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Standards help ensure AI systems are reliable
Street with autonomous vehicles
Autonomous vehicles use sensors and cameras to connect to the environment surrounding them (Photo:

Artificial intelligence (AI) continues to play an increasingly significant role in systems for decision making and autonomous processes. As digitalization continues to advance across industries, systems will have the potential to impact our lives, so we must ensure they are robust, in other words, resilient, accurate and reliable.

Some well-known examples include healthcare diagnosis, self-driving vehicles and autonomous robots that work side by side with humans in manufacturing facilities. In these situations, there could be serious implications should errors occur. There are plenty of other examples of where AI makes decisions, which impact our lives, such as in financial investments, job recruitment or the approval of loans.

Trusting the technology

How do we ensure AI systems are robust, in other words, how can we know that these systems will withstand the live situation they have been designed for, in terms of ability to function properly?

One way to do this is through the development of international standards. IEC and ISO together develop such standards for artificial intelligence, through their joint committee SC 42.

“Technologies such as artificial intelligence are fuelling the digital transformation of industry”, said Wael William Diab, Chair of SC 42. “SC 42 has been looking at the entire AI ecosystem, which includes novel approaches to address emerging issues such as trustworthiness from the start and thus enable broad adoption. The robustness series complements the portfolio of trustworthy and ethical standards the committee is developing.”

SC 42 recently published ISO/IEC Technical Report 24029-1, Artificial intelligence – Assessment of the robustness of neural networks – Part 1: Overview, which contributes towards ensuring that products and services using AI systems are safe.

Read the full e-tech interview with the editor of the Technical Report, to find out more about robustness of neural networks and the types of approaches or methods available to assess issues and risks tied to the robustness of AI systems.

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