AIML-07

Do you make your ML model transparent through documentation and log inputs and outputs?

Explanation

This question is asking whether your organization provides transparency into how your machine learning (ML) models work by documenting them and logging their inputs and outputs. What this means: 1. Documentation: Do you provide clear documentation about how your ML models work, what data they were trained on, their limitations, and intended use cases? 2. Logging inputs/outputs: Do you record what goes into the model and what comes out, creating an audit trail? 3. Explainability: Can you explain why the model made specific predictions or decisions? 4. Inspectability: Can users examine the model's internal workings? Why it's asked in security assessments: - Transparency helps identify potential biases, security vulnerabilities, or unintended behaviors in ML systems - Logging creates accountability and helps with incident investigation if something goes wrong - Explainability is increasingly important for regulatory compliance (like GDPR's 'right to explanation') - Without transparency, ML models can be 'black boxes' that make decisions without oversight How to best answer: - Be specific about your documentation practices (what's documented, how it's maintained) - Describe your logging infrastructure (what's logged, retention periods, access controls) - Explain any explainability techniques you use (SHAP values, LIME, feature importance, etc.) - Mention any model cards, datasheets, or other standardized documentation you provide - If you have limitations in any areas, acknowledge them and describe any compensating controls

Guidance

Looking for model transparency, logging of inputs and outputs, explainations for the model's predictions, and allowing the users to inspect the model's internal representations.

Example Responses

Example Response 1

Yes, we provide comprehensive transparency for our ML models Each model has detailed documentation including its architecture, training data sources (with PII removed), performance metrics, limitations, and intended use cases This documentation is version-controlled and updated whenever models are retrained We implement extensive logging that captures all inputs to the model (sanitized of sensitive information), model outputs, confidence scores, and processing timestamps These logs are retained for 90 days and are accessible for audit purposes For explainability, we use SHAP (SHapley Additive exPlanations) values to help explain individual predictions and provide feature importance metrics Our customer-facing applications include an 'Explain this result' feature that provides non-technical explanations of major factors influencing predictions Additionally, we publish model cards following Google's framework for all production models, and our technical documentation allows customers to inspect model architectures and non-proprietary aspects of our feature engineering.

Example Response 2

Yes, our ML model transparency framework has three components: documentation, logging, and explainability tools For documentation, we maintain a centralized repository with detailed specifications for each model, including data lineage, training methodologies, validation results, and known limitations This documentation undergoes peer review before each model release For logging, our platform automatically captures timestamped records of all model inputs, outputs, and intermediate confidence scores in a tamper-evident logging system with a 180-day retention policy These logs are encrypted at rest and in transit, with access controls limiting visibility to authorized personnel For explainability, we've implemented a combination of global and local explanation techniques - LIME for local explanations of individual predictions and permutation importance for global feature relevance Our enterprise customers receive access to a dashboard where they can explore these explanations and understand how specific features influence model decisions within their specific use cases.

Example Response 3

No, we currently have limited transparency into our ML models While we do maintain basic documentation about model purposes and general functionality, we don't provide detailed information about model architectures or training methodologies as these are considered proprietary Our logging is primarily focused on system performance metrics rather than comprehensive input/output tracking We capture aggregate statistics about model usage but don't maintain detailed logs of individual predictions due to storage constraints and performance considerations We're currently exploring options to implement more robust explainability tools, but our models (primarily deep neural networks) present challenges for straightforward interpretation We recognize this limitation and are working to improve our transparency capabilities in our next major platform update scheduled for Q3 of next year, which will include a more comprehensive logging framework and the addition of model cards for our primary prediction services.

Context

Tab: AI
Category: AI Machine Learning