Documentation Practices of Artificial Intelligence
Stefan Arnold, Dilara Yesilbas, Rene Gr\"obner, Dominik Riedelbauch,, Maik Horn, and Sven Weinzierl
TL;DR
This paper reviews current AI documentation practices, highlighting evolving trends towards more comprehensive, engaging, and automated approaches to improve transparency and accountability in AI systems.
Contribution
It provides a comprehensive literature review of AI documentation practices, identifying key characteristics and the evolution towards more holistic and automated documentation methods.
Findings
Shift towards holistic documentation
Increase in automation support
Growing emphasis on transparency and accountability
Abstract
Artificial Intelligence (AI) faces persistent challenges in terms of transparency and accountability, which requires rigorous documentation. Through a literature review on documentation practices, we provide an overview of prevailing trends, persistent issues, and the multifaceted interplay of factors influencing the documentation. Our examination of key characteristics such as scope, target audiences, support for multimodality, and level of automation, highlights a dynamic evolution in documentation practices, underscored by a shift towards a more holistic, engaging, and automated documentation.
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Taxonomy
TopicsBig Data and Business Intelligence · Explainable Artificial Intelligence (XAI)