Pub Date : 2023-07-01DOI: 10.1177/20539517231180575
Mareike Bauer, Maximilian Heimstädt, Carlos Franzreb, Sonja Schimmler
Many scientists share preprints on social media platforms to gain attention from academic peers, policy-makers, and journalists. In this study we shed light on an unintended but highly consequential effect of sharing preprints: Their contribution to conspiracy theories. Although the scientific community might quickly dismiss a preprint as insubstantial and ‘clickbaity’, its uncertain epistemic status nevertheless allows conspiracy theorists to mobilize the text as scientific support for their own narratives. To better understand the epistemic politics of preprints on social media platforms, we studied the case of a biomedical preprint, which was shared widely and discussed controversially on Twitter in the wake of the coronavirus disease 2019 pandemic. Using a combination of social network analysis and qualitative content analysis, we compared the structures of engagement with the preprint and the discursive practices of scientists and conspiracy theorists. We found that despite substantial engagement, scientists were unable to dampen the conspiracy theorists’ enthusiasm for the preprint. We further found that members from both groups not only tried to reduce the preprint's epistemic uncertainty but sometimes deliberately maintained it. The maintenance of epistemic uncertainty helped conspiracy theorists to reinforce their group's identity as skeptics and allowed scientists to express concerns with the state of their profession. Our study contributes to research on the intricate relations between scientific knowledge and conspiracy theories online, as well as the role of social media platforms for new genres of scholarly communication.
{"title":"Clickbait or conspiracy? How Twitter users address the epistemic uncertainty of a controversial preprint","authors":"Mareike Bauer, Maximilian Heimstädt, Carlos Franzreb, Sonja Schimmler","doi":"10.1177/20539517231180575","DOIUrl":"https://doi.org/10.1177/20539517231180575","url":null,"abstract":"Many scientists share preprints on social media platforms to gain attention from academic peers, policy-makers, and journalists. In this study we shed light on an unintended but highly consequential effect of sharing preprints: Their contribution to conspiracy theories. Although the scientific community might quickly dismiss a preprint as insubstantial and ‘clickbaity’, its uncertain epistemic status nevertheless allows conspiracy theorists to mobilize the text as scientific support for their own narratives. To better understand the epistemic politics of preprints on social media platforms, we studied the case of a biomedical preprint, which was shared widely and discussed controversially on Twitter in the wake of the coronavirus disease 2019 pandemic. Using a combination of social network analysis and qualitative content analysis, we compared the structures of engagement with the preprint and the discursive practices of scientists and conspiracy theorists. We found that despite substantial engagement, scientists were unable to dampen the conspiracy theorists’ enthusiasm for the preprint. We further found that members from both groups not only tried to reduce the preprint's epistemic uncertainty but sometimes deliberately maintained it. The maintenance of epistemic uncertainty helped conspiracy theorists to reinforce their group's identity as skeptics and allowed scientists to express concerns with the state of their profession. Our study contributes to research on the intricate relations between scientific knowledge and conspiracy theories online, as well as the role of social media platforms for new genres of scholarly communication.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136260355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231210273
Liam Magee, Vanicka Arora, Luke Munn
Drawing from the resources of psychoanalysis and critical media studies, in this article we develop an analysis of large language models (LLMs) as ‘automated subjects’. We argue the intentional fictional projection of subjectivity onto LLMs can yield an alternate frame through which artificial intelligence (AI) behaviour, including its productions of bias and harm, can be analysed. First, we introduce language models, discuss their significance and risks, and outline our case for interpreting model design and outputs with support from psychoanalytic concepts. We trace a brief history of language models, culminating with the releases, in 2022, of systems that realise ‘state-of-the-art’ natural language processing performance. We engage with one such system, OpenAI's InstructGPT, as a case study, detailing the layers of its construction and conducting exploratory and semi-structured interviews with chatbots. These interviews probe the model's moral imperatives to be ‘helpful’, ‘truthful’ and ‘harmless’ by design. The model acts, we argue, as the condensation of often competing social desires, articulated through the internet and harvested into training data, which must then be regulated and repressed. This foundational structure can however be redirected via prompting, so that the model comes to identify with, and transfer , its commitments to the immediate human subject before it. In turn, these automated productions of language can lead to the human subject projecting agency upon the model, effecting occasionally further forms of countertransference. We conclude that critical media methods and psychoanalytic theory together offer a productive frame for grasping the powerful new capacities of AI-driven language systems.
