Global AI Auditing Framework: Recommendations for Audit Design, Process, and Procedure

The International Panel on the Information Environment (IPIE) proposes a comprehensive global AI auditing framework to address the lack of common standards in AI inspection. The framework emphasizes the need for auditor independence, stakeholder engagement, and the inclusion of environmental and labor impacts. It provides a seven-step approach covering audit scope, auditor roles, context identification, criteria development, evidence collection, methodology, and post-audit transparency to protect human rights and collective wellbeing, particularly for marginalized communities in the majority world.

• The current AI auditing ecosystem lacks common standards and procedures, which hinders the ability to compare findings across different auditing approaches and maintain consistent quality and accountability.
• AI audits are predominantly conducted in North America and Europe, often neglecting the disparate impacts of AI on the majority world, including labor exploitation, human rights violations, and environmental degradation.
• The proposed framework recommends a structured audit process consisting of seven key components: defining the audit scope, establishing the auditor's role, identifying context and stakeholders, setting audit criteria, gathering evidence, applying a mixed methodology, and executing post-audit activities.
• Audit criteria must be precise and contextual, specifically addressing data privacy, indigenous data sovereignty, fair labor practices (such as protection from algorithmic management), and environmental impacts including e-waste and carbon footprints.
• Effective auditing requires that auditors move beyond relying on provided documentation to perform active testing of AI system performance and scenario tests over extended periods to capture long-term impacts.
• To ensure accountability, audit results must be made public through open-access journals, news media, or public repositories, and include a point of contact for stakeholders to contest AI system aspects.

APA

McKinley, C., Sylvester, P., Faveri, B., Johnson-León, M., Chun, W. H. K., Hannák, A., Mendoza, M., Broussard, M., Enikolopov, R., Nelson, A., Sandvig, C., Sesan, ‘., Sporle, A., Srihari, R., Srinivasan, J., Wilson, C., Zou, M., Valenzuela, S., & Howard, P. (2024). Global AI Auditing Framework: Recommendations for Audit Design, Process, and Procedure. International Panel on the Information Environment. https://www.ipie.info/research/sfp2024-2

Chicago

McKinley, Catherine, Prem Sylvester, Benjamin Faveri, Maureen Johnson-León, Wendy Hui Kyong Chun, Anikó Hannák, Marcelo Mendoza, et al. Global AI Auditing Framework: Recommendations for Audit Design, Process, and Procedure. International Panel on the Information Environment, 2024. https://www.ipie.info/research/sfp2024-2.

Wikipedia

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BibTeX

@techreport{mckinley2024global, author = {McKinley, Catherine and Sylvester, Prem and Faveri, Benjamin and Johnson-León, Maureen and Chun, Wendy Hui Kyong and Hannák, Anikó and Mendoza, Marcelo and Broussard, Meredith and Enikolopov, Ruben and Nelson, Alondra and Sandvig, Christian and Sesan, ‘Gbenga and Sporle, Andrew and Srihari, Rohini and Srinivasan, Janaki and Wilson, Christo and Zou, Mimi and Valenzuela, Sebastián and Howard, Philip}, title = {{Global AI Auditing Framework: Recommendations for Audit Design, Process, and Procedure}}, institution = {International Panel on the Information Environment}, year = {2024}, url = {https://www.ipie.info/research/sfp2024-2}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }