  Artificial Intelligence (AI), particularly through the advent of large-scale
generative AI (GenAI) models such as Large Language Models (LLMs), has become a
transformative element in contemporary technology. While these models have
unlocked new possibilities, they simultaneously present significant challenges,
such as concerns over data privacy and the propensity to generate misleading or
fabricated content. Current frameworks for Responsible AI (RAI) often fall
short in providing the granular guidance necessary for tangible application,
especially for Accountability-a principle that is pivotal for ensuring
transparent and auditable decision-making, bolstering public trust, and meeting
increasing regulatory expectations. This study bridges the accountability gap
by introducing our effort towards a comprehensive metrics catalogue, formulated
through a systematic multivocal literature review (MLR) that integrates
findings from both academic and grey literature. Our catalogue delineates
process metrics that underpin procedural integrity, resource metrics that
provide necessary tools and frameworks, and product metrics that reflect the
outputs of AI systems. This tripartite framework is designed to operationalize
Accountability in AI, with a special emphasis on addressing the intricacies of
GenAI.
