In this talk is offer three challenges for a critical data journalism practice drawing on the insights and examples from The Data Journalism Handbook: Towards a Critical Data Practice: https://www.aup.nl/en/book/9789462989511/the-data-journalism-handbook. The talk is a keynote given at the Digital Methods Initiative Summer School at the University of Amsterdam on 5 July 2021.

1. Towards a critical data journalism practice
Liliana Bounegru
@bb_liliana / lilianabounegru.org
Lecturer in Digital Methods
King’s College London

6. “Across the world journalists were discovering new ways to work by telling
data-led stories in innovative ways. … This should not be seen as an isolated
development within the field of journalism. These were just the effects of
huge developments in international transparency beyond the setting up of
open data portals. … They also included increased access to powerful free
data visualization and cleaning tools, such as OpenRefine, Google Fusion
Tables, Many Eyes, Datawrapper, Tableau Public and more. Those free tools
combined with access to a lot of free public data facilitated the production
of more and more public-facing visualizations and data projects.”
– Rogers, Simon. 2021. “From The Guardian to Google News Lab: A Decade of Working
in Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.

8. Three challenges for
critical data journalism practice

10. 1. Make stories both with and about data
2. Align with marginalised issues and actors
3. Cultivate reflexive ways of telling

11. 1. Make stories both with and about data
2. Align with marginalised issues and actors
3. Cultivate reflexive ways of telling

12. “… the crucial role of data journalists as users and
critics of data.”
– Emmanuel Didier, Ecole normale supérieure; author of America by the Numbers:
Quantification, Democracy, and the Birth of National Statistics

13. “Taking a feminist approach to data journalism means
tuning in to the ways in which inequality enters
databases and algorithms, as well as developing
strategies to mitigate those biases.”
–D’Ignazio, Catherine. 2021. “Data Journalism: What’s Feminism Got to Do With
I.T.?” The Data Journalism Handbook: Towards A Critical Data Practice.

14. “… data journalism can serve not just to reinforce and
reify dominant regimes of datafication – or ways of
rendering life into data (van Dijck, 2014) – but also to
interrogate them and make space for public
involvement and intervention around data
infrastructures.”
– Gray, J., & Bounegru, L. (2019). What a difference a dataset makes? Data journalism
and/as data activism. In J. Evans, S. Ruane, & H. Southall (Eds.), Data in Society:
Challenging Statistics in an Age of Globalisation.

15. Algorithmic accountability reporting

16. “… a re-orientation of the traditional watchdog
function of journalism towards the power wielded
through algorithms (Diakopoulos, 2015).”
–Diakopoulos, Nicholas. 2021. “The Algorithms Beat: Angles and Methods for
Investigation.” The Data Journalism Handbook: Towards A Critical Data Practice.

19. “… at least four driving forces … appear to underlie
many algorithmic accountability stories: (a)
discrimination and unfairness, (b) errors or mistakes in
predictions or classifications, (c) legal or social norm
violations, and (d) misuse of algorithms by people
either intentionally or inadvertently.”
–Diakopoulos, Nicholas. 2021. “The Algorithms Beat: Angles and Methods for
Investigation.” The Data Journalism Handbook: Towards A Critical Data Practice.

20. Reconfiguring infrastructures for
making data journalism

22. “… There are consequences to having very few actors running
such platforms and large numbers of journalists depending on
them in the cross-border journalism realm. One of these could
be understood as what in the landscape of ‘big tech’ has been
called a 'hyper-modern form of feudalism’ based on data
ownership (Morozov, 2016). This concept draws attention to
how total control of users’ data and interactions is placed in the
hands of a few companies who face no competition.”
– Cândea, Ştefan. 2021 “Data Feudalism: How Platforms Shape Cross-Border
Investigative Networks.” The Data Journalism Handbook: Towards A Critical Data
Practice.

24. How can data journalism projects tell stories both with and about data
including the various actors, processes, institutions, infrastructures and forms of
knowledge through which data is made?
How can data journalism projects account for the collective character of digital
data, platforms, algorithms and online devices, including the interplay
between digital technologies and digital cultures?
How can data journalism projects cultivate their own ways of making things
intelligible, meaningful and relatable through data, without simply uncritically
advancing the ways of knowing “baked into” data from dominant institutions,
infrastructures and practices?
1. Make stories both with and about data

25. 1. Make stories both with and about data
2. Align with marginalised issues and actors
3. Cultivate reflexive ways of telling

26. Data journalism by whom, for whom
and in whose interests?

27. “… the role of journalism in maintaining social orders
that support state aims and goals and structures and
ideologies such as patriarchy, settler colonialism and
White supremacy (Callison & Young, 2020).”
– Young, Mary Lynn, and Candis Callison. 2021. “Data Journalism: In Whose
Interests?” The Data Journalism Handbook: Towards A Critical Data Practice.

