Intersectional Data Science

Data-driven research and applications often fail in very concrete ways: facial recognition systems that perform poorly for women with darker skin tones, health studies that overlook symptoms outside a narrow reference group, or policy analyses that work “on average” but miss those most affected. Such outcomes are rarely the result of bad intentions. More often, they arise from data practices that treat social characteristics—such as gender, ethnicity, class, age, or disability—in isolation or ignore them altogether. Intersectionality helps make visible how these characteristics intersect, shaping who appears in data, whose experiences are treated as the norm, and where systematic blind spots emerge. At the same time, it opens up possibilities for asking more precise questions and interpreting data in more reflective and socially aware ways.
This 5-hour in-person workshop offers an introductory and practice-oriented overview of Intersectional Data Science. Participants will become familiar with how bias can emerge at different stages of the data process—from study design and data collection to analysis and interpretation—and how an intersectional approach can be used to critically reflect on these stages. The workshop introduces core concepts, typical challenges, and selected analytical strategies used in intersectional research, without assuming prior expertise in intersectionality.

Participants will learn about conceptual and operational considerations when designing intersectional studies, including how research questions can be translated into feasible data structures. The workshop also provides an overview of data protection and ethical considerations, particularly when working with sensitive characteristics and small or potentially identifiable subgroups. Through a combination of short lectures, interactive modules, and guided exercises, participants are invited to reflect on how intersectional perspectives could be integrated into their own data work.

The Speaker
Dr. Clemens Striebing
is a senior researcher at the Fraunhofer Institute for Industrial Engineering IAO in Berlin. He conducts research on organisational culture, equality and innovation-oriented diversity in the scientific system. His work combines empirical research, strategic consulting and teaching on diversity-sensitive technology design.

Zielgruppe
The workshop is aimed at researchers and practitioners who work with data and wish to broaden their methodological and analytical perspective. It does not aim to provide a comprehensive toolbox, but rather to raise awareness, clarify key principles, and offer orientation for engaging with intersectionality in data science in a reflective and responsible way.

Sprache
Englisch

Max. Anzahl Teilnehmende
15

Standort
Johannisstraße 13 – Auditorium Zur Rosen, Jena

Bitte melden Sie sich über das Qualifizierungs-PortalExterner Link der Uni Jena für die Veranstaltung an.