Guest Blog: The 'doings' behind the data

by Isaelle Donatz-Fest

Donatz-Fest, I. (2024). The ‘doings’ behind data: An ethnography of police data construction. Big Data & Society, 11(3). https://doi.org/10.1177/20539517241270695

Data are the lifeblood of algorithmic systems. But data are often taken for granted by public organizations who see them as something just lying around, ready to use. Such is the case with police reports, which are increasingly used as data for algorithmic applications for policing worldwide.

But there are ‘doings’ behind data. Data are created in unexpected places—like the front seat of a speeding police car or the desk of an overworked detective. Material factors and human actors interact behind-the-scenes, informing data creation and interpretation.

I spent ~200 hours (ethnographically) observing how street-level employees at the Netherlands Police translate events to police reports. What I found was that data work is deeply embedded in policing, shaped by personal values, organizational context, and practical considerations. 

Structured data often clashes with the officers' understanding of a situation. Registration software demands incidents are fit into predefined categories, but the messy world that we live in rarely fits neatly into such boxes. Unstructured data provides more flexibility and richness but introduces complexities for standardization and (algorithmic) interpretation. Open text fields open the door to linguistic nuances, inconsistencies, and what I term 'voice,' the various identities present in the text.

I saw officers wrestle with these limitations, sometimes bending rules, sometimes choosing the path of least resistance. These choices reflect officer values and the pressures they face. Whether it’s a commitment to justice, a desire to help a colleague, or the need to quickly move on to the next call, the context impacts the data directly.

This work offers new empirical insight on the data underpinning public sector algorithms. By understanding the doings behind data, we can begin to question how we use them in algorithmic systems, which is particularly relevant in fields as impactful and powerful as policing. 

Guest Video: Anders Kristian Munk, Mathieu Jacomy, and Matilde Ficozzi about their paper "Beyond artificial intelligence controversies: What are algorithms doing in the scientific literature?"

 https://orcid.org/0000-0002-5542-3065

https://orcid.org/0000-0002-6417-6895

Guest Blog: Problem-solving? No, problem-opening! A Method to Reframe and Teach Data Ethics as a Transdisciplinary Endeavour

by Stefano Calzati and Hendrik Ploeger

Calzati, S., & Ploeger, H. (2024). Problem-solving? No, problem-opening! A method to reframe and teach data ethics as a transdisciplinary endeavour. Big Data & Society, 11(3). https://doi.org/10.1177/20539517241270687

Technology can go a long way in “chopping up” reality and reifying resources – and data are no exception to that. The thingness of data – just think of the refrain “data as the new oil” – is often considered as a given, i.e., a datum. Yet a growing body of research has shown that data are inherently sociotechnical, leading to regard them as bundlings originating from processes at the coalescing point of technical and non-technical actors, factors, and values. So, the questions become: How to operationalize this? How, for instance, to teach new generations of undergraduates being trained in computer science, data analytics, software engineering, and similar technical subjects, that data are sociotechnical bundlings? How to incorporate such understanding into their practices? 

In the article “Problem-solving? No, problem-opening! A Method to Reframe and Teach Data Ethics as a Transdisciplinary Endeavour” we set out to answer these questions. First, we reconceptualize data ethics as not much a normative (dos vs don’ts) and axiomatic (good vs bad) toolbox, but a critical compass to think about data as sociotechnical bundlings and orient their fair processing. This, in turn, entails that data technologies are always good and bad at once, insofar as they produce, at all times, value-laden entanglements and un/intended consequences that demand to be unpacked and assessed in context, i.e., from different perspectives, simultaneously, and over time. This is an inherent transdisciplinary endeavor which cuts across epistemological boundaries, resists any privilege point of reference, and configures an ongoing multidimensional analysis. 

What we describe in detail in the article is the application of this view to an elective course titled “Ethics for the data-driven city” which we purposedly designed and taught as part of the Geomatics master program at Delft University of Technology. Notably, we developed a transdisciplinary method that is not problem-solving, but problem-opening, that is, a method that help students recognize and problematize the irreducibility of all ethical stances and the contingency of all technological “solutions”, especially when these are situated in the city as a complex system that resists computation. Overall, the course compels students, on the one hand, to think critically about (the definition of) problems, by shifting the ground on which engineering problem-solving rests, and, on the other hand, to materialize such critical shift into their final assignments, conceived in the form of digital or physical artefacts.

