Systematic review reveals critical gaps in current artificial intelligence auditing frameworks, highlighting the need to integrate value chain stages and maturity levels for compliance.
Key Points
To systematically review existing methodologies, frameworks, and techniques for auditing artificial intelligence systems with an emphasis on ethical principles and regulatory compliance.
Conducted a systematic literature search across major academic databases, including Google Scholar, IEEE, ACM, and Springer.
Categorized existing auditing frameworks according to stages of the AI value chain and organizational AI maturity levels.
Identified significant gaps in current AI auditing approaches regarding standard coverage of legal requirements and ethical considerations.
Demonstrated that aligning auditing frameworks with specific AI value chain stages and organizational maturity levels improves evaluation relevance and effectiveness.