28. “Indigenous peoples have also been subject to contending with
extensive anthropological and government archives and
consistent media misrepresentations and stereotypes (Anderson
& Robertson, 2011) in the service of varied forms and histories
of settler colonialism (Tuck & Yang, 2012; Wolfe, 2006). Hence,
the stakes for data journalism specifically as an extension of
notions of machine-based objectivity are profound.”
– Young, Mary Lynn, and Candis Callison. 2021. “Data Journalism: In Whose
Interests?” The Data Journalism Handbook: Towards A Critical Data Practice.

29. “Walter (2016) has termed these data 5D data: Data that focus
on Difference, Disparity, Disadvantage, Dysfunction and
Deprivation.”
– Kukutai, Tahu, and Maggie Walter. 2021. “Indigenous Data Sovereignty: Implications
for Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.

30. “Indigenous data deserts”
– Kukutai, Tahu, and Maggie Walter. 2021. “Indigenous Data Sovereignty: Implications
for Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.

31. “Laguna Pueblo journalist Jenni Monet (2020)
characterizes Indigenous communities in the United
States as ‘Asterisk nations,’ which are those for whom
no data exists.”
– Young, Mary Lynn, and Candis Callison. 2021. “Data Journalism: In Whose
Interests?” The Data Journalism Handbook: Towards A Critical Data Practice.

32. “… ID-SOV [Indigenous data sovereignty] as an
emerging site of science and activism.”
– Kukutai, Tahu, and Maggie Walter. 2021. “Indigenous Data Sovereignty: Implications
for Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.

34. “ID-SOV research and networks … represent valuable
sources of data and data expertise that can inform
more equitable, critical and just approaches to
journalism involving Indigenous peoples and issues.”
– Kukutai, Tahu, and Maggie Walter. 2021. “Indigenous Data Sovereignty: Implications
for Data Journalism.” The Data Journalism Handbook: Towards A Critical Data Practice.

36. – Ma, Yolanda Jinxin. 2021. “Alternative Data Practices in China.” The Data Journalism
Handbook: Towards A Critical Data Practice.

37. How can data journalism projects make space for public participation and
intervention in interrogating established data sources and re-imagining which
issues are accounted for through data, and how?
How can data journalism projects collaborate around transnational issues in
ways which avoid the logic of the platform and the colony, and affirm
innovations at the periphery?
How can data journalism support marginalized communities to use data to tell
their own stories on their own terms, rather than telling their stories for them?
2. Align with marginalised issues and actors

38. 1. Make stories both with and about data
2. Align with marginalised issues and actors
3. Cultivate reflexive ways of telling

39. “An empirically self-assured profession.”
– Anderson, C. W. 2021. “Genealogies of Data Journalism.” The Data Journalism
Handbook: Towards A Critical Data Practice.

40. “Epistemologically, there is an increasing belief
amongst computational journalists that digital facts in
some way ‘speak for themselves,’ or at least these
facts will do so when they have been properly
collected, sorted and cleaned.”
– Anderson, C. W. 2021. “Genealogies of Data Journalism.” The Data Journalism
Handbook: Towards A Critical Data Practice.

41. “ … [a genealogy of data journalism] would prompt a
useful form of critical self-reflexivity, one that might
help mitigate the (understandable and often well-
deserved) self-confidence of working data journalists
and reporters.”
– Anderson, C. W. 2021. “Genealogies of Data Journalism.” The Data Journalism
Handbook: Towards A Critical Data Practice.

42. Provisionality and uncertainty

43. “… journalism ought to rethink the means and
mechanisms by which it conveys its own provisionality
and uncertainty.”
– Anderson, C. W. 2021. “Genealogies of Data Journalism.” The Data Journalism
Handbook: Towards A Critical Data Practice.

44. “In particular, data journalists might think harder
about how to creatively represent uncertainty in their
empirical work.”
– Anderson, C. W. 2021. “Genealogies of Data Journalism.” The Data Journalism
Handbook: Towards A Critical Data Practice.

45. “As I started to do them [sketches with data] I had this realization that they could be quite an
effective way to communicate the uncertainty of data projects. They could remind people that a
human was responsible for making all of these design decisions.”
– Chalabi, Mona, and Jonathan Gray. 2021. “Sketching With Data.” The Data Journalism Handbook: Towards A
Critical Data Practice.

