Model Transparency

Our Commitment to Transparency

AI and ML models should be transparent, explainable, and auditable—especially in regulated environments where decisions affect individuals or organisational operations. We prioritise model transparency to build trust and enable informed oversight.

What We Mean by Transparency

Model transparency encompasses:

  • Explainability: Understanding how models reach decisions
  • Interpretability: Ability to understand model behaviour and predictions
  • Auditability: Capability to review, validate, and reproduce model outcomes
  • Documentation: Comprehensive records of model development and deployment
  • Disclosure: Clear communication about model capabilities and limitations

Model Documentation

We maintain detailed documentation for production models including:

  • Model cards: Purpose, architecture, performance metrics, limitations
  • Data provenance: Training data sources, quality, representativeness
  • Training methodology: Algorithms, hyperparameters, validation approach
  • Performance metrics: Accuracy, precision, recall, fairness metrics
  • Use case specifications: Intended purpose and acceptable use
  • Limitations and risks: Known biases, edge cases, failure modes
  • Versioning: Model lineage and change history

Explainability Techniques

We employ various explainability methods:

  • Feature importance: Identifying which features drive predictions
  • SHAP values: Explaining individual predictions
  • LIME: Local interpretable model-agnostic explanations
  • Attention visualisation: For deep learning models
  • Counterfactual explanations: What would change the outcome
  • Rule extraction: Deriving interpretable rules from models

Model Interpretability

Where feasible, we favour inherently interpretable models (decision trees, linear models, rule-based systems). When complex models are necessary, we implement post-hoc explainability mechanisms and complementary interpretable models.

Auditability and Reproducibility

To enable auditing:

  • Version control for code, data, and models
  • Experiment tracking and metadata logging
  • Model registries with lineage tracking
  • Reproducible training pipelines
  • Audit logs of model predictions and decisions
  • Testing suites and validation datasets

Disclosure to Users

When individuals interact with AI systems, we provide:

  • Clear indication that AI is being used
  • Explanation of the AI's role in decision-making
  • Information about data used and how decisions are made
  • How to request human review or challenge decisions
  • Contact points for questions and concerns

Human-Understandable Explanations

Explanations are tailored to the audience—technical teams receive detailed technical explanations, while end-users and decision-makers receive high-level, accessible explanations. We avoid jargon and use clear language.

Monitoring and Drift Detection

Continuous monitoring for model performance degradation, data drift, and concept drift. Alerts trigger reviews when models behave unexpectedly or performance falls below thresholds. Regular retraining and recalibration processes.

Regulatory Compliance

Our transparency practices align with GDPR Article 22 (automated decision-making), UK GDPR, FCA AI guidance, and emerging AI regulations requiring explainability and human oversight.

Model Governance

Governance processes include:

  • Model risk assessment and approval workflows
  • Periodic model reviews and validation
  • Change management for model updates
  • Incident response for model failures
  • Retirement and decommissioning procedures

Limitations

We acknowledge that some advanced models (e.g., large language models, deep neural networks) are inherently difficult to fully explain. We are transparent about these limitations and implement compensating controls such as robust testing, monitoring, and human oversight.

Continuous Improvement

As explainable AI (XAI) research advances, we adopt new techniques and tools. We actively participate in industry forums and contribute to best practices for model transparency.

Contact

Model transparency enquiries: ai-ethics@bugni.io

For more on our AI practices, see Responsible AI