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

arXiv: https://arxiv.org/abs/2311.13158 · HTML: https://mlsanity.com/paper/2311.13158/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2023.

## Built on by (most cited first, 16 of 16)

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- [Evaluation of AI Ethics Tools in Language Models: A Developers' Perspective Case Study](https://mlsanity.com/paper/2512.15791/) (2025)
- [Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forward](https://mlsanity.com/paper/2601.13122/) (2026)
- [A Privacy by Design Framework for Large Language Model-Based Applications for Children](https://mlsanity.com/paper/2602.17418/) (2026)
- [Participatory provenance as representational auditing for AI-mediated public consultation](https://mlsanity.com/paper/2604.20711/) (2026)
- [No One to Blame: A Framework of Constitutive AI Unaccountability](https://mlsanity.com/paper/2608.12104/) (2026)
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## Abstract (arXiv)

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.