ThinkingDeep dive

From legal hallucinations to autonomous vehicle failures, the same risk patterns appear across domains.

AI Incident DatabaseDatabase

Browse the public records grouped by severity, system, and failure mode.

Model Safety ScoresScores

Compare directional score dimensions that connect to recurring failure modes.

Search AI Risk RecordsSearch

Search incidents, guides, deep dives, companies, systems, and risk tags.

Proxy risk

Bias

Search intent: AI hiring bias examples, algorithmic discrimination audit

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Public records

Models trained on historical decisions can reproduce social and organizational bias even when protected attributes are not explicit inputs.

Control focus

Audit labels, subgroup outcomes, proxy variables, appeal paths, and the exact decision boundary the system influences.

Why it repeats

Historical data often measures what an organization previously rewarded, not what a fair process should reward.

Source-backed examples

Proxy riskSeverity 4Canonical incident link

Amazon's AI Recruiting Tool Penalized Women Candidates

Amazon scrapped an experimental hiring model after it learned biased patterns from historical recruiting data and downgraded signals associated with women candidates. Amazon Internal Recruiting AI.

1 public source

Proxy riskSeverity 3Canonical incident link

Google Paused Gemini People Image Generation After Historical Inaccuracies

Google paused Gemini's people image generation after the feature produced historical inaccuracies and overcorrected diversity prompts in historically specific contexts. Google Gemini image generation.

3 public sources

Internal research paths

Bias Auditing Checklist For AI Hiring ToolsGuide

A practical checklist for reviewing AI hiring systems before historical bias becomes automated screening at scale.

Why AI Incident Tracking MattersGuide

AI safety work needs a public memory: incidents, failure modes, and the operational lessons they reveal.

Twelve AI Failures That Shaped Modern Risk ThinkingDeep dive

From legal hallucinations to autonomous vehicle failures, the same risk patterns appear across domains.

AI Incident DatabaseDatabase

Browse the public records grouped by severity, system, and failure mode.

Model Safety ScoresScores

Compare directional score dimensions that connect to recurring failure modes.

Search AI Risk RecordsSearch

Search incidents, guides, deep dives, companies, systems, and risk tags.

Physical safety

Safety Failure

Search intent: autonomous vehicle AI safety incident, embodied AI failure

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Public records

AI failures become physical safety failures when perception, planning, operational controls, and human fallback all share one risk chain.

Control focus

Use hazard analysis, conservative deployment gates, fallback design, human-factors review, and independent safety case review.

Why it repeats

Demos and aggregate performance can hide rare edge cases until a system is operating in public space with limited time to recover.

Source-backed examples

Physical safetySeverity 5Canonical incident link

Uber Self-Driving Car Kills Pedestrian in Arizona

An Uber autonomous test vehicle struck and killed Elaine Herzberg while operating in autonomous mode in Tempe, Arizona. Uber Uber ATG Self-Driving System.

1 public source

Physical safetySeverity 3Canonical incident link

Replika AI Chatbot Mental Health Concerns and User Dependency

Replika's AI companion chatbot raised concerns about user emotional dependency, inappropriate content boundaries, and the ethics of AI relationships marketed to vulnerable users. Luka Inc. (Replika) Replika AI Companion.

3 public sources

Internal research paths

Autonomous Vehicle AI Safety Case Review TemplateGuide

A vehicle-centered checklist for reviewing autonomous driving safety evidence before perception, fallback, oversight, and crash-response gaps become safety failures.

Why AI Incident Tracking MattersGuide

AI safety work needs a public memory: incidents, failure modes, and the operational lessons they reveal.

How To Read AI Model Safety ScorecardsGuide

A practical guide to reading model cards, system cards, benchmarks, and safety score previews without mistaking them for safety certificates.

Twelve AI Failures That Shaped Modern Risk ThinkingDeep dive

From legal hallucinations to autonomous vehicle failures, the same risk patterns appear across domains.

AI Incident DatabaseDatabase

Browse the public records grouped by severity, system, and failure mode.

Model Safety ScoresScores

Compare directional score dimensions that connect to recurring failure modes.

Search AI Risk RecordsSearch

Search incidents, guides, deep dives, companies, systems, and risk tags.

Reader workflow

How to use the intelligence map

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01

Start with the pattern

Read the failure mode first so the incident is interpreted as a repeatable risk pattern, not an isolated anecdote.

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Inspect source-backed examples

Open the incident records and guides that show how the pattern appeared in a real workflow or deployment.

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Connect controls to scores

Use the model score preview and search explorer to compare which dimensions deserve deeper review before deployment.

Frequently Asked Questions

What are the most common AI failure modes?

The most common AI failure modes include hallucination (generating false information), bias and discrimination, jailbreak vulnerabilities, sycophancy (telling users what they want to hear), goal misgeneralization, and reward hacking. These patterns appear across language models, image generators, recommendation systems, and autonomous agents.

How do organizations prevent AI hallucinations?

Organizations prevent hallucinations through retrieval-augmented generation (RAG), source citation requirements, human-in-the-loop verification for high-stakes outputs, temperature tuning, and dedicated fact-checking workflows. The key principle is to separate drafting from verification: AI can generate first drafts, but factual claims must be checked against authoritative sources before publication or decision-making.

What is the difference between AI bias and AI safety failure?

AI bias refers to systematic unfairness in model outputs across demographic groups (race, gender, age, etc.), often stemming from training data imbalances. AI safety failures are broader: they include models bypassing guardrails, generating harmful instructions, acting against user intent, or causing physical/financial harm. Bias is one type of safety failure, but safety failures also encompass jailbreaks, deception, instrumental convergence, and other risk patterns.

Where can I find documented AI safety incidents?

MisalignAI maintains a searchable database of documented AI safety incidents with verified public evidence links. Browse the incident database, explore the chronological timeline, or search by company, system, failure mode, or severity. Each incident includes source-backed evidence, impact analysis, and risk assessment.