Consultancy and Services
AI Safety, Fairness & Interpretability Audits
Independent evaluation of automated decision-making.
Purpose & context
Independent evaluation of automated decision-making.
When organisations deploy AI systems that make consequential decisions, independent evaluation is essential. We conduct fairness audits that identify disparities in model outcomes across demographic groups, assess interpretability for non-technical stakeholders, and provide actionable recommendations for improvement. The review covers source-data integrity, fairness across relevant groups, interpretability for intended users and transparency about methods and limitations. We consider both the data and the systems that consume it.
A good fit when
- An automated system influences consequential decisions.
- A model needs independent evaluation before deployment.
- You need to understand fairness, reliability and oversight gaps.
What we cover
Focused work. Useful evidence.
Audit model outcomes for demographic disparities
Evaluate interpretability and explainability of AI systems
Assess data quality and representation in training sets
Review model documentation and development practices
Provide actionable recommendations with remediation guidance
Traceability from source data through model inputs to downstream decisions
Transparency documentation and intended-use limitations
What you take forward
Outputs designed to be used.
The final scope is agreed together. Depending on your needs, the engagement can include:
- Agreed evaluation scope and risk questions
- Performance, fairness and explanation assessment
- Documented limitations and residual risks
- Improvement and monitoring recommendations
How we approach it
From the question to a practical next step.
Clarify the intended use
Review the decision, users, affected groups and acceptable limits. Agree evaluation data, access and the questions an audit can reasonably answer.
Examine the evidence
Assess data quality, relevant performance differences, explanation methods and foreseeable misuse. Review uncertainty and failure conditions, including where the evaluation lacks sufficient data.
Support a defensible decision
Connect findings to deployment safeguards, human oversight and monitoring. Document remaining limitations and the changes that should trigger another assessment.
Illustrative use case
What this can look like in practice.
A team uses a model to rank applications. The review could examine data coverage, errors across relevant groups, the usefulness of explanations and the process for human challenge. Findings would support a deployment decision without treating an audit as a guarantee of safety.
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 agreed evaluation scope and risk questions are reviewed against the decisions you need to make. Limitations, dependencies and follow-up work remain visible in the handover.