Abstract
Auditing of AI systems is a promising way to understand and manage ethical problems and societal risks associated with contemporary AI systems, as well as some anticipated future risks. Efforts to develop standards for auditing AI systems have therefore understandably gained momentum. However, we argue that creating auditing standards is not just insufficient, but actively harmful by proliferating unheeded and inconsistent standards, especially in light of the rapid evolution and ethical and safety challenges of AI. Instead, the paper proposes the establishment of an AI Audit Standards Board, responsible for developing and updating auditing methods and standards in line with the evolving nature of AI technologies. Such a body would ensure that auditing practices remain relevant, robust, and responsive to the rapid advancements in AI. The paper argues that such a governance structure would also be helpful for maintaining public trust in AI and for promoting a culture of safety and ethical responsibility within the AI industry. Throughout the paper, we draw parallels with other industries, including safety-critical industries like aviation and nuclear energy, as well as more prosaic ones such as financial accounting and pharmaceuticals. AI auditing should emulate those fields, and extend beyond technical assessments to include ethical considerations and stakeholder engagement, but we explain that this is not enough; emulating other fields' governance mechanisms for these processes, and for audit standards creation, is a necessity. We also emphasize the importance of auditing the entire development process of AI systems, not just the final products...
Overview
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The paper advocates for the establishment of an AI Audit Standards Board to address the rapid evolution and unique challenges in auditing AI systems.
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It identifies shortcomings in current static auditing standards, which fail to keep pace with AI technology advancements and varied applications.
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The proposed AI Audit Standards Board would continuously develop and update auditing standards, enhancing trust through comprehensive and transparent practices.
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Drawing from analogous regulatory bodies in other industries, this board could significantly influence future AI governance and regulatory policies.
Establishing an AI Audit Standards Board: Addressing the Dynamics and Nuances in AI System Auditing
Introduction
The realm of auditing AI systems, crucial for assessing both ethical considerations and systemic risks, is undergoing rapid evolution. Building upon analogous industries such as aviation, nuclear energy, and pharmaceuticals, the authors argue for the creation of an AI Audit Standards Board. This entity would not merely develop standards but continuously adapt them, ensuring audits are relevant amidst the fast-paced advancements in AI technology.
Current Standards and Their Limitations
AI system auditing has largely been informed by practices established in other audits, such as finance. These existing methods focus on periodic evaluations which unfortunately do not suffice due to the swiftly evolving capabilities of AI systems. Foundation models introduce unpredictable functionalities which can quickly make the static auditing standards obsolete. Additionally, the heterogeneity in the types of AI applications complicates the establishment of a one-size-fits-all auditing standard.
- Issue with Static Standards: Static standards become quickly outdated as AI technology evolves.
- Varied Applications: Different AI applications require distinct auditing approaches which current standards do not adequately support.
Proposal for an AI Audit Standards Board
Recognizing the deficiencies in the current auditing practices, the paper proposes establishing an AI Audit Standards Board responsible for the ongoing development of dynamic auditing standards. This board would take cues from existing regulatory bodies in other industries but tailor its functions to address the unique challenges posed by AI systems.
- Responsibilities: Develop, implement, and routinely update AI auditing standards.
- Structure: Operate independently while engaging with various stakeholders including AI developers, users, and regulators.
- Goal: Enhance trust in AI and ensure ethical and safety considerations are constantly addressed.
Practical Implications of the Standards Board
Implementing an AI Audit Standards Board would not only standardize the auditing of AI systems but also imbue a sense of public trust through transparency and reliability in audits. It would ensure that audits are comprehensive—covering not just the AI systems but the entire development process, addressing everything from initial design to post-deployment.
- Comprehensive Auditing: Cover the AI system's entire lifecycle, resembling practices in pharmaceuticals.
- Enhanced Trust: Public trust is bolstered via transparent and independent auditing practices.
Theoretical Contributions and Future Directions
The paper’s advocacy for an AI Audit Standards Board contributes theoretically by framing AI system auditing within a continuous improvement context, rather than periodic evaluations. This approach can significantly influence future regulatory policies and AI governance frameworks.
- Continuous Improvement: Advocating for dynamic auditing standards that evolve with AI advancements.
- Influence on AI Governance: The board could set a precedent for how AI systems are regulated and audited globally.
Conclusion
The paper underscores the necessity for an AI Audit Standards Board to address the current inadequacies in AI system auditing. By drawing parallels with other regulatory bodies and pointing out the unique challenges posed by AI systems, it offers a robust framework for ensuring ongoing relevance and effectiveness of AI audits. This proposal, if implemented, could pioneer a significant shift in how AI systems are managed and trusted by the public and could establish a foundation for more responsible AI development and deployment.
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