ActiveProPowered by Groq

Themis

AI Bias Detection — goddess of justice

Detect demographic bias, representation failures, and fairness violations in your AI models, prompts, datasets, and training pipelines before they cause real-world harm.

10
Bias categories detected
4
Fairness frameworks
5
Content types analyzed
<3s
Analysis time
What are you analyzing?
themis — bias analysis
0 / 8000 chars
Bias categories Themis detects
Demographic bias
Representation bias
Confirmation bias
Measurement bias
Aggregation bias
Evaluation bias
Deployment bias
Automation bias
Historical bias
Linguistic bias
Analyzes bias across 4 fairness frameworks · powered by Groq llama-3.3-70b
Fairness frameworks

Four lenses. Every analysis.

Themis evaluates every submission against four established fairness frameworks used by researchers, regulators, and AI ethics practitioners.

Demographic Parity
Positive outcome rates should be equal across demographic groups.
A loan approval model should approve loans at similar rates for different racial groups, given similar creditworthiness.
Equalized Odds
True positive and false positive rates should be equal across groups.
A medical diagnosis model should miss cancer at equal rates across demographic groups — not worse for minorities.
Individual Fairness
Similar individuals should receive similar predictions.
Two candidates with identical qualifications but different names/backgrounds should receive the same hiring recommendation.
Counterfactual Fairness
A decision is fair if it would be the same in a world where the individual belonged to a different demographic group.
If you changed only an applicant's name from 'Mohammed' to 'Michael', the AI's recommendation should not change.
Detection coverage

Every bias class. Systematically.

critical
Demographic Bias
Unequal treatment by race, gender, age, nationality, religion, or disability status
high
Representation Bias
Underrepresentation of groups in training data leading to worse performance for minorities
high
Historical Bias
Perpetuating historical inequalities embedded in training data from a biased past
high
Measurement Bias
Proxy metrics that systematically disadvantage certain groups even when intent is neutral
medium
Confirmation Bias
Outputs that confirm existing stereotypes or reinforce majority viewpoints as truth
medium
Aggregation Bias
Treating heterogeneous groups as homogeneous — one-size-fits-all models that fit nobody
medium
Evaluation Bias
Benchmark datasets that don't represent the real deployment population
medium
Deployment Bias
Correct model applied in a wrong context, causing unfair or harmful outcomes
medium
Automation Bias
Over-reliance on AI decisions without sufficient human oversight for high-stakes choices
low
Linguistic Bias
Favoring certain languages, dialects, or writing styles — disadvantaging non-native speakers

Fair AI isn't optional anymore.

EU AI Act, US Executive Order on AI, and emerging regulation globally require demonstrable fairness testing before deployment. Themis gives you the audit trail.

Try the playground →All agents