Understanding how AI systems fail in real-world conditions
A curated gallery of common AI system failure modes, focused on why failures occur at the system level, not on model mistakes or implementation bugs.
This repository captures production-facing failure patterns that often emerge only after AI systems are deployed and used at scale.
Author — Aditi Khare
Writing on AI research, product thinking, and system architecture
🌐 Website: aditikhare.com
🔗 GitHub Repository: AI Failure Modes Gallery
🤗 Live Demo: View on Hugging Face
💼 LinkedIn: Aditi Khare
⭐ If this repository helps you think more clearly about AI system failures, consider starring it.
Most AI failures are not sudden.
They emerge gradually from:
- unexamined assumptions
- weak evaluation signals
- architectural blind spots
- unclear trust boundaries
These failures are often predictable in hindsight—but rarely documented systematically.
This gallery exists to make those patterns explicit.
This repository provides:
- A taxonomy of AI system failure modes
- System-level explanations for why failures occur
- A shared vocabulary for discussing failure risk early
It is intentionally:
- Descriptive, not prescriptive
- Pattern-focused, not solution-driven
- System-oriented, not model-specific
This is not:
- A debugging guide
- A postmortem collection
- A reliability playbook
- A list of fixes
No remediation steps are provided.
Only failure awareness is surfaced.
Use this gallery to:
- Anticipate failure risks early
- Stress-test system assumptions
- Frame post-incident discussions
- Improve evaluation and design thinking
It is most useful before production issues arise.
Failure Mode: Silent performance degradation
Where It Appears: Long-running AI systems
Why It Happens:
- Distribution shift
- Feedback loops
- Inadequate observability
Why It’s Dangerous:
Failures accumulate without triggering alerts.
No fixes are suggested.
Only system awareness is built.
failure_modes/
├── taxonomy.md
├── system_drift.md
├── feedback_loops.md
├── observability_gaps.md
examples/
├── real_world_patterns.md
diagrams/
├── failure_flows.md
🧠 Final Note
AI system failures are rarely caused by a single mistake. They emerge from interactions between models, systems, people, and context. This gallery exists to make those interactions visible—before failures occur.
⭐ If this repository helps you think more clearly about AI system failures, consider starring it.
© 2026 Aditi Khare. All rights reserved.