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Ensuring Responsible Big Data Research

28 / 06 / 2021

Guidelines to think about how to conduct a successful study based on responsible Big Data research

When carrying out research on privacy-preserving Big Data technologies, researchers should not overlook ethical, legal, societal and economic implications of big data applications.

Zook et al. proposes ten simple rules for responsible big data research that should address the increasingly complex ethical issues in big data research. The first five rules address the chance of harm resulting from big data research practices. The second five rules help researchers to contribute to building best practices that fit their disciplinary and methodological approaches.


  1. Acknowledge that data represents people and can do harm – until proven otherwise, data should be considered people, thereby acknowledging the difficulty of disassociating data from specific individuals.
  2. Recognize that privacy is more than a binary value – privacy depends on the nature of the data, the context in which they were created and obtained, and the expectations and norms of those who are affected.
  3. Guard against the reidentification of your data – possible vectors of reidentification in the data should be identified and minimized.
  4. Practice ethical data sharing – data should be shared as specified in research protocols, but concerns of potential harm from informally collected big data need to be a proactively addressed.
  5. Consider the strengths and limitations of your data; big does not automatically mean better – provenance and evolution of your data should be documented, messiness and multiple meanings should be acknowledged.
  6. Debate the tough, ethical choices – ethical practice for big data research should be debated with peers.
  7. Develop a code of conduct for your organization, research community, or industry – rules for responsible big data research should be developed and established.
  8. Design your data and systems for auditability – responsible internal auditing processes flow easily into audit systems and also keep track of factors that might contribute to problematic outcomes.
  9. Engage with the broader consequences of data and analysis practices – big data research has societal-wide effects.
  10. Know when to break these rules – responsible big data research depends on more than meeting checklists.

If you’re interested in these kinds of guidelines come check out where you can find analysis and guidelines for similar issues.