Towards a Responsible AI Metrics Catalogue: A Collection of Metrics for AI Accountability

Accountability in AI requires three types of measurable metrics: The paper identifies process metrics (how AI decisions are made), resource metrics (tools and frameworks available), and product metrics (actual AI outputs), providing a practical framework that organizations can use to track and demonstrate responsible AI practices rather than relying on vague principles alone.

Current responsible AI frameworks lack practical guidance for implementation: Existing frameworks often describe what accountability should look like in theory but fail to provide the specific, measurable steps companies need to take, creating a gap between stated values and actual practice that this metrics catalogue aims to address.

Generative AI systems present unique accountability challenges: The paper highlights that large language models and similar GenAI systems create particular concerns around data privacy and the generation of misleading or fabricated content, requiring tailored metrics beyond those developed for traditional AI systems.

Accountability is essential for building public trust and meeting regulations: The research emphasizes that transparent, auditable decision-making enabled by accountability metrics is critical for maintaining public confidence in AI systems and satisfying increasingly stringent regulatory requirements from governments worldwide.

A comprehensive metrics approach draws from both academic research and real-world practice: The systematic review integrated findings from academic literature and grey literature (industry reports, practitioner guidance), suggesting that effective AI accountability requires insights from both researchers and organizations actually deploying these systems.