The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis
arXiv:2408.02379v2 Announce Type: replace-cross Abstract: Developing and certifying safe - or so-called trustworthy - AI has become an increasingly salient issue, especially in light of upcoming regulation such as the EU AI Act. In this context, the black-box nature of machine learning models limits the use of conventional avenues of approach towards certifying complex technical systems. As a potential solution, methods to give insights into this black-box - devised in the field of eXplainable AI (XAI) - could be used. In this study, the potential and shortcomings of such methods for the purpo
The increasing development and deployment of advanced AI, coupled with emerging regulation like the EU AI Act, necessitates robust methods for safety and certification.
The certification of trustworthy AI is critical for its widespread adoption and integration into regulated sectors, impacting both technological progress and societal trust.
The focus on Explainable AI (XAI) as a path to certification changes how AI models will be developed, evaluated, and accepted by regulators and users.
- · XAI researchers and developers
- · AI certification bodies
- · Companies developing highly regulated AI systems
- · AI auditing firms
- · Developers of black-box AI models
- · Companies unable to explain their AI systems
- · Uncertified AI solutions
Increased investment and research into XAI techniques for practical application in safety and certification.
New standards and regulatory frameworks will emerge, explicitly demanding XAI capabilities for AI deployment in critical systems.
The market for AI solutions will bifurcate, with 'certifiable AI' commanding higher trust and adoption in sensitive domains.
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Read at arXiv cs.AI