BD&S Journal will be on break from Aug 4th to Sept 4th, 2024

 The editorial team of the journal Big Data & Society will be on break from August 4th to September 4th 2024.  Please accept any delays in processing and reviewing your submission, and in related correspondence during that time. Thank you!

Guest Blog: Jascha Bareis on Trust and AI

by Jascha Bareis

Bareis, J. (2024). The trustification of AI. Disclosing the bridging pillars that tie trust and AI together. Big Data & Society, 11(2). https://doi.org/10.1177/20539517241249430

Does it make sense to tie trust with AI?

Everywhere we look: companies, politics, and research: so many people are focusing on AI. Being approached as the core technological institution of our times, notions of trust are repeatedly mobilized. Especially policy makers seem to feel urged to highlight the need of trustworthiness in relation to AI. Take the current European AI act that claims the EU to be a “leader in the uptake of trustworthy AI” (AIA, Article 1a), or the US 2023 US executive order on “Safe, Secure and Trustworthy AI”.

I simply asked myself: Despite all this attention, is it at all legitimate to marry a technology with an intersubjective phenomenon that used to be reserved between humans only? I can trust my neighbor next door taking care of my cat, but can I trust the TESLA Smart Cat Toilet automating cleaning cycle to take care of its poo-poo, too (indeed, the process of gadget’ification and smartification does not spare cat toilets)?

Does it make sense at all to talk about trust in the latter case or are we just dealing with a conceptual misfit? Doing more research, I realized that the way trust is handled in both the policy and academic AI debate is very sloppy, staying undertheorized and just somehow taken for granted.

I noticed that users approach trust and AI as something intersubjective, expecting great things from their new AI powered gadget and then being utterly disappointed if it fails to do so (because even if branded as “smart”, there is actually no AI in the TESLA Smart Cat Toilet). Users perceive AI as something being highly mediated by powerful actors, as when Elon Musk trusts that AI will be the cure to the world’s problems, many people seem to follow blindly (but do they trust AI then, or Elon?). And as something that can mobilize greater political dimensions and strong sentiments. As when my friend told me that she would certainly distrust AI because she distrusted the integrity of EU politicians who instead of regulating it, let Big Tech get “greedy and rich”.

Communication, mediation, sentiments, expectations, power, misconceptions – all of this seemed to have a say in the relationship between AI and trust. This created a messy picture with AI and trust being enmeshed in a social complex interplay with overlapping epistemic realms and actors.

As a consequence, I set out to problematize this relationship in this paper. I argue that trust is located in the social. And only if one acknowledges that AI is a very social phenomenon as well, this relationship makes sense at all. AI produces notions of trust and distrust because it is woven and negotiated in the everyday realities of users and society, with AI applications mediating human relationships, producing intimacies, social orders and knowledge authorities. I came up with the following analytical scheme.

I run through the scheme in the paper and describe its value and limitations, rendering trustworthy AI as a constant and complex dynamic between the actual technological developments and the social realities, political power struggles and futures that are associated with it.

Call for Special Theme Proposals for Big Data & Society (Due August 15, 2024)

 Call for Special Theme Proposals for Big Data & Society

The SAGE open access journal Big Data & Society (BD&S) is soliciting proposals for a Special Theme to be published in 2025/26. BD&S is a peer-reviewed, interdisciplinary, scholarly journal that publishes interdisciplinary social science research about the emerging field of Big Data practices and how they are reconfiguring relations, expertise, methods, concepts and knowledge across academic, social, cultural,  political, and economic realms. BD&S moves beyond usual notions of Big Data to engage with an emerging field of practices that is not defined by but generative of (sometimes) novel data qualities such as extensiveness, granularity, automation, and complex analytics including data linking and mining. The journal attends to digital content generated through online and offline practices, including social media, search engines, Internet of Things devices, and digital infrastructures across closed and open networks, from commercial and government transactions to digital archives, open government and crowd-sourced data. Rather than settling on a definition of Big Data, the Journal makes this an area of interdisciplinary inquiry and debate explored through multiple disciplines and themes.

Special Themes can consist of a combination of Original Research Articles (6 maximum, up to 10,000 words each), Commentaries (4 maximum, 3,000 words each) and one Editorial Introduction (3,000 words). All Special Theme content will have the Article Processing Charges waived. All submissions will go through the Journal’s standard peer review process.

While open to submissions on any theme related to Big Data we particularly welcome proposals related to Big Data and the Global South / Global Majority; Indigenous data and data sovereignty; queer and trans data; and Big Data and racialization. You can find the full list of special themes published by BD&S at http://journals.sagepub.com/page/bds/collections/index 

Format of Special Theme Proposals

Researchers interested in proposing a Special Theme should submit an outline with the following information.

 

• An overview of the proposed theme, including how it relates to existing research and the aims and scope of the Journal, and the ways it seeks to expand critical scholarly research on Big Data.

• A list of titles, abstracts, authors and brief biographies. For each, the type of submission (ORA, Commentary) should also be indicated. If the proposal is the result of a workshop or conference that should also be indicated.

• Short bios of the Guest Editors including affiliations and previous work in the field of Big Data studies. Links to homepages, Google Scholar profiles or CVs are welcome, although we don’t require CV submissions.

• A proposed timing for submission to Manuscript Central. This should be in line with the timeline outlined below.

 

Information on the types of submissions published by the Journal and other guidelines is available at https://journals.sagepub.com/author-instructions/BDS  .

 

Timeline for Proposals

Submit proposals by August 15, 2024 via this online form https://forms.gle/VQZM4eSRZDAYgLvY9  

(Note: you must have a Google account in order to access this form). Do not send proposals via email as they will not be reviewed. The Editorial Team of BD&S will review proposals and make a decision by October 2024. Manuscripts would be submitted to the journal (via manuscript central) by or before February 2025. 

For further information or discuss potential themes please contact Dr. Matthew Zook at zook@uky.edu.

 

Guest Blog: Data as environment, environment as data. One Health in collaborative data-intensive science

by Lucilla Barchetta (@lucilla_ba)

One Health considers health as interdependent across humans and non-humans. Its development is thus necessarily interdisciplinary, posing a number of challenges in ontoepistemic terms. Increasingly, technology and big data are seen as the solution to these challenges, allowing to aggregate heterogenous data. This article’s argument is that technology does not eliminate the need to agree on shared epistemological premises across disciplines, and this takes time and efforts of social coordination. 

Drawing insights from ethnographic field research, the article introduces the concept of ‘data as environment.’ Data isn’t just about bits and bytes; it’s about an interconnected structure that weaves together knowledge systems, individuals, processing tools, and bio-social interactions in the complex tapestry of data-intensive knowledge co-production. This environment becomes a contact structure, entangling not just data, but the very essence of collaborative efforts across biomedical, environmental, and social sciences. 

The concept of ‘data as environment’ is different from previous similar concepts such as “data infrastructures,” “data ecosystems,” “data communities,” “data journeys,” “data collaboratives” and “data cultures” because it does not posit something called “data” as distinguished from something called “environment”. Instead, we conceive data itself as an environment, a structure of contact that emerges in putting into relationship various elements, activated by use, maintenance and slippages. We, thus, address the mainstream criticism that data are self-contained bits, enclosed and autonomous objects, extracted and abstracted from the flux of becoming. 

The article makes a compelling point by unravelling political-ethical questions embedded in the emerging technoscientific worlds of the Anthropocene. It goes beyond the surface of data, prompting us to ponder the ethical implications and societal dimensions of the evolving landscape of data-intensive science. 

In essence, this article beckons us to embrace a holistic understanding of research – where data is not just information but an environment that shapes and is shaped by the collaborative efforts of diverse scientific subjectivities and entities. As we navigate the complexities of the Anthropocene, this exploration of 'data as environment' becomes a crucial lens through which we can better comprehend the dynamic interplay between data, knowledge, and the evolving landscape of interdisciplinary collaboration.