{"title":"Structured like a language model: Analysing AI as an automated subject","authors":"Liam Magee, Vanicka Arora, Luke Munn","doi":"10.1177/20539517231210273","DOIUrl":"https://doi.org/10.1177/20539517231210273","url":null,"abstract":"Drawing from the resources of psychoanalysis and critical media studies, in this article we develop an analysis of large language models (LLMs) as ‘automated subjects’. We argue the intentional fictional projection of subjectivity onto LLMs can yield an alternate frame through which artificial intelligence (AI) behaviour, including its productions of bias and harm, can be analysed. First, we introduce language models, discuss their significance and risks, and outline our case for interpreting model design and outputs with support from psychoanalytic concepts. We trace a brief history of language models, culminating with the releases, in 2022, of systems that realise ‘state-of-the-art’ natural language processing performance. We engage with one such system, OpenAI's InstructGPT, as a case study, detailing the layers of its construction and conducting exploratory and semi-structured interviews with chatbots. These interviews probe the model's moral imperatives to be ‘helpful’, ‘truthful’ and ‘harmless’ by design. The model acts, we argue, as the condensation of often competing social desires, articulated through the internet and harvested into training data, which must then be regulated and repressed. This foundational structure can however be redirected via prompting, so that the model comes to identify with, and transfer , its commitments to the immediate human subject before it. In turn, these automated productions of language can lead to the human subject projecting agency upon the model, effecting occasionally further forms of countertransference. We conclude that critical media methods and psychoanalytic theory together offer a productive frame for grasping the powerful new capacities of AI-driven language systems.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135856836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231206794
Lucy Suchman
This commentary starts with the question ‘How is it that AI has come to be figured uncontroversially as a thing, however many controversies “it” may engender?’ Addressing this question takes us to knowledge practices that philosopher of science Helen Verran has named a ‘hardening of the categories’, processes that not only characterise the onto-epistemology of AI but also are central to its constituent techniques and technologies. In a context where the stabilization of AI as a figure enables further investments in associated techniques and technologies, AI's status as controversial works to reiterate both its ontological status and its agency. It follows that interventions into the field of AI controversies that fail to trouble and destabilise the figure of AI risk contributing to its uncontroversial reproduction. This is not to deny the proliferating data and compute-intensive techniques and technologies that travel under the sign of AI but rather to call for a keener focus on their locations, politics, material-semiotic specificity, and effects, including their ongoing enactment as a singular and controversial object.
{"title":"The uncontroversial ‘thingness’ of AI","authors":"Lucy Suchman","doi":"10.1177/20539517231206794","DOIUrl":"https://doi.org/10.1177/20539517231206794","url":null,"abstract":"This commentary starts with the question ‘How is it that AI has come to be figured uncontroversially as a thing, however many controversies “it” may engender?’ Addressing this question takes us to knowledge practices that philosopher of science Helen Verran has named a ‘hardening of the categories’, processes that not only characterise the onto-epistemology of AI but also are central to its constituent techniques and technologies. In a context where the stabilization of AI as a figure enables further investments in associated techniques and technologies, AI's status as controversial works to reiterate both its ontological status and its agency. It follows that interventions into the field of AI controversies that fail to trouble and destabilise the figure of AI risk contributing to its uncontroversial reproduction. This is not to deny the proliferating data and compute-intensive techniques and technologies that travel under the sign of AI but rather to call for a keener focus on their locations, politics, material-semiotic specificity, and effects, including their ongoing enactment as a singular and controversial object.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135856843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231206806
Christoffer Bagger, Arni Már Einarsson, Victoria Andelsman Alvarez, Maja Klausen, Stine Lomborg
This commentary calls for further research into digital resignation within non-market contexts, particularly in relation to the datafied welfare state, as distinct from commercial big tech platforms. We aim to nuance the concept of digital resignation by relating it to the digitization of institutions and public services upholding the Danish welfare state, including health services, childcare, and news consumption. These cases illustrate that datafication stimulates citizens’ discomfort by registering privacy-intrusive information and setting new standards for being a good citizen, which resignation research can help us understand. We use the case examples to propose new avenues for digital resignation research and question whether organizations, institutions, and governments themselves can be digitally resigned. As such, the usefulness of digital resignation as a concept can be expanded.
