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Such details provide a deeper understanding and appreciation for Ai Data Auditing Techniques.
But how are we to understand the term ‘AI auditing’? In the broadest sense, auditing refers to an independent examination of any entity, conducted with a view to express an opinion thereon (Gupta, 2004 ). So understood, auditing has a long history of promoting trust and transparency in areas like security and financial accounting (LaBrie & Steinke, 2019 ). The basic idea is simple: just as the financial transactions of an organisation can be audited for correctness, completeness and legality, so the design and employ of AI systems can be audited with respect to not only their technical performance but moreover their alignment with organisational policies and hard regulations. While this analogy between financial audits and the auditing of AI is useful, it only goes so far. Analogies sometimes constrain our reasoning by uncritically carrying over assumptions from one domain to another (Taddeo, 2016 ). Hence, a more precise conceptualisation of auditing of AI that makes its functional and operational components explicit is needed.
Such details provide a deeper understanding and appreciation for Ai Data Auditing Techniques.
The prospect of auditing AI systems has recently attracted much attention from researchers, companies and policymakers alike. Following Sandvig et al.’s ( 2014 ) article Auditing Algorithms, a rich and growing academic literature focuses on how auditing procedures can assist identify and mitigate the risks AI systems pose. In parallel, an AI auditing ecosystem is emerging whereby professional services firms provide auditing (or ‘assurance’) services to aid clients ensure that the AI systems they design and deploy are ethical, legal and technically robust. This development is not limited to the private sector (Morley et al., 2021 ). For example, in the Artificial Intelligence Act (AIA), the European Commission ( 2021 ) sketches the contours of a union-wide auditing ecosystem and mandates ‘conformity assessments with the involvement of an independent third party’ for specific high-risk AI systems.
The latest developments, practical guidance, and AI tools for internal auditing, fostering a deeper understanding and application of this ever-evolving innovation to empower internal audit professionals. Browse articles, podcasts, webinars, conferences, tools, briefs and papers, and practical guidance, all curated to enhance your understanding and application of artificial intelligence.
The auditor’s role in the Digital Age needs to evolve and adapt in a manner in which audits are a mechanism to identify patterns and trends from large data sets. These insights provide support for risk assessments, project scoping, and proactive and early identification of potential issues, among other things. For example, in today’s rapidly evolving technology landscape, existing AI and ML techniques not only detect fraudulent transactions and identify high-risk issues such as unknown system activity from user endpoints, but learning models can as well be built from such interventions.
