🤖 AI Summary
This study addresses the lack of empirical evaluation regarding whether existing dataset documentation frameworks effectively foster developer reflectivity. Combining mixed-methods thematic analysis with corpus-assisted discourse analysis, the research systematically examines how prevailing documentation frameworks—and their real-world instantiations—cover core dimensions of reflectivity. The findings reveal, for the first time, that current frameworks consistently overlook critical reflective themes. Building on this insight, the authors develop a reflectivity-oriented coding manual and propose an enhanced datasheet template incorporating targeted prompts to elicit deeper reflection. This work offers actionable strategies and practical tools to strengthen the reflective capacity of dataset documentation practices.
📝 Abstract
It is prominently recognized that dataset development in machine learning is a value-laden process from problem formulation to data processing, use, and reuse. Structured documentation frameworks such as datasheets, data statements, and dataset nutrition labels have been created to aid developers in documenting how their datasets were produced and, according to the creators of the frameworks, to facilitate reflexivity in dataset development. While reflexivity is a stated goal, it is unclear whether and to what extent these structured dataset documentation frameworks incorporate concepts from reflexivity literature (at FAccT and elsewhere) and whether the use of the frameworks demonstrates reflexivity. Here, we adopt mixed-method thematic analysis and corpus-assisted discourse analysis to explore how reflexivity is incorporated in structured documentation frameworks and their responses. We demonstrate empirically that there is a general lack of engagement with major themes of reflexivity in both dataset documentation frameworks and published applications of these frameworks. We present a codebook of major reflexivity topics, recommend actionable strategies, and propose a set of extended datasheet questions to more effectively incorporate these topics into structured documentation frameworks and in the FAccT literature.