AI Pipeline Traceability Records for Accountable Model Deployment
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Solution Overview
Problem
Existing technologies lack effective methods for tracing and managing the stages of Artificial Intelligence (AI) pipelines, which hinders accountability and transparency in AI model deployment and operation.
Innovation Solution
A device and method for receiving, installing, and tracing AI models with trace instructions, creating records, and transmitting information about the AI model's installation stages, enabling traceability-aware AI management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI models are deployed without traceability mechanisms, then deployment speed is improved, but accountability and transparency deteriorate
Solution Approach 1:
The patent applies preliminary action by generating traceability records during the AI model training and deployment process itself, rather than adding traceability mechanisms afterward. The traceability information is created as the AI pipeline executes, capturing provenance data, hyperparameters, and processing information in real-time, which maintains deployment speed while ensuring accountability from the outset.
2Reliability
If detailed traceability records are created for all AI pipeline stages, then transparency is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the AI pipeline into distinct stages (data preparation, training, validation, deployment) and generating traceability records for each stage separately. This modular approach captures necessary transparency information without creating a monolithic complex system, as each stage's traceability can be managed and stored independently.
Solution Approach 2:
The patent introduces an intermediary traceability management system that mediates between the AI pipeline components and the record storage. This intermediary layer standardizes the collection and formatting of traceability information from various AI components, reducing the overall system complexity by providing a unified interface for transparency tracking.
3Measurement precision
If trace instructions are configured for multiple AI pipeline stages, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent applies continuity of useful action by generating traceability information continuously during the AI pipeline execution rather than pausing to collect data. The traceability records are created as part of the normal processing flow, with minimal overhead, ensuring that measurement precision is maintained without significant processing time loss.
4Loss of information
If comprehensive AI model tracing is implemented, then information completeness is improved, but data storage requirements increase
Solution Approach 1:
The patent applies the extraction principle by selectively capturing only the essential traceability information from each AI pipeline stage, such as provenance data, hyperparameters, and processing outcomes. Rather than storing all possible data, the system extracts and stores only the critical information needed for accountability and transparency, reducing storage requirements while maintaining information completeness.
Data Source
AI summary
Procedures, methods, architectures, apparatuses, systems, devices, and computer program products enabling tracing of Artificial Intelligence, AI, tasks and host devices. A device receives, from at least one other device, an Artificial Intelligence, AI, model for installation on the device to perform an AI task, and information indicative of trace instructions based on trace criteria for the AI model requested by the device, wherein the trace information corresponds to information for tracing one or more stages of an AI pipeline associated with the AI model, installs the AI model, creates a record for the installed AI model, and transmits a message to a record device, the message comprising information indicative of at least part of the created record.