Is your AI treating everyone fairly?
ETHYX AI helps teams inspect datasets and automated decisions for hidden unfairness before they affect real people.
FAIRNESS RISK SCORE
67
500
Rows Analyzed
4
Fairness Checks
3
Risks Detected
1
Mitigation Plan
AI bias is invisible — until it harms someone
ETHYX flags hidden bias before your model ever reaches production.
Historical Discrimination
AI models trained on biased data repeat and amplify past inequalities at scale.
High-Stakes Decisions
Hiring, lending, and insurance algorithms need fairness checks before deployment.
Healthcare & Safety
Biased diagnostic models can deny care to entire populations without anyone noticing.
Everything you need to audit AI fairness
DATASET BIAS SCAN
Upload any tabular dataset and detect statistical imbalances across protected attributes like gender, race, and age.
FAIRNESS METRICS
Compute Disparate Impact, Statistical Parity, Equal Opportunity, and Calibration with one click.
GROUP IMPACT COMPARISON
Visualize how model outcomes differ across demographic subgroups with interactive charts.
EXPLAINABLE DECISIONS
AI-powered plain-English explanations of why each metric passed or failed and what it means.
MITIGATION STRATEGY
Receive actionable recommendations to rebalance training data or adjust model thresholds.
AUDIT REPORT EXPORT
Generate a comprehensive PDF fairness report ready for compliance review and stakeholder sharing.
Four Steps to Fair AI
Upload Dataset
Upload your CSV or connect to your data warehouse. ETHYX supports datasets up to 500k rows.
Map Attributes
Select your target variable and protected attributes like gender, race, or age group.
Run Fairness Audit
ETHYX computes five key fairness metrics and generates AI-powered explanations in seconds.
Review & Mitigate
Explore results, apply mitigation strategies, and export a compliance-ready audit report.
Five Pillars of Fair AI
ETHYX computes these industry-standard metrics on every audit to give you a complete picture of model fairness.
Disparate Impact
P(Y=1|D=0) / P(Y=1|D=1)
Threshold: ≥ 0.80
Ratio of positive outcome rates between unprivileged and privileged groups. Below 0.80 signals adverse impact.
Statistical Parity
P(Ŷ=1|D=0) - P(Ŷ=1|D=1)
Threshold: ± 0.05
Difference in selection rates. Measures whether the model selects at equal rates regardless of group membership.
Equal Opportunity
TPR(D=0) - TPR(D=1)
Threshold: ± 0.05
Difference in true positive rates. Ensures qualified individuals have equal chance of positive prediction.
Average Odds
½(ΔFPR + ΔTPR)
Threshold: ± 0.05
Average of FPR and TPR differences. A balanced measure of equalized odds across groups.
Calibration
E[Y|Ŷ=p, D=d] ≈ p
Threshold: > 0.90
Whether predicted probabilities match observed outcomes equally for each group. Key for risk scoring.
See a Real Audit Report
| Metric | Value | Threshold | Result |
|---|---|---|---|
| Disparate Impact | 0.74 | 0.80 | FAIL |
| Statistical Parity | -0.24 | ±0.05 | FAIL |
| Equal Opportunity | -0.18 | ±0.05 | FAIL |
| Calibration | 0.92 | >0.90 | PASS |
Build fairer AI before launch.
Join the teams using ETHYX AI to ship responsible machine learning models with confidence.