Introduction
Artificial intelligence (AI) is a rapidly evolving field that includes machine learning, deep learning [1] [2], and generative AI [1] [2]. As these technologies advance [1], they are subject to increasing scrutiny from industry leaders and regulators [2]. This scrutiny aims to define parameters, establish auditing processes, and assess the implications of attorney-client privilege in AI audits [2].
Description
Artificial intelligence (AI) encompasses machine learning [1] [2], deep learning [1] [2], and generative AI [1] [2], and is currently under increasing scrutiny from industry leaders and regulators [2]. As these technologies evolve [1], there is a concerted effort to define their parameters, establish auditing processes, and assess the implications of attorney-client privilege in relation to these audits. The regulatory landscape surrounding AI is becoming increasingly complex, characterized by a blend of existing statutes [2], enforcement actions [2], and recent legislative initiatives [2].
To guide future regulations and set industry standards [2], the application of soft law is considered essential [2]. Although not legally binding [2], soft law provides a framework for organizations to anticipate enforceable guidelines. A prominent example is the NIST AI Risk Management Framework (AI RMF) [2], which offers recommendations for AI governance and auditing practices aimed at enhancing transparency [2].
The demand for transparent AI has underscored the necessity of identifying biases within AI systems, leading to a reevaluation of current legal frameworks and standards for AI audits [2]. Factors that influence the protection of AI audits under attorney-client privilege include the audit’s purpose [2], jurisdiction [2], and the involvement of third-party auditors [2], with the determination of privilege ultimately dependent on specific circumstances [2].
Conclusion
The evolving landscape of AI regulation has significant implications for both industry and legal frameworks. As AI technologies continue to develop, the establishment of clear guidelines and auditing processes will be crucial in ensuring transparency and accountability. The role of soft law and frameworks like the NIST AI RMF will be pivotal in shaping future standards, while the complexities of attorney-client privilege in AI audits will require careful consideration to protect sensitive information.
References
[1] https://www.jdsupra.com/legalnews/ai-audit-best-practices-for-building-8571995/
[2] https://www.lexology.com/library/detail.aspx?g=64d6b78c-bf22-4060-9808-9e46897a506f
