A mathematical approach to the boolean minimization problem

Qualitative comparative analysis, Boolean minimization, Computer algorithms, Small-N research,

Beyond Informed Consent—Investigating Ethical Justifications for Disclosing, Donating or Sharing Personal Data in Research

In the last two decades, we have experienced a tremendous growth of the digital infrastructure, leading to an emerging web ecosystem that involves a variety of new types of services. A characteristic element of this web ecosystem is the massive increase of the amount, availability and interpretability of digitalized information—a development for which the buzzword “big data” has been coined. For research, this offers opportunities that just 20 years ago were believed to be impossible. Researchers now can access large participant pools directly using services like Amazon Mechanical Turk, they can collaborate with companies like Facebook to analyze their massive data sets, they can create own research infrastructures by, e.g., providing data-collecting Apps for smartphones, or they can enter new types of collaborations with citizens that donate personal data. Traditional research ethics with its focus of informed consent is challenged by such developments: How can informed consent be given when big data research seeks for unknown patterns? How can people control their data? How can unintended effects (e.g., discrimination) be prevented when a person donates personal data? In this contribution, we will discuss the ethical justification for big data research and we will argue that a focus on informed consent is insufficient for providing its moral basis. We propose that the ethical issues cluster along three core values—autonomy, fairness and responsibility—that need to be addressed. Finally, we outline how a possible research infrastructure could look like that would allow for ethical big data research