GxP AI Platform Lifecycle Tracking and Audit
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Solution Overview
Problem
Artificial intelligence (AI) algorithms lack formal control and audit trails, making it difficult to track and explain their learning improvements and output over time, which can lead to speculation and mistrust in their operations.
Innovation Solution
A fully compliant end-to-end GxP platform is implemented to track and document the learning improvements of AI algorithms throughout their life cycle, from development to validation and production, using a GxP chain identifier (ID) to record and identify specific datasets and production details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI algorithms are developed without formal control procedures, then development speed and flexibility are improved, but control and audit trail capabilities deteriorate
Solution Approach 1:
The system segments the AI algorithm lifecycle into distinct phases (development, training, validation, production) with dedicated environments for each. This segmentation enables formal control procedures to be applied at each stage without hindering overall development speed, as each phase can be independently managed and audited.
Solution Approach 2:
The GxP platform acts as an intermediary system that implements formal control and audit trail mechanisms between the flexible development process and regulatory requirements. It provides the necessary monitoring, tracking, and documentation capabilities without requiring changes to the core AI development methodology.
2Measurement precision
If AI algorithms are trained continuously to improve precision, then algorithm accuracy is improved, but understanding and explainability of algorithm developments deteriorate
Solution Approach 1:
The system implements comprehensive feedback mechanisms that automatically log and track all training activities, data inputs, and model changes. This feedback loop ensures that continuous learning processes are fully documented, maintaining explainability while achieving improved precision through ongoing training.
Solution Approach 2:
The platform performs preliminary actions by pre-configuring audit trails, data tracking, and version control mechanisms before training begins. This ensures that all subsequent training activities are automatically documented and traceable, eliminating the need for post-hoc explanation efforts.
3Reliability
If comprehensive tracking and documentation of algorithm lifecycle is implemented, then compliance and trust are improved, but system complexity increases
Solution Approach 1:
The GxP platform is designed as a universal system that handles multiple functions: development environment management, training data tracking, validation monitoring, production deployment, and audit trail generation. This multi-functionality consolidates what would otherwise be separate complex systems into a single integrated platform, improving compliance without proportionally increasing complexity.
Data Source
AI summary
A GxP (good practice) platform is implemented to enable artificial intelligence (AI) algorithms to be tracked from creation through training and into production. Deployed algorithms are assigned a GxP chain ID that enables identification of production details associated with respective algorithms. Trained algorithms, each of which are respectively associated with a GxP chain ID, are containerized and can be utilized through an application programing interface (API) to provide a service. The GxP chain ID is linked to production details stored within a database, in which the production details can include information such as data used to train the algorithm, a history version, a date/time stamp when the algorithm was validated, software and hardware on which the algorithm was developed and trained, among other details. Changes to the algorithm can be tracked using an immutable ledger facilitated by the implementation of blockchain.