Research
Interpretability & Explainability
Understanding how systems reach their conclusions.
Purpose & context
Understanding how systems reach their conclusions.
When organisations deploy systems that make consequential decisions, they need to understand and explain how those systems reach their conclusions. We develop interpretability tools, conduct explainability audits, and create documentation frameworks that make automated decisions transparent to affected communities and regulatory bodies.
A good fit when
- Decision-makers cannot explain how a model is used.
- Users need understandable reasons and review routes.
- Technical explanations need validation and documentation.
What we cover
Focused work. Useful evidence.
Develop interpretability tools for complex ML models
Conduct explainability audits of production systems
Create model documentation frameworks (model cards, Datasheets for Datasets)
Design human-readable explanations for automated decisions
Advise on regulatory requirements for AI transparency
What you take forward
Outputs designed to be used.
The final scope is agreed together. Depending on your needs, the engagement can include:
- Explanation needs and audience map
- Assessment of selected explanation methods
- Model documentation and known limitations
- Human-review and communication guidance
How we approach it
From the question to a practical next step.
Identify the audience
Separate what engineers, operators, reviewers and affected users need to understand. An explanation should answer a defined question rather than merely display a chart.
Test the explanation
Check consistency and sensitivity to changes in the inputs or model. Distinguish an association in a prediction from evidence that changing a feature will cause a real-world outcome.
Support human decisions
Place explanations alongside uncertainty, limitations and review procedures. Document how someone can question a result and who has authority to change the decision.
Illustrative use case
What this can look like in practice.
A model flags transactions for review. Analysts may need relevant contributing signals, while a customer needs an understandable explanation and a route to correction. The design should serve both audiences without exposing sensitive detection logic unnecessarily.
An example of the work—not a client case study.Reference frameworks
Explore independent guidance that can inform the scope and evaluation criteria for this work. These links do not imply accreditation or endorsement.
Planning an engagement
What should we prepare?
Bring the decision you need to make, a description of the relevant systems or data, known constraints and your timeline. We can agree the appropriate access and confidentiality arrangements before any sensitive information is shared.
How do we define success?
We agree specific questions and useful outputs at the start. Outputs such as explanation needs and audience map are reviewed against the decisions you need to make. Limitations, dependencies and follow-up work remain visible in the handover.