Fairness Auditing Platform

Detect hidden bias
before it harms people.

AI systems now make life-changing decisions — loans, jobs, medical care. FairSight inspects your datasets and models for discrimination so you can fix it before deployment.

1

Upload Data

2

Configure

3

Run Audit

4

View Results

What domain are you auditing?

💰 Loan / Credit 💼 Hiring / Recruitment 🏥 Medical / Diagnosis ⚖ Criminal Justice 🏠 Housing 🎓 Education ⬡ Other

Built-in sample

Loan Approval Dataset

1,000 applicants · deliberate gender & race bias · ready to audit instantly

No upload needed

Your own data

Upload any CSV dataset

Loan, hiring, medical, housing — any domain with a binary outcome column

📂

Drop CSV or click to browse

Any tabular dataset · UTF-8 · binary outcome required

Audit Overview

Bias Risk Score

–

out of 100

–

Positive outcome rate by demographic group

Blue = most advantaged · Red = most disadvantaged

Per-attribute severity

Attribute	Data Bias	Model Bias	Disparate Impact	Parity Gap	Most Disadvantaged

Dataset Bias

Discrimination found in the raw training data — before any model is involved. Historical bias in data directly trains AI systems to discriminate.

Model Bias

The model's actual predictions audited across demographic groups. A model that amplifies historical bias causes real harm to real people.

Bias Mitigation

Three evidence-based strategies are applied server-side. Compare the results to choose what to deploy.

Click Run Mitigation to apply all three strategies on your server (Step 4 of the pipeline).