Ten simple rules for responsible big data research

Zook, Matthew; Barocas, Solon; boyd, danah; Crawford, Kate; Keller, Emily; Gangadharan, Seeta Peña; Goodman, Alyssa A.; Hollander, Rachelle D.; Koenig, Barbara A.; Metcalf, Jacob; Narayanan, Arvind; Nelson, Alondra; Pasquale, Frank A.

doi:10.1371/journal.pcbi.1005399

Cited by 244 publications

(213 citation statements)

References 21 publications

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“…These findings hold important implications for the growing data science field, both within and beyond CSCW. Trustworthy data science systems are a priority for organizations and researchers alike-evident, for instance, in rubrics for assessing a data science system's production-readiness [13] or rules for conducting responsible big data research [105]. Such forms of advice address a range of sociotechnical challenges, helping data scientists manage aspects ranging from performance evaluation and feature engineering to algorithmic harm and ethical data sharing.…”

Section: Collaboration Translation and Accountability: Implicationsmentioning

confidence: 99%

Trust in Data Science

Passi

Jackson

2018

Proc. ACM Hum.-Comput. Interact.

129

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The trustworthiness of data science systems in applied and real-world settings emerges from the resolution of specific tensions through situated, pragmatic, and ongoing forms of work. Drawing on research in CSCW, critical data studies, and history and sociology of science, and six months of immersive ethnographic fieldwork with a corporate data science team, we describe four common tensions in applied data science work: (un)equivocal numbers, (counter)intuitive knowledge, (in)credible data, and (in)scrutable models. We show how organizational actors establish and re-negotiate trust under messy and uncertain analytic conditions through practices of skepticism, assessment, and credibility. Highlighting the collaborative and heterogeneous nature of real-world data science, we show how the management of trust in applied corporate data science settings depends not only on pre-processing and quantification, but also on negotiation and translation. We conclude by discussing the implications of our findings for data science research and practice, both within and beyond CSCW.

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Section: Collaboration Translation and Accountability: Implicationsmentioning

confidence: 99%

Trust in Data Science

Passi

Jackson

2018

Proc. ACM Hum.-Comput. Interact.

129

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“…In addition, scientists and practitioners have the ethical responsibility to minimize potential harm to people who share illegal wildlife trade content on social media platforms (Zook et al. ). For example, the privacy policy and terms of use of each social media platform should be followed strictly and only publicly available social media data used.…”

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confidence: 99%

A framework for investigating illegal wildlife trade on social media with machine learning

Minin

Fink

Hiippala

et al. 2018

Conservation Biology

105

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“…Shortly after Hurricane Katrina, the newly launched Google Earth showed a lack of placemarkers in the Lower Ninth Ward, a low‐income, black neighborhood (Crutcher & Zook, ). From a data validity perspective, these results are encouraging, but we must be cautious of spurious patterns (Miller & Goodchild, ; Zook et al, ).…”

Section: Introductionmentioning

confidence: 88%

A spatial analysis of non‐English Twitter activity in Houston, TX

Haffner

2018

Transactions in GIS

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The use of social media data in geographic studies has become common, yet the question of social media's validity in such contexts is often overlooked. Social media data suffers from a variety of biases and limitations; nevertheless, with a proper understanding of the drawbacks, these data can be powerful. As cities seek to become “smarter,” they can potentially use social media data to creatively address the needs of their most vulnerable groups, such as ethnic minorities. However, questions remain unanswered regarding who uses these social networking platforms, how people use these platforms, and how representative social media data is of users' everyday lives. Using several forms of regression, I explore the relationships between a conventional data source (the U.S. Census) and a subset of Twitter data potentially representative of minority groups: tweets created by users with an account language other than English. A considerable amount of non‐stationarity is uncovered, which should serve as a warning against sweeping statements regarding the demographics of users and where people prefer to post. Further, I find that precisely located Twitter data informs us more about the digital status of places and less about users' day‐to‐day travel patterns.

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Ten simple rules for responsible big data research

Cited by 244 publications

References 21 publications

Trust in Data Science

Trust in Data Science

A framework for investigating illegal wildlife trade on social media with machine learning

A spatial analysis of non‐English Twitter activity in Houston, TX

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