Introduction
Artificial intelligence (AI) technology is advancing rapidly [2], necessitating the creation of compliance frameworks to ensure its responsible development, deployment [2], and use [2]. These frameworks include regulatory schemes [2], new laws, and industry standards designed to enhance the safety [2], trustworthiness [2], and fairness of AI applications [2].
Description
Artificial intelligence (AI) technology is rapidly evolving [2], leading to the establishment of various compliance frameworks aimed at ensuring responsible development [2], deployment [2], and use of AI systems [2]. These frameworks encompass regulatory schemes [2], newly enacted laws [2], and a growing array of industry and technical standards [2], all designed to enhance safety [2], trustworthiness [2], and fairness in AI applications [2].
Providers [1] [2], developers [1] [2], and deployers of AI systems recognize the importance of implementing robust controls to comply with legal requirements [2], maintain public trust [2], mitigate litigation risks [2], and ensure responsible AI usage [2]. A comprehensive methodology [2], known as the Standardized Assessment and Governance Enhancement (SAGE) framework [2], has been developed to address the challenges posed by the evolving compliance landscape [2].
The SAGE framework serves as a resource for building cross-regulatory programs by breaking down complex regulations into discrete, actionable requirements through a process called “atomization.” This approach has been applied to significant AI-related laws and standards, including the EU AI Act and the NIST AI Risk Management Framework [1]. Legal experts analyze these regulations to extract thousands of individual requirements [1], which are stored in a database [1], ensuring that the nuances and context are accurately captured [1].
To maintain internal consistency and reduce duplication [1], the framework employs a two-step process utilizing natural language processing (NLP) techniques [1]. An embedding algorithm assesses the semantic similarity between requirements [1], allowing for the consolidation of those with high similarity scores [1]. This results in a streamlined set of requirements that reflect essential obligations without unnecessary repetition [1].
The SAGE framework also facilitates an understanding of how requirements overlap across different sources [1]. As new regulations are analyzed [1], the same methods are applied to integrate them into the existing database [1], ensuring a comprehensive view of compliance obligations [1]. The algorithm generates numerical representations of each requirement [1], enabling precise comparisons based on their meanings [1].
By providing actionable guidance on compliance needs [1], including cross-jurisdictional advice and resources [1], the SAGE framework helps organizations navigate the complexities of AI governance [1]. It maps core requirements across leading standards [1], allowing for efficient alignment on necessary organizational controls [1]. This crosswalk process distills thousands of criteria into key compliance categories [1], addressing critical areas such as AI inventory [1], bias mitigation [1], and system decommissioning [1].
Conclusion
The SAGE framework significantly impacts AI governance by offering a structured approach to compliance, reducing redundancy, and ensuring consistency across various regulatory requirements. It aids organizations in aligning with essential standards, thereby enhancing the safety, trustworthiness [2], and fairness of AI systems. This comprehensive framework not only mitigates legal and litigation risks but also fosters public trust in AI technologies.
References
[1] https://www.lexology.com/library/detail.aspx?g=07b93ece-26ad-4f47-8eea-31375d107693
[2] https://www.jdsupra.com/legalnews/sage-a-systematic-approach-to-data-6972678/
