AI Fairness & Bias Testing
Test your AI systems for bias and discrimination. Identify issues before they affect real people and damage trust.
AI systems can perpetuate and amplify existing biases in ways that are difficult to detect without structured testing. A hiring tool that disadvantages certain demographics, a lending model that produces disparate outcomes by ethnicity, a customer service system that responds differently based on names or dialects — these are real problems that real organisations face. Our AI fairness and bias testing service provides a rigorous, evidence-based assessment of your AI systems for discriminatory patterns. We test across protected characteristics — age, gender, ethnicity, disability, religion, and others relevant to your context — using both statistical analysis and scenario-based testing. We examine training data for historical biases, evaluate model outputs for differential treatment, and assess downstream impacts on affected groups. The output is a clear, quantified picture of where bias exists in your systems, how significant it is, and what practical steps you can take to address it. We also help you establish ongoing monitoring so that bias does not creep back in as models are updated and data changes.
Use Cases
What this looks like in practice
Hiring & Recruitment AI
Test CV screening, candidate scoring, and interview scheduling tools for bias across gender, ethnicity, age, disability, and educational background.
Lending & Credit Decisions
Evaluate credit scoring and lending recommendation models for disparate impact across protected characteristics, as required by financial regulators.
Customer-Facing AI
Test chatbots, recommendation engines, and personalisation systems for differential treatment based on user demographics or language patterns.
Training Data Audit
Analyse training datasets for representation gaps, historical biases, labelling inconsistencies, and proxy variables that could introduce discrimination.
Public Sector AI
Assess AI systems used in public services — benefits processing, risk scoring, resource allocation — for compliance with equality duties.
Technology
Tools we work with
How It Works
Our approach
Scope & Protected Groups
Define which systems to test, which characteristics to assess, and which fairness metrics apply
Data Analysis
Examine training and evaluation data for representation, labelling quality, and proxy variables
Model Testing
Run structured tests across demographic groups using statistical and scenario-based methods
Impact Assessment
Quantify the magnitude and significance of any identified biases with clear metrics
Remediation & Monitoring
Recommend practical fixes and establish ongoing monitoring for bias detection
Starting from
£10K
Timeline
1-2 weeks
Ready to get started?
Book a free strategy call and we'll assess whether this service is the right fit for your business.