The Impossibility Theorem and Why No Model Can Satisfy Every Fairness Metric at Once
When group base rates differ, no algorithm satisfies calibration, equal error rates, and demographic parity at once. Learn the math behind fairness trade-offs.
Safety & Red Teaming
AI safety and red teaming is the practice of stress-testing models for harmful behaviors — adversarial prompting, toxicity evaluation, and assessment methods that find failures before deployment.
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What topics does this domain cover?
4 topicsEach topic below is a key concept in this domain. Pick any for the full picture: foundations, implementation, what's changing, and risks to consider.
Bias and Fairness Metrics →
Bias and fairness metrics are quantitative measures used to detect, quantify, and report systematic disparities in …
6 articles
Hallucination →
Hallucination is what happens when a large language model generates text that sounds confident and coherent but is …
6 articles
Red Teaming for AI →
Red teaming for AI is adversarial testing where humans or automated systems deliberately probe an AI model to find …
7 articles
Toxicity and Safety Evaluation →
Toxicity and safety evaluation encompasses the metrics, datasets, and frameworks used to measure whether AI systems …
6 articles
Four perspectives on this domain
MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Updated Mar 28, 2026
Concepts covered
What Are Bias and Fairness Metrics and How They Detect Discrimination in ML Predictions
Fairness metrics test whether ML models discriminate by group. Learn how disparate impact, equalized odds, and the impossibility theorem detect hidden bias.
HarmBench, ToxiGen, and MLCommons Taxonomy: The Datasets and Standards Behind AI Safety Testing
HarmBench, ToxiGen, and MLCommons AILuminate define how AI safety is measured. Learn the datasets, classifiers, and taxonomies behind modern toxicity evaluation.
What Is Toxicity and Safety Evaluation and How Guard Models Score Harmful AI Outputs
Toxicity and safety evaluation scores AI outputs for harm using classifiers and red teaming. Learn how guard models detect toxic content and where they fail.
Demographic Parity vs. Equalized Odds vs. Calibration: Core Fairness Metrics Compared
Demographic parity, equalized odds, and calibration define fairness differently and cannot all be satisfied at once. Learn what that trade-off means.
False Positives in Toxicity Detection: Dialect Bias, Bypasses
Toxicity classifiers over-flag minority dialects and miss adversarial attacks. Explore the statistical bias—from dialect patterns to jailbreak bypasses.
OWASP LLM Top 10, MITRE ATLAS, and the Frameworks That Structure AI Red Teaming
OWASP LLM Top 10 and MITRE ATLAS give red teams structured attack categories. Learn how these frameworks turn AI security testing from guesswork into coverage.
Red Teaming for AI: Adversarial Testing Exposes Failures
Red teaming uses adversarial testing to reveal AI vulnerabilities. Discover what it catches, mechanics, and why it outperforms traditional security approaches.
Intrinsic vs. Extrinsic, Closed vs. Open Domain: The Taxonomy and Prerequisites of LLM Hallucination
LLM hallucination isn't one problem — it's four. Learn the intrinsic vs. extrinsic taxonomy, the domain split, and the prerequisites that reframe the field.
What Is AI Hallucination and How Statistical Next-Token Prediction Creates Confident Falsehoods
AI hallucinations aren't bugs — they emerge from how next-token prediction works. Learn why LLMs produce confident falsehoods and what limits current fixes.
Why Zero-Hallucination LLMs Remain Impossible: Autoregressive Limits and Benchmark Ceilings in 2026
LLM hallucination is mathematically inevitable. Explore the autoregressive limits, benchmark ceilings, and why zero-hallucination LLMs remain impossible in 2026.
Automated Red Teaming Misses What Humans Catch: Coverage Gaps
Automated red teaming outperforms human testing but misses critical failures. Coverage gaps explain why automated testing remains fundamentally incomplete.