{"title":"Digital resignation and the datafied welfare state","authors":"Christoffer Bagger, Arni Már Einarsson, Victoria Andelsman Alvarez, Maja Klausen, Stine Lomborg","doi":"10.1177/20539517231206806","DOIUrl":"https://doi.org/10.1177/20539517231206806","url":null,"abstract":"This commentary calls for further research into digital resignation within non-market contexts, particularly in relation to the datafied welfare state, as distinct from commercial big tech platforms. We aim to nuance the concept of digital resignation by relating it to the digitization of institutions and public services upholding the Danish welfare state, including health services, childcare, and news consumption. These cases illustrate that datafication stimulates citizens’ discomfort by registering privacy-intrusive information and setting new standards for being a good citizen, which resignation research can help us understand. We use the case examples to propose new avenues for digital resignation research and question whether organizations, institutions, and governments themselves can be digitally resigned. As such, the usefulness of digital resignation as a concept can be expanded.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135856901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231210276
Heather Ford, Tamson Pietsch, Kelly Tall
Digital platforms produce bias and inequality that have a significant impact on peoples’ sense of self, agency and life chances. Wikipedia has largely evaded the criticism of other algorithmic systems like Google search and training databases like ImageNet, but Wikipedia is a critical source of representation in our current era – not only because it is one of the world's most popular websites, but because its data are being used as training data for the AI systems that are increasingly used for decision-making. We conducted an analysis of Wikipedia biographies in a national context, comparing the temporality and subjects of notability between English Wikipedia and the Australian Honours system in order to understand Wikipedia's unique role in the production of notability over the site's 20-year history. Framing Wikipedia as an active producer (rather than a reflection) of notability, we demonstrate that women are more likely to be awarded a Wikipedia page after the award announcements or not at all if their contribution is for labour relating to the caring professions than if their service is for sports, arts and films, politics or the judiciary. We argue that Wikipedia's inability to recognise gendered care work as noteworthy is mirrored in its own practices.
{"title":"Gender and the invisibility of care on Wikipedia","authors":"Heather Ford, Tamson Pietsch, Kelly Tall","doi":"10.1177/20539517231210276","DOIUrl":"https://doi.org/10.1177/20539517231210276","url":null,"abstract":"Digital platforms produce bias and inequality that have a significant impact on peoples’ sense of self, agency and life chances. Wikipedia has largely evaded the criticism of other algorithmic systems like Google search and training databases like ImageNet, but Wikipedia is a critical source of representation in our current era – not only because it is one of the world's most popular websites, but because its data are being used as training data for the AI systems that are increasingly used for decision-making. We conducted an analysis of Wikipedia biographies in a national context, comparing the temporality and subjects of notability between English Wikipedia and the Australian Honours system in order to understand Wikipedia's unique role in the production of notability over the site's 20-year history. Framing Wikipedia as an active producer (rather than a reflection) of notability, we demonstrate that women are more likely to be awarded a Wikipedia page after the award announcements or not at all if their contribution is for labour relating to the caring professions than if their service is for sports, arts and films, politics or the judiciary. We argue that Wikipedia's inability to recognise gendered care work as noteworthy is mirrored in its own practices.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231205479
Silvia Semenzin
One of the most intriguing discussions concerning blockchain technology revolves around its potential to ‘do good’. Consequently, numerous projects and institutions are showing interest in the capacity of blockchain to impact the social sphere positively. However, so far, very little literature has addressed the fundamental notion of ‘good’ that underlies its implementation or explores its connection to social justice theories. This article aims to analyse the narratives that surround the use of blockchain for social good and to compare them with traditional concepts that are significant in social justice theories, such as distribution and recognition. Results show that the selected informants involved in the blockchain scene tend to frame social good in rational, mathematical, and often competitive terms. This tendency contributes to the reinforcement of a neoliberal imaginary that neglects to address structural inequalities as relevant issues. Instead, it envisions social justice as an avenue for generating value, enhancing meritocracy, and ensuring technical accountability, echoing Silicon Valley's aspirations to ‘change the world’.
