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Highlights
From Black Box to Glass Box – Building Trustworthy AI Across the Lifecycle
AI systems operate as complex, dynamic entities, yet many of their decisions remain hidden from developers and users. This black box nature and the corresponding lack of transparency create significant challenges for industries, such as automotive, where safety and compliance are critical. An AI will nearly always give an answer, even if it is wrong or delivered with low prediction confidence. Developers struggle to understand the neural processes behind AI decisions, making it difficult to identify limitations in datasets or models. At the same time, regulatory frameworks, such as ISO/PAS 8800 for automotive, demand proven AI explainability and validation without providing clear guidance on how to achieve it.
Fragmented toolchains and siloed workflows further increase effort and risk gaps in regulatory conformance. Without early detection of hidden flaws, AI models risk compromising safety and trust once deployed. With the AI Software Integrity Builder, you can analyze your data model in detail and gain full visibility into the model’s behavior.
Keysight’s AI Software Integrity Builder introduces a novel, lifecycle-based approach to AI Assurance, answering the essential question: “What happens inside my black box, and how do I ensure a trustworthy AI deployment?” The solution helps to deliver the safety evidence required for regulatory conformance, empowering teams to validate, explain, adjust, and continuously improve AI systems.
Unlike fragmented toolchains that address isolated aspects of AI testing, Keysight’s integrated framework spans the entire lifecycle: from dataset analysis and model validation to inference-based testing in real-world environments.
Key benefits:
- Designed for Compliance: Supports compliance with emerging regulations like the EU AI Act and ISO/PAS 8800 for automotive.
- Built for Scale: Enables continuous AI assurance across development and maintenance workflows.
- Holistic Lifecycle Coverage: Covers not only dataset analysis & model optimization but also inference monitoring and domain adaptation – an all-in-one software solution.
Keysight empowers engineering teams to move from fragmented testing to a unified AI assurance strategy, enabling them to deploy AI systems that are not only performant but also explainable, auditable, and compliant by design.
Beyond Fragmented Testing – A Unified Lifecycle Approach to AI Assurance
While the market offers mostly open-source tools and vendor solutions that focus on isolated aspects of AI testing, most stop at analyzing datasets or models without addressing real-world behavior. Keysight closes this gap with a holistic lifecycle approach that not only validates what the model was trained on, but also how it behaves in deployment scenarios - an essential part of analysis for safety-critical domains such as automotive. This integrated software solution ensures traceability, explainability, and compliance with emerging regulations like the EU AI Act and international standards such as ISO/PAS 8800.
Core capabilities include:
- Dataset Analysis: Analyzes data quality using statistical methods to uncover biases, gaps, and inconsistencies that may affect model performance.
- Model-Based Validation: Delivers Explainable AI (XAI) by design, explaining model decisions and uncovering hidden correlations that enable developers to understand the patterns and limitations of an AI system.
- Inference-Based Testing: Goes beyond static validation by analyzing how the model performs in real-world environments, detecting deviations from training behaviour, and recommending improvements for the next iteration.
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