What is it about?
The study presented in the paper advocates for a process-oriented approach to AI accountability, integrating both technical and socio-technical dimensions. Through a multivocal literature review (MLR), the study develops a system-level metrics framework tailored to operationalize AI accountability. This framework includes process metrics (procedural guidelines), resource metrics (necessary tools and frameworks), and product metrics (resultant artifacts). The primary contributions are the creation of a process-centric metrics catalog for AI accountability, the categorization of these metrics, and the foundation for a comprehensive Responsible AI framework.
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Why is it important?
As AI evolves into more advanced forms, particularly with the advent of large-scale generative models (GenAI) such as Large Language Models (LLMs), it brings not only technological innovations but also significant safety challenges. These challenges include data privacy concerns, lack of transparency, and the dissemination of misinformation. Such issues are particularly critical in sectors where the misuse of AI, especially advanced AI, can lead to unfavorable outcomes such as biased decision-making and dual-use technology concerns. Addressing these challenges necessitates the operationalization of responsible AI. Accountability, a cornerstone of responsible AI, serves as the backbone for enhancing AI safety. While the proliferation of Responsible AI principles worldwide highlights a growing recognition of their importance, legislative efforts such as the EU AI Act underscore the urgency of developing more concrete guidelines to enforce these principles, particularly accountability. By creating a process-centric metrics catalog for AI accountability and categorizing these metrics, this research addresses critical safety challenges in AI and lays the groundwork for a comprehensive Responsible AI framework, which is essential for enhancing AI safety and ensuring the responsible deployment of advanced AI technologies.
Perspectives
Writing this article has been a particularly fulfilling experience for me. This research on AI accountability metrics is a culmination of extensive exploration into both academic and grey literature, aiming to address some of the most pressing challenges in responsible AI. Personally, I believe that the metrics catalogue we have developed will provide a practical framework for developers and policymakers alike, fostering greater transparency and trust in AI systems. I hope this work not only advances the field of responsible AI but also inspires others to consider the nuanced dimensions of accountability in their own AI projects.
Boming Xia
CSIRO
Read the Original
This page is a summary of: Towards a Responsible AI Metrics Catalogue: A Collection of Metrics for AI Accountability, April 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3644815.3644959.
You can read the full text:
Resources
An AI System Evaluation Framework for Advancing AI Safety: Terminology, Taxonomy, Lifecycle Mapping
The advent of advanced AI underscores the urgent need for comprehensive safety evaluations, necessitating collaboration across communities (i.e., AI, software engineering, and governance). However, divergent practices and terminologies across these communities, combined with the complexity of AI systems-of which models are only a part-and environmental affordances (e.g., access to tools), obstruct effective communication and comprehensive evaluation. This paper proposes a framework for AI system evaluation comprising three components: 1) harmonised terminology to facilitate communication across communities involved in AI safety evaluation; 2) a taxonomy identifying essential elements for AI system evaluation; 3) a mapping between AI lifecycle, stakeholders, and requisite evaluations for accountable AI supply chain. This framework catalyses a deeper discourse on AI system evaluation beyond model-centric approaches.
Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering
Responsible AI (RAI) is essential for the widespread adoption of AI, addressing ethical challenges beyond algorithmic fairness. Many AI ethics frameworks lack practical guidance, leaving practitioners with general principles. This article presents an RAI Pattern Catalogue, developed through a multivocal literature review, offering actionable patterns for AI stakeholders. The catalogue is organized into three groups: multi-level governance patterns, trustworthy process patterns, and RAI-by-design product patterns. These patterns provide practical steps to ensure responsible AI practices throughout the entire AI system development lifecycle.
Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agents
Foundation model-enabled generative AI equips agents with advanced reasoning and language capabilities to autonomously achieve users' goals. However, designing these agents involves challenges like hallucinations, explainability, and accountability. This paper presents a pattern catalogue from a systematic literature review, offering 17 architectural patterns with context, forces, and trade-offs analyses. It provides clear guidance for designing effective foundation model-based agents, enhancing goal-seeking and plan generation.
Contributors
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