{"title":"‘Blockchain for good’: Exploring the notion of social good inside the blockchain scene","authors":"Silvia Semenzin","doi":"10.1177/20539517231205479","DOIUrl":"https://doi.org/10.1177/20539517231205479","url":null,"abstract":"One of the most intriguing discussions concerning blockchain technology revolves around its potential to ‘do good’. Consequently, numerous projects and institutions are showing interest in the capacity of blockchain to impact the social sphere positively. However, so far, very little literature has addressed the fundamental notion of ‘good’ that underlies its implementation or explores its connection to social justice theories. This article aims to analyse the narratives that surround the use of blockchain for social good and to compare them with traditional concepts that are significant in social justice theories, such as distribution and recognition. Results show that the selected informants involved in the blockchain scene tend to frame social good in rational, mathematical, and often competitive terms. This tendency contributes to the reinforcement of a neoliberal imaginary that neglects to address structural inequalities as relevant issues. Instead, it envisions social justice as an avenue for generating value, enhancing meritocracy, and ensuring technical accountability, echoing Silicon Valley's aspirations to ‘change the world’.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135855886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231210269
Netta Avnoon, Amalya L Oliver
This paper follows the reaction of the radiology profession to artificial intelligence (AI). We examine the effort of radiology as a powerful medical specialty to maintain its professional jurisdiction while allowing AI's disruption. We study the discursive work of radiologists as evident in their academic publications. Our results suggest that radiologists hold simultaneously multiple perspectives in regard to AI, which allow them to be both conservative and innovative in their relations to it: accept it, subordinate it, reject it and surrender to it, all the same time. These perspectives are: (a) to integrate AI tools and skills into the radiology profession by cooperating and coproducing with AI experts while preserving the core values and structures of the radiology profession; (b) to absorb AI into radiology as (yet another) technology, subordinating it to radiologists’ authority; (c) to fight-off the threat made by AI by undermining the legitimacy and capabilities of AI in radiology and strengthening professional boundaries and (d) to assimilate the radiology profession into the field of AI. These perspectives enable radiologists as a powerful medical specialty to engage in a rhetorical dance with the equally powerful AI specialty and challenge techno-optimistic approaches to innovation.
