AI Trust Framework for Fairness, Robustness, and Explainability
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Solution Overview
Problem
Current AI/ML models in telecommunication networks lack a framework for incorporating trustworthiness, particularly in terms of fairness, robustness, and explainability, which is crucial for regulatory compliance and user trust, especially in critical applications like autonomous vehicles.
Innovation Solution
A framework (TAIF) is introduced to manage and monitor the trustworthiness of AI/ML models by translating service intents into trust level requirements, configuring, measuring, and ensuring fairness, robustness, and explainability across the AI/ML pipeline stages, using AI Trust Engines and Managers to enforce and monitor these qualities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI/ML models are deployed in telecommunication networks to improve service automation and performance, then productivity and speed are improved, but trustworthiness (fairness, robustness, explainability) deteriorates due to lack of framework
Solution Approach 1:
The patent introduces Trust Engines and Trust Managers as intermediary components between the AI/ML pipeline and the service. These intermediaries translate service intents into trust level requirements and monitor fairness, robustness, and explainability metrics, thereby enabling automated services while maintaining trustworthiness through dedicated oversight mechanisms.
Solution Approach 2:
The framework performs preliminary actions by translating service intents into trust level requirements before AI/ML model execution. Trust Managers proactively configure and monitor fairness, robustness, and explainability metrics in advance, ensuring that trustworthiness considerations are integrated into the AI pipeline from the beginning rather than added as an afterthought.
2Reliability
If AI/ML pipelines are made more transparent and explainable to improve trustworthiness, then fairness and explainability are improved, but device complexity increases due to additional monitoring and translation components
Solution Approach 1:
The patent segments the trustworthiness management functionality into distinct modular components: Trust Engines that translate service intents into trust requirements, and Trust Managers that monitor specific metrics (fairness, robustness, explainability). This segmentation allows each component to have a specialized function, making the overall complex system more manageable and maintainable through clear separation of concerns.
3Reliability
If trust level requirements are translated into specific fairness, robustness, and explainability metrics to improve trustworthiness management, then reliability is improved, but measurement precision requirements increase
Solution Approach 1:
The patent transforms the abstract concept of trustworthiness into concrete measurable parameters by defining specific metrics for fairness, robustness, and explainability. Trust Engines translate service-level trust requirements into these quantifiable parameters, enabling precise measurement and monitoring of trustworthiness throughout the AI/ML pipeline execution.
Data Source
AI summary
Method comprising: receiving a trust level requirement for a service; translating the trust level requirement into a requirement for at least one of a fairness, an explainability, and a robustness of a calculation performed by an artificial intelligence pipeline related to the service; providing the requirement for the at least one of the fairness, the explainability, and the robustness to a trust manager of the artificial intelligence pipeline.