Comprehensive Analytics

BiasClean v3.0 gives you a clear and simple way to understand fairness in your dataset. It turns complex statistics into easy-to-read charts, tables and explanations. There is no jargon, and you do not need any background in data science.

1. What “Comprehensive Analytics” Means

The analytics system checks your dataset from different angles and shows:

  • Where groups are under-represented

  • Where groups appear too often

  • How serious each imbalance is

  • How much it affects fairness in your chosen domain

  • How the system improves fairness after rebalancing

Everything is explained in clean, everyday language so anyone can follow it.

2. What You See in the Analytics Report

When you upload your data, the system creates:

A. Group Representation Table

Shows how many people belong to each demographic group (e.g., age, gender, ethnicity) and how this compares to what is expected in the real world.

B. Traffic-Light Colours

BiasClean uses simple colours:

  • Red – serious unfairness

  • Amber – medium unfairness

  • Green – balanced and healthy

The colours help you understand the problem at a glance.

C. Domain Awareness

The system adjusts results based on the domain you select.
For example:

  • Justice: Ethnicity and SES matter more

  • Health: Disability, Gender and Ethnicity matter more

  • Finance: SES and Region matter more

This makes the analytics smarter and more realistic.

D. Weighted Fairness Score

This is a number that shows how fair or unfair the dataset is overall, based on the seven UK fairness features and their domain weights.
A lower score means more unfairness.
A higher score means better balance.

E. Visual Charts

BiasClean shows clean charts such as:

  • Before vs After fairness

  • Group balance charts

  • Domain-weight impact charts

  • Data-retention charts

They help you “see” what the system is doing.

3. Before and After Comparison

One of the most helpful parts of the analytics page is the “Before vs After” section.

It shows:

  • How the dataset looked before rebalancing

  • How it looks after SMOTE and controlled undersampling

  • How fairness improved

  • How much data was kept

This helps users understand exactly what changed.

4. Why These Analytics Matter

Without fairness analytics:

  • A dataset may look fine at first glance

  • Hidden biases stay unnoticed

  • AI models may treat certain groups unfairly

  • Decisions can become unreliable or discriminatory

With BiasClean analytics:

  • Problems are visible

  • Improvements are measurable

  • Reports are easy to read and share

  • Fairness becomes transparent and explainable

These analytics are suitable for:

  • Students

  • Teachers

  • Researchers

  • Businesses

  • Regulators

  • Anyone who wants a fair dataset

5. Designed for Non-Technical Users

The system never expects you to know statistics or coding.
It explains results using simple words, such as:

  • “over-represented”

  • “under-represented”

  • “balanced”

  • “fairer than before”

It also provides short explanations next to charts so that anyone can understand the results immediately.

6. What You Can Download

After the analytics run, you can download:

  • The full analytics report (PDF/HTML)

  • The rebalanced dataset (CSV)

  • The summary of fairness scores

  • The visual charts of before/after results

This makes sharing with colleagues or regulators very easy.

 

📊  How to Read the Analytics Report

The analytics report in BiasClean v2.0 is designed to be simple, clear and beginner-friendly. You do not need any maths or coding knowledge. This guide explains how to read each part of the report step by step.

1. Start With the Overview Box

At the top of the report you will see a short summary showing:

  • Fairness Score (Before) – how fair the dataset was at the start

  • Fairness Score (After) – how fair it is after rebalancing

  • Data Retained – what percentage of the original data is kept

  • Rebalancing Mode – Standard or Industry Mode

This gives you a “big picture” view of how much improvement happened.

2. Look at the Group Representation Table

This table shows how many records belong to each group, such as:

  • Age bands

  • Gender

  • Ethnic groups

  • Regions

  • Disability status

  • Migration status

Each group has two values:

  • Expected % – based on real UK population patterns

  • Your Data % – what your dataset actually contains

If a group’s Your Data % is far from the Expected %, it means the dataset might be unfair.

3. Use the Traffic-Light Colours

Colours make it easy to spot problems:

  • Green – Good balance

  • Amber – Medium imbalance

  • Red – Strong imbalance

If you see many amber or red areas, it means the dataset needs attention.

4. Check the Domain Weights

When you pick a domain (for example, Justice or Health), the system uses different importance levels for each feature. This is called a domain-specific weight.

For example:

  • In Health, Disability and Gender matter a lot more.

  • In Finance, SES and Region matter the most.

These weights help the system decide which imbalances are the most serious.

5. Read the Disparity Explanations

Next to each major finding, you will see a short explanation written in plain English.
This answers questions like:

  • Which group is under-represented?

  • How much is the gap?

  • Why does it matter in this domain?

  • Is the difference large enough to cause unfair outcomes?

These explanations connect the numbers to real-world meaning.

6. Compare “Before” and “After” Charts

The report includes visual charts that show:

  • How groups were distributed before rebalancing

  • How they look after SMOTE and controlled adjustments

  • How the fairness score improved

  • How much data was added or removed

These charts help you see whether the dataset is now more balanced.

7. Look at the “Production-Ready Status”

At the end of the report, the system shows a final message:

  • Production-Ready

  • Needs Review

  • Or Diagnostic Only

This tells you whether the new dataset is suitable for real use in:

  • AI models

  • School or university projects

  • Business decisions

  • Research

  • Compliance checks

8. Download Your Files

When you finish reading the report, you can download:

  • The rebalanced dataset (CSV)

  • The full analytics report (PDF/HTML)

  • Visual charts

  • A summary sheet for sharing

These files are easy to give to teachers, colleagues, regulators or your technical team.