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Developing Trustworthy AI Standards: ITI Provides Recommendations for NIST The Information Technology Industry Council (ITI) has released its proposed recommendations for the National Institute of Standards and Technology (NIST) on the development of trustworthy artificial intelligence (AI) standards. ITI emphasizes the importance of collaboration with international counterparts to formulate a risk management framework for generative AI systems, fostering global alignment and transparency. The council also highlights the need for proportionate evaluation and auditing guidelines to strike a balance between effective risk management and innovation. With robust standards in place, the responsible and secure use of AI can be ensured as we enter the era of AI technology.

NIST Encouraged to Develop AI Standards Based on ITI Recommendations

The Information Technology Industry Council (ITI) has made public its proposed recommendations for the implementation by the National Institute of Standards and Technology (NIST) of its responsibilities in encouraging secure and trustworthy usage and development of artificial intelligence (AI) technologies.

Strengthening Global Alignment.

The ITI advocates for NIST to collaborate with international counterparts in formulating a risk management framework, specifically for generative AI systems. Such cooperation aims to unify approaches globally and sheds light on the role of developers and deployers in bolstering transparency in AI.

Evaluation and Auditing Recommendations.

The global tech trade association further suggests that NIST ensures its evaluation and auditing guidelines are proportionate to the risks posed by a particular AI system. Striking the balance between effective risk management and enabling innovation is critical.

Conclusion

As we move further into the era of AI, it becomes increasingly crucial to have robust standards in place for its responsible and secure use. The recommendations put forth by the ITI to NIST underscore the importance of adopting globally aligned practices and underpin the need for transparency in AI. For further details about these recommendations, please refer to the original Executive Gov article .