Ten simple rules for responsible big data research

By Matthew Zook, Solon Barocas, D. boyd, Kate Crawford, E. Keller, S. Gangadharan, Alyssa Goodman, R. Hollander, B. König, Jacob Metcalf and 3 more. 2017

309 citations recorded by the source corpus.

The use of big data research methods has grown tremendously over the past five years in both academia and industry. As the size and complexity of available datasets has grown, so too have the ethical questions raised by big data research. These questions become increasingly urgent as data and research agendas move well beyond those typical of the computational and natural sciences, to more directly address sensitive aspects of human behavior, interaction, and health. The tools of big data research are increasingly woven into our daily lives, including mining digital medical records for scientific and economic insights, mapping relationships via social media, capturing individuals’ speech and action via sensors, tracking movement across space, shaping police and security policy via “predictive policing,” and much more.

Subfields: Artificial Intelligence, Epidemiology, Safety Research

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Luciano Floridi

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Citations in the corpus

Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure (2020, background): “ts are analysed in light of the organization’s policies and commitments [114]. The dataset development documentation described above form part of the audit trail [48], boosting auditability by design [155]. The audit is but one form of review, however, and just as for software the entire dataset lif”
Ethics as Methods: Doing Ethics in the Era of Big Data Research—Introduction (2018, background): “This Special Issue contributes specific cases and fine-grained conceptual distinctions to ongoing discussions and critiques of data-driven research models and big data analytics (cf. Beer, 2017; boyd & Crawford, 2012; Cheney-Lippold, 2017; Leurs, 2017; Zook et al., 2017).”
Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning (2019, background): “Research communities have also begun to develop preliminary codes or principles to guide AI/ML development, while major Silicon Valley companies have committed time and attention to AI/ML’s ethical challenges [20]-[22].”
Artificial Intelligence and Journalism (2019, background): “Every technological system reflects the conscious and unconscious bias of its makers; AI is no different (Angwin & Larson, 2016; Angwin, Larson, Mattu, & Kirchner, 2016; Barocas & Selbst, 2016; Crawford, 2016, 2017; S. U. Noble, 2018; Zook et al., 2017).”
On learning and representing social meaning in NLP: a sociolinguistic perspective (2021, background): “…and discussions surrounding the use of online data in social media research—not only regarding collecting and storing data, but also how such data is shared, and how analyses based on such data are reported and disseminated (Fiesler and Proferes, 2018; Zook et al., 2017; Fiesler et al., 2020).”
Recommender systems and their ethical challenges (2020, background): “…biases and “filter bubbles” that are damaging to the normal functioning of public debate, group deliberation, and democratic institutions more generally (Bozdag 2013; Bozdag and van den Hoven, 2015; Harambam et al. 2018; Helberger et al. 2016; Koene et al. 2015; Reviglio 2017; Zook et al. 2017).”
Social media data for conservation science: A methodological overview (2019, background): “Only a small portion of all social media posts are geotagged, but the small portion is compensated by the vast volume of social media data (Poorthuis and Zook, 2017).”