{"title":"Nothing new under the sun: Medical professional maintenance in the face of artificial intelligence's disruption","authors":"Netta Avnoon, Amalya L Oliver","doi":"10.1177/20539517231210269","DOIUrl":"https://doi.org/10.1177/20539517231210269","url":null,"abstract":"This paper follows the reaction of the radiology profession to artificial intelligence (AI). We examine the effort of radiology as a powerful medical specialty to maintain its professional jurisdiction while allowing AI's disruption. We study the discursive work of radiologists as evident in their academic publications. Our results suggest that radiologists hold simultaneously multiple perspectives in regard to AI, which allow them to be both conservative and innovative in their relations to it: accept it, subordinate it, reject it and surrender to it, all the same time. These perspectives are: (a) to integrate AI tools and skills into the radiology profession by cooperating and coproducing with AI experts while preserving the core values and structures of the radiology profession; (b) to absorb AI into radiology as (yet another) technology, subordinating it to radiologists’ authority; (c) to fight-off the threat made by AI by undermining the legitimacy and capabilities of AI in radiology and strengthening professional boundaries and (d) to assimilate the radiology profession into the field of AI. These perspectives enable radiologists as a powerful medical specialty to engage in a rhetorical dance with the equally powerful AI specialty and challenge techno-optimistic approaches to innovation.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231188725
Alexander Campolo, Katia Schwerzmann
This paper analyzes the effects of a perceived transition from a rule-based computer programming paradigm to an example-based paradigm associated with machine learning. While both paradigms coexist in practice, we critically discuss the distinctive epistemological and ethical implications of machine learning's “exemplary” type of authority. To capture its logic, we compare it to computer programming rules that date to the middle of the 20th century, showing how rules and examples have regulated human conduct in significantly different ways. In contrast to the highly constructed, explicit, and prescriptive form of authority imposed by programming rules, machine learning models are trained using data that has been made into examples. These examples elicit norms in an implicit, emergent manner to make prediction and classification possible. We analyze three ways that examples are produced in machine learning: labeling, feature engineering, and scaling. We use the phrase “artificial naturalism” to characterize the tensions of this type of authority, in which examples sit ambiguously between data and norm.
{"title":"From rules to examples: Machine learning's type of authority","authors":"Alexander Campolo, Katia Schwerzmann","doi":"10.1177/20539517231188725","DOIUrl":"https://doi.org/10.1177/20539517231188725","url":null,"abstract":"This paper analyzes the effects of a perceived transition from a rule-based computer programming paradigm to an example-based paradigm associated with machine learning. While both paradigms coexist in practice, we critically discuss the distinctive epistemological and ethical implications of machine learning's “exemplary” type of authority. To capture its logic, we compare it to computer programming rules that date to the middle of the 20th century, showing how rules and examples have regulated human conduct in significantly different ways. In contrast to the highly constructed, explicit, and prescriptive form of authority imposed by programming rules, machine learning models are trained using data that has been made into examples. These examples elicit norms in an implicit, emergent manner to make prediction and classification possible. We analyze three ways that examples are produced in machine learning: labeling, feature engineering, and scaling. We use the phrase “artificial naturalism” to characterize the tensions of this type of authority, in which examples sit ambiguously between data and norm.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135805389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231206802
Paula Helm, Amalia de Götzen, Luca Cernuzzi, Alethia Hume, Shyam Diwakar, Salvador Ruiz Correa, Daniel Gatica-Perez
The extractive logic of Big Data-driven technology and knowledge production has raised serious concerns. While most criticism initially focused on the impacts on Western societies, attention is now increasingly turning to the consequences for communities in the Global South. To date, debates have focused on private-sector activities. In this article, we start from the conviction that publicly funded knowledge and technology production must also be scrutinized for their potential neocolonial entanglements. To this end, we analyze the dynamics of collaboration in an European Union-funded research project that collects data for developing a social platform focused on diversity. The project includes pilot sites in China, Denmark, the United Kingdom, India, Italy, Mexico, Mongolia, and Paraguay. We present the experience at four field sites and reflect on the project’s initial conception, our collaboration, challenges, progress, and results. We then analyze the different experiences in comparison. We conclude that while we have succeeded in finding viable strategies to avoid contributing to the dynamics of unilateral data extraction as one side of the neocolonial circle, it has been infinitely more difficult to break through the much more subtle but no less powerful mechanisms of paternalism that we find to be prevalent in data-driven North–South relations. These mechanisms, however, can be identified as the other side of the neocolonial circle.
