Blockchain Model Governance for Latent Feature Drift Monitoring
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Solution Overview
Problem
Conventional methods for monitoring machine learning models in production environments fail to effectively monitor shifts in latent features and their distributions, leading to potential misuse and lack of accountability in model deployment.
Innovation Solution
A model governance system that utilizes a blockchain to persist reference assets during model development, allowing for the monitoring of latent features and their distributions, and generates alerts based on deviations from predefined thresholds, ensuring responsible and ethical use of machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional monitoring methods are used for machine learning models in production, then the monitoring system is simple and easy to implement, but the system cannot effectively monitor shifts in latent features and their distributions, leading to lack of accountability
Solution Approach 1:
The patent applies preliminary action by persisting reference assets (including latent feature definitions, training data characteristics, and expected performance metrics) to the blockchain during the model development phase. This advance preparation enables automated monitoring in production by comparing current model behavior against these pre-established references, ensuring accountability without requiring complex real-time analysis infrastructure.
Solution Approach 2:
The blockchain serves as an intermediary layer between the machine learning model and the monitoring system. It immutably stores reference assets and facilitates automated comparison between production model outputs and expected behavior, enabling reliable accountability tracking while keeping the monitoring system architecture relatively simple and decentralized.
2Reliability
If blockchain is used to persist reference assets for model governance, then the audit trail is immutable and accountable, but the system complexity increases
Solution Approach 1:
Reference assets are persisted to the blockchain during the model development phase before deployment. This preliminary action ensures that the audit trail is established with complete and accurate information about model behavior expectations, eliminating the need for complex ongoing manual audits and simplifying production monitoring.
Solution Approach 2:
The blockchain creates an immutable copy of the reference assets and model governance information. This cryptographic copy ensures audit trail integrity without requiring complex verification systems, as the blockchain's inherent consensus mechanisms provide automatic validation of the stored data.
3Reliability
If real-time monitoring of latent features is implemented, then model misuse can be detected early, but the computational resources and system complexity increase
Solution Approach 1:
The patent extracts and monitors only the critical latent features that were identified during model development and persisted on the blockchain. By focusing monitoring efforts on these specific, pre-identified features rather than all model inputs and outputs, the system achieves effective misuse detection while minimizing computational resource consumption.
Solution Approach 2:
The latent features to be monitored are identified and defined during the model development phase, before deployment. This preliminary identification allows the production system to efficiently compute and compare only these specific features against blockchain-stored references, reducing real-time computational requirements while maintaining monitoring effectiveness.
Data Source
AI summary
A method includes determining, by a trained machine learning model, a score based at least on one or more latent features. The method also includes monitoring the determining of the score by the trained machine learning model. The monitoring includes determining one or more production statistics associated with the one or more latent features, derived variables and input data elements, and accessing one or more reference assets persisted on a model governance blockchain. The one or more reference assets includes one or more reference statistics and a threshold indicating a deviation between the one or more production statistics and the one or more reference statistics. The method also includes generating an alert based on the one or more production statistics associated with the one or more latent features meeting the threshold. Related methods and articles of manufacture are also disclosed.