Ethics, Integrity and Transparency: Research Collections and Sensitive Data
- Saša Madacki, University of Sarajevo
- Mario Hibert, Faculty of Philosophy, University of Sarajevo
- Lejla Hajdarpašić, Faculty of Philosophy, University of Sarajevo
The aim of this session is to examine the integrity of research collections in light of ethical considerations of handling sensitive data, information, and documents stored in collections held by libraries and archives. Sensitive data in these collections come from various sources and in various formats: as testimonies of war survivors in interviews and focus group transcripts, as member lists of various social groups and movements, as personal diaries, etc. The session will try to address how to protect sensitive information and personal data that may affect public opinion or public security in the light of open access to research data and user policies.
Integrity of Research Collections in Social Sciences: Boston College Tapes Case as a trigger to re-examine Balkan Collections on War and War Atrocities
Speaker: Saša Madacki
Social Science Data Archives collect data for writing research papers. Most of the currently stored data are quantitative and are used by the “second generation of researchers” to reinterpret “old” data in a new light. The aim of the Social Science Data Archives is to enhance the exchange of knowledge and sharing of data. But what happens to qualitative data (testimonies of war survivors deposited in special collections, interviews and focus group transcripts of these testimonies, lists of members of various social groups and movements, personal diaries)? These often contain sensitive information which is not protected by confidentiality agreements or legislation (as is the case with priests, doctors or lawyers). How can librarians and archivists deal with these issues?
Sensitive Empirical Research Data Sets
Speaker: Lejla Hajdarpašić
Researchers who collect and interpret empirical research data sets that form a basis for research papers in social sciences are encouraged to provide open research data sets in respectable open research data archives. Within the academic community, efforts towards providing open research data for the archives through institutional and/or national policies include collaboration with academic librarians who contribute to research data management but also to research data services. Why are librarians so essential in this process? And what additional safety concerns they need to address when managing sensitive empirical research data sets in archives?
Information Ethics and Empirical Data Sets
Speaker: Mario Hibert
The way data sources are published, collected, organized and disseminated highlights the gap between professional rhetoric and the reality of library practices, which is especially observable in informationally marginalized groups in the social, cultural and political life. According to Berman (1972), libraries should not offer to readers only “safe, orthodox, establishment-type literature”. In other words, by hiding behind neutrality, a culturally elitist privilege to avoid the political aspects of the profession, librarians distance themselves from the status quo (values of the dominant paradigm) and take radical responsibility to question library neutrality: “true professionalism implies evolution, if not revolution; those who ‘confess’ the vocation having concrete goals and standards for the advancement of existence, which inevitably means its movement, shaking and transformation” (West, 1972). Revolted by apolitical attitude, librarians with a “personal code”, are remembered as “whistle-blowers” who denounced “silence in the profession”. As implications of self-censorship are broad in the socially responsible public mission of “guardians of knowledge”, today’s hiding behind technological neutrality can be called public irresponsibility. Cukier and Mayer-Schöenberger (2013) claim that avoiding the post-digital materiality of “datafication” of everyday life leads to a very specific form of commodification. Here we shall try to demystify artificial intelligence as an instrument for extracting knowledge which Joler and Pasquinelli (2020) term “statistical science fiction”.