But how can organizations developing ML models enforce explainability and transparency standards when doing so might mean sharing with the public the very features, data sets, and model frameworks that represent that organization's proprietary intellectual property (IP)? Given machine learning's complexity and interdisciplinary nature, executives should employ a wide variety of approaches to manage the associated risks, which include building risk management into model development and applying holistic risk frameworks that leverage and adapt principles used in managing other types of enterprise risk. Whereas standard technical documentation is created to help practitioners implement a model, documentation focused on explainability and transparency informs consumers, regulators, and others about why and how a model or data set is being used. Such documentation includes a high-level overview of the model itself, including: its intended purpose, performance, and provenance; information about the training data set and training process; known issues or tradeoffs with the model; identified risk mitigation strategies; and any other information that can help contextualize the technology. Similarly, model documentation can become the proxy for sharing the model and its features and data sets with the world as opposed to sharing the actual "cookie recipe."
Mar-27-2021, 03:55:07 GMT