Ten simple rules for responsible big data research
Responsible big data research is not about preventing research but making sure that the work is sound, accurate, and maximises the good while minimising harm.
In "Ten simple rules for responsible big data research", Zook et al. highlight the need for direction in responsible big data research, and provide guidelines for addressing inevitable, complex ethical issues.
The beneficial possibilities for big data in science and industry are tempered by new challenges facing researchers that often lie outside their training and comfort zone. Social scientists now grapple with data structures and cloud computing, while computer scientists must contend with human subject protocols and institutional review boards (IRBs). While the connection between individual datum and actual human beings can appear quite abstract, the scope, scale, and complexity of many forms of big data creates a rich ecosystem in which human participants and their communities are deeply embedded and susceptible to harm. This complexity challenges any normative set of rules and makes devising universal guidelines difficult.
Another critical point in this article is the call for researchers to "make grappling with ethical questions part of their standard workflow". They use a number of examples to demonstrate that while anonymised data may seem innocuous, it can still cause harm and give rise to unanticipated ethical issues.
The ten "simple" rules are:
- Acknowledge that data are people and can do harm
- Recognize that privacy is more than a binary value
- Guard against the reidentification of your data
- Practice ethical data sharing
- Consider the strengths and limitations of your data; big does not automatically mean better
- Debate the tough, ethical choices
- Develop a code of conduct for your organization, research community, or industry
- Design your data and systems for auditability
- Engage with the broader consequences of data and analysis practices
- Know when to break these rules
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. The problems and choices researchers face are real, complex, and challenging and so too must be our response. We must treat big data research with the respect that it deserves and recognize that unethical research undermines the production of knowledge.
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