Scholarlink

<p>As AI systems continue to increase their capabilities of performing human tasks there is a growing need to understand how the AI system determined its decisions. Interpretability is a concept in trustworthy AI research that is focused on understanding of the inner workings and decisions that come from the AI system. Our previous research revealed that AI developers lack consistent approaches or tools for implementing interpretability. There has been substantial theoretical interpretability research, yet the development of practical approaches in the form of tools to assist AI developers on interpretability remains underexplored in the research. This paper develops a taxonomy of AI interpretability techniques based on an analysis of the research literature using the survey of surveys method across 70 papers, examining 30 papers from 2019 to 2023. This paper develops a hierarchical taxonomy at a lower-level of abstraction to present and build upon relevant research literature. This paper also provides an approach as a practical tool in the form of decision trees to help AI developers identify and categorize interpretability techniques based on applicability and characteristics. Unlike existing interpretability taxonomies, this study introduces AI developer-oriented sample decision trees to aid in operationalizing the selection of interpretability techniques. This bridges theoretical research with practical implementation for AI developers. Preliminary testing and validation with AI developers was conducted to ensure applicability of the proposed taxonomy and decision trees. Preliminary validation demonstrated conceptual clarity and practical relevance, forming a foundation for future large-scale evaluation. This research contributes to further bridge the gap between AI systems and ensuring the practical implementation of interpretability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Interpretability taxonomy: an approach for artificial intelligence developers technique selection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月29日

查看全文
  • Carter Cousineau,
  • Rozita Dara

摘要

As AI systems continue to increase their capabilities of performing human tasks there is a growing need to understand how the AI system determined its decisions. Interpretability is a concept in trustworthy AI research that is focused on understanding of the inner workings and decisions that come from the AI system. Our previous research revealed that AI developers lack consistent approaches or tools for implementing interpretability. There has been substantial theoretical interpretability research, yet the development of practical approaches in the form of tools to assist AI developers on interpretability remains underexplored in the research. This paper develops a taxonomy of AI interpretability techniques based on an analysis of the research literature using the survey of surveys method across 70 papers, examining 30 papers from 2019 to 2023. This paper develops a hierarchical taxonomy at a lower-level of abstraction to present and build upon relevant research literature. This paper also provides an approach as a practical tool in the form of decision trees to help AI developers identify and categorize interpretability techniques based on applicability and characteristics. Unlike existing interpretability taxonomies, this study introduces AI developer-oriented sample decision trees to aid in operationalizing the selection of interpretability techniques. This bridges theoretical research with practical implementation for AI developers. Preliminary testing and validation with AI developers was conducted to ensure applicability of the proposed taxonomy and decision trees. Preliminary validation demonstrated conceptual clarity and practical relevance, forming a foundation for future large-scale evaluation. This research contributes to further bridge the gap between AI systems and ensuring the practical implementation of interpretability.