From Reflection to Repair: A Scoping Review of Dataset Documentation Tools

Fuente: arXiv
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Autori principali: Reynolds-Cuéllar, Pedro, Wong-Villacres, Marisol, Garcia, Adriana Alvarado, Precel, Heila
Natura: Preprint
Pubblicazione: 2026
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author Reynolds-Cuéllar, Pedro
Wong-Villacres, Marisol
Garcia, Adriana Alvarado
Precel, Heila
author_facet Reynolds-Cuéllar, Pedro
Wong-Villacres, Marisol
Garcia, Adriana Alvarado
Precel, Heila
contents Dataset documentation is widely recognized as essential for the responsible development of automated systems. Despite growing efforts to support documentation through different kinds of artifacts, little is known about the motivations shaping documentation tool design or the factors hindering their adoption. We present a systematic review supported by mixed-methods analysis of 59 dataset documentation publications to examine the motivations behind building documentation tools, how authors conceptualize documentation practices, and how these tools connect to existing systems, regulations, and cultural norms. Our analysis shows four persistent patterns in dataset documentation conceptualization that potentially impede adoption and standardization: unclear operationalizations of documentation's value, decontextualized designs, unaddressed labor demands, and a tendency to treat integration as future work. Building on these findings, we propose a shift in Responsible AI tool design toward institutional rather than individual solutions, and outline actions the HCI community can take to enable sustainable documentation practices.
format Preprint
id arxiv_https___arxiv_org_abs_2602_15968
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle From Reflection to Repair: A Scoping Review of Dataset Documentation Tools
Reynolds-Cuéllar, Pedro
Wong-Villacres, Marisol
Garcia, Adriana Alvarado
Precel, Heila
Software Engineering
Artificial Intelligence
Computers and Society
Human-Computer Interaction
K.4.3
Dataset documentation is widely recognized as essential for the responsible development of automated systems. Despite growing efforts to support documentation through different kinds of artifacts, little is known about the motivations shaping documentation tool design or the factors hindering their adoption. We present a systematic review supported by mixed-methods analysis of 59 dataset documentation publications to examine the motivations behind building documentation tools, how authors conceptualize documentation practices, and how these tools connect to existing systems, regulations, and cultural norms. Our analysis shows four persistent patterns in dataset documentation conceptualization that potentially impede adoption and standardization: unclear operationalizations of documentation's value, decontextualized designs, unaddressed labor demands, and a tendency to treat integration as future work. Building on these findings, we propose a shift in Responsible AI tool design toward institutional rather than individual solutions, and outline actions the HCI community can take to enable sustainable documentation practices.
title From Reflection to Repair: A Scoping Review of Dataset Documentation Tools
topic Software Engineering
Artificial Intelligence
Computers and Society
Human-Computer Interaction
K.4.3
url https://arxiv.org/abs/2602.15968