AI Pipeline Trustworthiness Checks Under Network Resource Constraints
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Solution Overview
Problem
Existing AI/ML pipelines deployed in network entities with scarce resources face conflicts and instabilities due to unmanaged resource utilization, leading to drops in AI model and network performance, particularly in environments requiring low-latency operations.
Innovation Solution
A method and apparatus for feasibility checking of AI pipeline trustworthiness, involving an interface between the AI Trust Engine and AI Pipeline Orchestrator to exchange QoT and resource information, enabling efficient trade-offs between QoT, QoS, and resource constraints through standardized APIs for resource capability discovery and update queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If AI/ML pipeline is deployed at network entities with scarce resources to meet low-latency requirements, then response time is improved, but resource utilization becomes unmanaged leading to conflicts and instabilities
Solution Approach 1:
The system performs preliminary feasibility checks before deploying AI/ML pipelines to resource-constrained network entities. The network management entity evaluates whether the available resources can support the desired AI Quality of Trustworthiness (QoT) levels, preventing conflicts and instabilities by anticipating resource constraints before they occur.
Solution Approach 2:
The system implements continuous feedback mechanisms where the network management entity monitors resource utilization and AI pipeline performance in real-time. Based on this feedback, the system dynamically adjusts resource allocation and deployment decisions to maintain network stability while meeting low-latency requirements.
2Reliability
If TAI methods are employed to achieve desired AI QoT, then AI trustworthiness is improved, but resource consumption increases causing conflicts with QoS requirements
Solution Approach 1:
The system dynamically adjusts TAI method parameters based on available resources and required AI QoT levels. The network management entity selects and configures appropriate TAI techniques (such as fairness constraints, robustness optimizations, or explainability methods) with adjusted parameter settings to achieve the desired trustworthiness level while minimizing resource consumption.
Solution Approach 2:
The system applies TAI methods selectively rather than uniformly across all AI pipeline operations. The network management entity determines the minimum necessary application of TAI techniques required to meet AI QoT requirements, avoiding excessive resource consumption while maintaining adequate trustworthiness levels.
3Reliability
If multiple TAI techniques are applied simultaneously to ensure fairness, explainability and robustness, then AI QoT is improved, but device complexity increases
Solution Approach 1:
The system segments the application of TAI techniques across different phases of the AI pipeline and different network entities. The network management entity divides the complex task of ensuring fairness, explainability, and robustness into separate, manageable components that can be applied independently at appropriate stages, reducing overall system complexity.
Solution Approach 2:
The network management entity implements a universal framework that handles multiple TAI requirements through a single integrated system. This multi-functional approach allows the same infrastructure to manage fairness constraints, robustness optimizations, and explainability requirements, avoiding the need for separate complex systems for each TAI aspect.
Data Source
AI summary
A method, executable by a first network entity or function associated to a network, wherein the first network entity or function has an interface configured to receive information in relation to an artificial intelligence, AI, trustworthiness level from a network management entity or function associated to the network and a second network entity or function associated to the network, has an interface configured to receive information in relation to an AI service level from the network management entity or function, the method comprising communicating with the second network entity or function via an interface established between the first network entity or function and the second network entity or function.