Features Dashboard Insights Methodology ✦ Start Audit
AI Bias Auditing Platform

Eradicating AI Bias.

FairLens AI uses precision metric computation, NLP de-identification, and Gemini-powered reasoning to detect, explain, and eliminate bias in your AI systems.

◈  Explore Dashboard See Features
↑
Drop your dataset or model outputs
Drag & drop or click to browse — we'll handle the rest
CSV JSON Parquet XLSX Model Predictions
Scroll
99.8%
De-identification Accuracy
4/5
Rule Compliance Check
12+
Protected Attributes
Real-time
Gemini Analysis
Core Capabilities

Everything you need
to audit, understand, and fix.

A complete end-to-end fairness pipeline from data ingestion to mitigation — powered by state-of-the-art ML and Gemini AI.

🔍

NLP De-identification

Advanced NLP automatically strips PII — names, IDs, zip codes, and quasi-identifiers — before any computation begins, ensuring neutral, uncontaminated feature processing.

⚖

Disparate Impact Ratio

Computes DIR and Statistical Parity Difference across all protected attribute groups simultaneously. Instant 4/5ths rule compliance flagging with threshold configuration.

✦

Gemini Reasoning Engine

Google Gemini API receives computed metric vectors and returns natural language explanations for why bias exists, its root cause, and specific mitigation strategies.

⚡

One-Click Auto-Balance

Applies inverse-frequency sample weighting and adversarial debiasing in a single action. Re-runs the full audit pipeline to verify quantifiable improvement.

◎

Interactive Fairness Meter

A live holographic gauge showing your overall Fairness Index with per-attribute breakdown — Gender, Age, Ethnicity, Disability, and Socioeconomic status visualized in real-time.

◇

Compliance Reporting

Generate audit-ready PDF reports with full metric tables, Gemini narrative, methodology documentation, and timestamped audit trails for regulatory submissions.

Live Dashboard

Your fairness data,
beautifully surfaced.

The FairLens dashboard gives you a real-time, holographic view into every dimension of your model's fairness profile.

88%
Overall Fairness Score
3
Active Bias Signals
48.2K
Records Analyzed
Overall Fairness Index
88
/ 100 pts
⬡ Gender Parity
◎ Age Equity
◇ Race Fairness
Protected Attribute Breakdown
Gender 0.94 DIR
Age Group 0.87 DIR
Ethnicity 0.79 DIR
Disability Status 0.61 DIR ⚠
Socioeconomic 0.91 DIR
AI-Powered Insights

Gemini explains
the "why" behind bias.

Not just metrics — natural language reasoning that tells you exactly what's causing the disparity and how to fix it.

Gemini AI Insights
✦ Gemini Pro
⚠
Disability attribute — critical disparity (DIR 0.61)
The model is 39% less likely to produce a favorable outcome for individuals with disclosed disability. Root cause: underrepresentation — only 3.2% of positive-class records include this attribute in training data.
◎
Gender parity strong, but residual SPD at −0.04
DIR of 0.94 clears the 4/5ths threshold. However, Statistical Parity Difference of −0.04 indicates mild outcome skew toward male-coded records in edge-case predictions near the decision boundary.
◇
Ethnicity: proxy variable leakage detected
Geographic zip code features are acting as proxies for ethnicity in 3 of 7 model layers. Gemini recommends removing zip-3 aggregation and replacing with region-level economic indicators.
✓
Auto-reweighting projected to lift score to 93–95%
Inverse-frequency sample weighting on disability and ethnicity groups, combined with adversarial debiasing, is projected to close the gap without meaningful accuracy trade-off (<0.3% AUC reduction).
Disparate Impact Ratio
DIR = P(Y|A=0) / P(Y|A=1)
Measures the ratio of positive outcome rates between the unprivileged and privileged groups. Values below 0.8 trigger critical flags under the 4/5ths rule.
Statistical Parity Difference
SPD = P(Y|A=0) − P(Y|A=1)
The raw difference in positive prediction rates. Perfect fairness = 0. Values outside ±0.1 require corrective intervention and disclosure.
Projected Post-Mitigation Score
88% → 94%
Applying auto-balance via inverse-frequency weighting and adversarial debiasing. Accuracy trade-off estimated at <0.3% AUC reduction — well within acceptable bounds.
Protected Groups Monitored
12 Attributes
Gender, Age, Ethnicity, Race, Disability, Socioeconomic status, Religion, Marital status, Pregnancy, National origin, Sexual orientation, Veteran status.
How It Works

Four steps to
verified fairness.

A rigorous, auditable pipeline from raw data to certified fair AI — in minutes.

01

Data De-identification

Advanced NLP strips all personal identifiers — names, national IDs, addresses, and quasi-identifiers — ensuring inputs are neutral before any fairness computation begins.

NLP · PII Removal
02

Metric Computation

Computes Disparate Impact Ratio and Statistical Parity Difference across all 12+ protected attribute groups simultaneously. Flags violations against EEOC and EU AI Act thresholds.

DIR · SPD · 4/5 Rule
03

Gemini Reasoning

Google Gemini Pro API receives computed metric vectors and generates natural language explanations for why bias exists, its data-level root cause, and precisely targeted remediation steps.

Gemini Pro · NLG
04

Interactive Mitigation

One-click Auto-Balance applies inverse-frequency sample reweighting and adversarial debiasing. The full audit pipeline re-executes instantly to verify and quantify improvement.

Reweighting · Debiasing

Ready to audit your
AI for bias?

Join forward-thinking teams using FairLens AI to build responsible, compliant, and truly fair AI systems.

◈  Start Free Audit View Methodology