{"title":"Diversity and neocolonialism in Big Data research: Avoiding extractivism while struggling with paternalism","authors":"Paula Helm, Amalia de Götzen, Luca Cernuzzi, Alethia Hume, Shyam Diwakar, Salvador Ruiz Correa, Daniel Gatica-Perez","doi":"10.1177/20539517231206802","DOIUrl":"https://doi.org/10.1177/20539517231206802","url":null,"abstract":"The extractive logic of Big Data-driven technology and knowledge production has raised serious concerns. While most criticism initially focused on the impacts on Western societies, attention is now increasingly turning to the consequences for communities in the Global South. To date, debates have focused on private-sector activities. In this article, we start from the conviction that publicly funded knowledge and technology production must also be scrutinized for their potential neocolonial entanglements. To this end, we analyze the dynamics of collaboration in an European Union-funded research project that collects data for developing a social platform focused on diversity. The project includes pilot sites in China, Denmark, the United Kingdom, India, Italy, Mexico, Mongolia, and Paraguay. We present the experience at four field sites and reflect on the project’s initial conception, our collaboration, challenges, progress, and results. We then analyze the different experiences in comparison. We conclude that while we have succeeded in finding viable strategies to avoid contributing to the dynamics of unilateral data extraction as one side of the neocolonial circle, it has been infinitely more difficult to break through the much more subtle but no less powerful mechanisms of paternalism that we find to be prevalent in data-driven North–South relations. These mechanisms, however, can be identified as the other side of the neocolonial circle.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135856204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231203665
Lee McGuigan, Sarah Myers West, Ido Sivan-Sevilla, Patrick Parham
Digital advertising and technology companies are resigned to a new privacy imperative. They are bracing for a world where third-party tracking will be restricted by design or by law. Digital resignation typically refers to how companies cultivate a sense of powerlessness about privacy among internet users. Our paper looks through this optic from the other end of the lens: How is the digital advertising industry coping with the increasing salience of privacy? Recent developments have forced companies to implement “privacy-preserving” designs—or at least promise some semblance of privacy. Yet, the industry remains dependent on flows of data and means of identification to enable still-desired targeting, measurement, and optimization. Our paper analyzes this contradiction by looking at systems that aim to replicate existing functionalities while protecting user “privacy.” We call this a form of “cynical resignation” and characterize its key maneuvers as follows: (a) sanitizing surveillance; (b) party-hopping; and (c) sabotage. We argue that this “cynical resignation” to a privacy imperative represents a policy failure. In the absence of decisive interventions into the underlying business models of data capitalism, companies offer techno-solutionism and self-regulations that seem to conform to new laws and norms while reinforcing commitments to data-driven personalization. This may benefit the largest tech companies, since their privileged access to first-party data will make more companies reliant on them, and their computational power will be even more valuable in a world where modeling is used to compensate for the loss of third-party data and traditional methods of personal identification.
{"title":"The after party: Cynical resignation in Adtech's pivot to privacy","authors":"Lee McGuigan, Sarah Myers West, Ido Sivan-Sevilla, Patrick Parham","doi":"10.1177/20539517231203665","DOIUrl":"https://doi.org/10.1177/20539517231203665","url":null,"abstract":"Digital advertising and technology companies are resigned to a new privacy imperative. They are bracing for a world where third-party tracking will be restricted by design or by law. Digital resignation typically refers to how companies cultivate a sense of powerlessness about privacy among internet users. Our paper looks through this optic from the other end of the lens: How is the digital advertising industry coping with the increasing salience of privacy? Recent developments have forced companies to implement “privacy-preserving” designs—or at least promise some semblance of privacy. Yet, the industry remains dependent on flows of data and means of identification to enable still-desired targeting, measurement, and optimization. Our paper analyzes this contradiction by looking at systems that aim to replicate existing functionalities while protecting user “privacy.” We call this a form of “cynical resignation” and characterize its key maneuvers as follows: (a) sanitizing surveillance; (b) party-hopping; and (c) sabotage. We argue that this “cynical resignation” to a privacy imperative represents a policy failure. In the absence of decisive interventions into the underlying business models of data capitalism, companies offer techno-solutionism and self-regulations that seem to conform to new laws and norms while reinforcing commitments to data-driven personalization. This may benefit the largest tech companies, since their privileged access to first-party data will make more companies reliant on them, and their computational power will be even more valuable in a world where modeling is used to compensate for the loss of third-party data and traditional methods of personal identification.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135856900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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