82 AI MAGAZINE
The general consensus of participants was that, due
to different factors such as the increasing pervasive-
ness of social media, and the availability of advanced
tools and methods to model and analyze complex
networks, modeling and mining social-media-driven
complex networks is a continuously evolving subject
that deserved continued discussion and research.
Roberto Interdonato (The French Agricultural Research Centre for International Development [CIRAD]),
Sabrina Gaito (Università degli Studi di Milano),
Alessandra Sala (Nokia Bell Labs), and Andrea Tagarelli
(University of Calabria) served as co-chairs of the
Critical Data Science
Critical data science is defined as the practice of working with and modeling data (the data science), combined with identifying and questioning the core
assumptions that drive that practice (the critical
aspect). It can be regarded as the intersection (or perhaps the union) of data science and critical data or
algorithm studies, and an example of a critical technical practice. The objective of the workshop was to
create a prime space where scholars from different
backgrounds can meet and discuss how to do data
science in responsible, sustainable, and interdisciplinary settings.
The workshop included short presentations by
participants to support reflection of their own and
neighboring scientific practices, and to create opportunities for further cooperation. Participants covered
a broad range of backgrounds: industry data science
and engineering, computer science, computational
social science, linguistics, classics, environmental and
human rights activism, social work, digital democracy, and the arts. The workshop was guided by two
blocks of questions: politics and practice.
For politics, the questions included: What are
our experiences of paradigmatic politics? Who are
the insiders, and who are the outsiders for effecting
change? Do we feel capable of intervening in curricular decision-making, and can we disrupt dominant
narratives of big data hegemony, efficiency, and objectivity? What does it mean to do data science for
good, and for whom? What would be my personal
priorities: short-term, and long-term?
For practice, the questions included: What concrete
actions can we take? How can we create spaces and
time for collaboration besides always-hectic, project-based logics? Which incentive and reward structures
would we need for that? Which skills do we want to
establish in the training of the next generation? How
can I/we collaborate? With whom? For what tasks?
Workshop presentations and discussions both
delved into how we can change our socio-technical
practices. When several computer scientists remem-
bered specific biographical aspects or experiences that
led them, unlike many technical practitioners, to be
open to nontechnical perspectives, we learned that
critical technical practice requires deeply personal
involvement with scientific routines. For many, it was
personal connections or commitments to political
projects that led them to start questioning claims of
objectivity, neutrality, and universality, with these
aspects commonly leaving little room for reflec-
tion. On the other hand, from the broad range of
presentations we learned that hybrid approaches —
including social scientific analysis, as well as elements
of engagement of participation — are indeed pos-
sible. Furthermore, there are already communities
growing like the Association for Computing Machin-
ery FAT* conference, or critical data and algorithm
studies, surveillance studies, science and technology
studies, human interface design and data activism;
and a number of projects presented at the workshop
that remain technical products or analyses perfectly
capture these approaches, because they are already
overcoming the under-theorized pragmatism that
drives so much of software engineering and data anal-
The workshop concluded with a set of ideas and
priorities on how to design critical technical practice in data science. The ideas and priorities included
systematic reflection, participatory action research,
team building, publishing venues, linking sectors,
education, institution building, documentation, ethical principles, and funding. Katja Mayer and Momin
M. Malik served as co-chairs of this workshop.
Diego Alburez-Gutierrez is a research scientist at the Max
Planck Institute for Demographic Research.
Eshwar Chandrasekharan is a PhD candidate in computer
science at the Georgia Institute of Technology.
Rumi Chunara is an assistant professor at New York
Sofia Gil-Clavel is a PhD student at the Max Planck Institute for Demographic Research.
Aniko Hannak is an assistant professor at the Vienna University of Economics and Business.
Roberto Interdonato is a research scientist at The French
Agricultural Research Centre for International Development
(CIRAD), Unit of Mixed Research TETIS, Montpellier, France.
Kenneth Joseph is an assistant professor at the University
at Buffalo, State University of New York.
Kyriaki Kalimeri is a researcher at the Institute for Scientific Interchange (ISI), Turin, Italy.
Sanjay Kairam is a research scientist at Twitch Interactive.
Momin M. Malik is a postdoctoral fellow at Harvard
Katja Mayer is the Elise Richter fellow at the University of
Yelena Mejova is a research leader at the Institute for Scientific Interchange (ISI), Turin, Italy.
Daniela Paolotti is a research leader at the Institute for Scientific Interchange (ISI), Turin, Italy.
Emilio Zagheni is director of the Max Planck Institute for