Decision & Recommendation System Testing
Evaluate the ranking, matching, scoring and prioritisation systems that influence what people see, receive or are allowed to access.
The Challenge
Ranking, matching, scoring and recommendation systems quietly decide what people see, what they are offered and what they can access. Small changes in features or data can shift outcomes for entire groups.
These systems learn from historical data and user feedback, so they can encode past bias, drift as populations change, and reinforce their own decisions through feedback loops — often without anyone noticing.
We test decision and recommendation systems across retail recommendations, customer routing, risk scoring, workflow prioritisation and recruitment matching, so you understand how they behave and where they fail.
What Can Go Wrong
What we evaluate in decision, ranking and recommendation systems
Recommendation Quality
Recommendations are irrelevant, low-value or fail to serve the user’s actual intent.
Ranking Consistency
Rankings shift unpredictably for equivalent inputs or minor changes.
Proxy Features
Seemingly neutral features act as proxies for protected or sensitive attributes.
Historical Bias
Models trained on past outcomes reproduce and amplify historical disparities.
Feedback Loops
Biased outputs shape future data, reinforcing the system’s own decisions over time.
Outcome Disparities
Groups receive systematically different outcomes without a justified reason.
Drift
Behaviour degrades as populations, inputs or upstream data change.
Explainability & Robustness
Decisions are hard to explain, or break under changing populations and edge-case inputs.
Our Testing Methodology
A rigorous approach to evaluating decision and recommendation systems
Map the System
We document the features, data, scoring logic and decisions the system drives.
Counterfactual & Scenario Testing
We test how changes in inputs and attributes affect rankings, matches and scores.
Outcome Analysis
We analyse outcomes across groups and over time to detect disparities and drift.
Robustness & Explainability
We probe stability under changing inputs and assess how decisions can be explained.
What You Get
Behaviour & Fairness Report
Evidence of quality, consistency and disparity issues with severity and impact.
Proxy & Drift Analysis
Identification of proxy features and monitoring points for drift over time.
Evaluation Recommendations
Practical, prioritised steps to improve reliability, consistency and fairness.
Evaluate Your Decision & Recommendation Systems
Understand how your ranking, matching and scoring systems behave — and where they fail.
Discuss a Partner Pilot