46. “It is not really perfect: To fit all of the rhinos in the scale is a little bit questionable I would say
… But it makes you feel something about the numbers. And it is also transparent about its
shortcomings. … When readers look at the illustrations of the endangered species they can
look at the rhinos and think, ‘It is a little bit off but I get it.’ They have access to that critique in
a way that they don’t with computer generated graphics.”
– Chalabi, Mona, and Jonathan Gray. 2021. “Sketching With Data.” The Data Journalism Handbook: Towards A
Critical Data Practice.

47. The role of emotions

48. “Part of the beauty of data visualization is that it can make things feel more visceral.”
– Chalabi, Mona, and Jonathan Gray. 2021. “Sketching With Data.” The Data Journalism Handbook: Towards A
Critical Data Practice.

49. “… a major finding from our research was the
important role that emotions play in people’s
engagements with data visualizations.”
– Kennedy, Helen, et al. 2021. “Data Visualizations: Newsroom Trends and Everyday
Engagements.” The Data Journalism Handbook: Towards A Critical Data Practice.

50. “First, analytics dashboards have important emotional
dimensions that are too often overlooked. … The power and
appeal of metrics are significantly grounded in the data’s
ability to elicit particular feelings, such as excitement,
disappointment, validation and reassurance.”
– Petre, Caitlin. 2021. “Data-Driven Editorial?: Considerations for Working With
Audience Metrics.” The Data Journalism Handbook: Towards A Critical Data Practice.

51. “Chartbeat knew that this emotional valence was a powerful part of the dashboard’s appeal,
and the company included features to engender emotions in users. For instance, the dashboard
was designed to communicate deference to journalistic judgement, cushion the blow of low
traffic and provide opportunities for celebration in newsrooms.”
– Petre, Caitlin. 2021. “Data-Driven Editorial?: Considerations for Working With Audience Metrics.” The Data
Journalism Handbook: Towards A Critical Data Practice.

52. “As with every other communications medium,
leveraging emotion in data comes with ethical
responsibilities.”
– D’Ignazio, Catherine. 2021. “Data Journalism: What’s Feminism Got to Do With
I.T.?” The Data Journalism Handbook: Towards A Critical Data Practice.

53. Plurality and situatedness

54. “data journalists critical of digital universalist
frameworks should aim … to consciously diversify data
sources and decentre methods that would privilege
‘big data’ as the exclusive or most legitimate key to
mapping empirical events and social realities.”
– Chan, Anita Say. 2021. “Data Journalism, Digital Universalism and Innovation in the
Periphery.” The Data Journalism Handbook: Towards A Critical Data Practice.

55. “Moves towards a ‘decolonization of knowledge’ underscore
the significance of the diverse ways through which citizens and
researchers in the Global South are engaging in bottom-up
data practices. These practices leverage an emphasis on
community practices and human-centred means of assessing
and interpreting data—for social change, as well as speaking
for the resistances to uses of big data that increase oppression,
inequality and social harm.”
– Chan, Anita Say. 2021. “Data Journalism, Digital Universalism and Innovation in the
Periphery.” The Data Journalism Handbook: Towards A Critical Data Practice.

56. “Data journalists critical of digital universalism’s new extensions
in data universalism should take heart to find allies and
resonant concerns for developing accountable and responsible
data practices with scholars in critical data studies, algorithm
studies, software and platform studies, and postcolonial
computing.”
– Chan, Anita Say. 2021. “Data Journalism, Digital Universalism and Innovation in the
Periphery.” The Data Journalism Handbook: Towards A Critical Data Practice.

57. – Muñoz, Eliana A. Vaca. 2021. “Organizing Data Projects With Women and Minorities
in Latin America.” The Data Journalism Handbook: Towards A Critical Data Practice.

58. How might data journalists cultivate and consciously affirm their own styles of working
with data, which may draw on, yet remain distinct from areas such as statistics, data
science and social media analytics?
How might data journalism develop a style of objectivity which affirms, rather than
minimizes, its own role in intervening in the world and in shaping relations between
different actors in collective life?
How can data journalism projects tell stories about big issues at scale (e.g., climate
change, inequality, multinational taxation, migration) while also affirming the
provisionality and acknowledging the models, assumptions and uncertainty involved
in the production of numbers?
3. Cultivate reflexive ways of telling

59. 1. Make stories both with and about data
2. Align with marginalised issues and actors
3. Cultivate reflexive ways of telling

60. Thank you!
Liliana Bounegru
@bb_liliana / lilianabounegru.org
Lecturer in Digital Methods
King’s College London