2020/11/17 by David Piorkowski, D. Gonzalez, Piorkowski, David +8 · 1 citation
Business, Management and Accounting · Computer Science · Decision Sciences · Social Sciences · #Artificial Intelligence (cs.AI) #Business Process Modeling and Analysis #Data Quality and Management #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Personal Information Management and User Behavior #Privacy, Security, and Data Protection #Software Engineering (cs.SE) #cs.AI #cs.SE
paper · pdf · doi:10.48550/arxiv.2011.08774
15 pages, 1 figure, 8 tables
arxiv created 2020/11/17 · openalex publication_date 2020/11/17 · arxiv updated 2020/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A vital component of trust and transparency in intelligent systems built on machine learning and artificial intelligence is the development of clear, understandable documentation. However, such systems are notorious for their complexity and opaqueness making quality documentation a non-trivial task. Furthermore, little is known about what makes such documentation "good." In this paper, we propose and evaluate a set of quality dimensions to identify in what ways this type of documentation falls short. Then, using those dimensions, we evaluate three different approaches for eliciting intelligent system documentation. We show how the dimensions identify shortcomings in such documentation and posit how such dimensions can be use to further enable users to provide documentation that is suitable to a given persona or use case.