Summary

These ten principles are the outcome of a paper authored by Matthew Zook and nine other colleagues (Zook, et.al., 2017). It is modeled on PLOS Computational Biology's ongoing collection of rules and starts with the premise that "all big data research on social, medical, psychological, and economic phenomena engages with human subjects, and researchers have the ethical responsibility to minimize potential harm." The first five rules outline "how to reduce the chance of harm resulting from big data research practices" and the second "the second five rules focus on ways researchers can contribute to building best practices that fit their disciplinary and methodological approaches."
The focus of the first five rules is primarily on the harm that could be caused by the sharing and misapplication of data collected about people. To an extent, it is an elaboration of the non-maleficence principle. The authors note that "data ungoverned by substantive consent practices, whether social media or the residual DNA we continually leave behind us, may seem public but can cause unintentional breaches of privacy." Corporate tracking based on social media data may be perceived as "creepy".
Despite the existence of 'terms of use' statements or consent agreements, this paper places the onus of ethical use of data on researchers. They cite Markham (2016) saying "we can make [data ethics] an easier topic to broach by addressing ethics as being about choices we make at critical junctures; choices that will invariably have impact." They also recommend making "a code of conduct for your organization, research community, or industry" though they do note that researchers should know when to break these rules (for example, "in times of natural disaster or a public health emergency"). "In short," they write, "responsible big data research is not about preventing research but making sure that the work is sound, accurate, and maximizes the good while minimizing harm."

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