Pub Date : 2023-07-01DOI: 10.1177/20539517231190078
Fernanda Odilla, Alice Mattoni
Developed by tech-savvy citizens, Rosie is a bot that autonomously checks the public spending of elected representatives of the Brazilian Lower Chamber and uses Twitter to engage peoplein discussing suspicious findings. Rosie is the most visible face of Operação Serenata de Amor (Operation Love Serenade), a data-enabled activism initiative that revolves around the creation, use and dissemination of open data to hold politicians accountable and empower citizens to react against the misuse of public funds. The article draws on an original data set – including interviews, participant online observation notes and secondary qualitative materials – to examine Operação Serenata de Amor, focusing on how material and symbolic elements related to both human and non-human actors shape the organisational patterns of this type of initiative. The findings suggest that there are three organisational patterns, each with further specific challenges, based on the presence of three modes of participation that depend on different types of engagement with digital technologies and data. Findings indicate that data-enabled activism can emerge with typical characteristics and values of tech startups, such as the goal of creating a sustainable budget and providing strategic content by validating it with user feedback, while also retaining some traits of online activism, such as ad hoc and temporal networks of highly autonomous actors concerned with specific contentious issues. In this respect, the eventual demobilisation of Operação Serenata de Amor's initiators due to commercial values and struggles to maintain it active and engaging can be seen as a cautionary tale for data-enabled activism, particularly for initiatives closely associated with civic innovation and social tech startups.
罗西是一个机器人，由精通技术的公民开发，它可以自动检查巴西下议院当选代表的公共支出，并使用Twitter与人们讨论可疑的发现。罗西是“爱的小夜曲行动”(opera o Serenata de Amor)最引人关注的面孔，这是一个以数据为基础的行动主义倡议，围绕开放数据的创造、使用和传播，让政治家承担责任，并赋予公民权力，反对滥用公共资金。本文利用原始数据集——包括访谈、参与者在线观察笔记和二手定性材料——来研究歌剧《爱的Serenata》，重点关注与人类和非人类行动者相关的物质和符号元素如何塑造这类倡议的组织模式。研究结果表明，有三种组织模式，每一种都有进一步的具体挑战，基于三种参与模式的存在，这些模式依赖于与数字技术和数据的不同类型的接触。研究结果表明，数据支持的行动主义可能具有科技初创公司的典型特征和价值观，例如创建可持续预算的目标，并通过用户反馈验证提供战略性内容，同时也保留了在线行动主义的一些特征，例如关注特定争议问题的高度自主行为者的临时和临时网络。在这方面，opera o Serenata de Amor的发起人由于商业价值而最终解散，并努力保持其活跃和参与，这可以被视为数据支持行动主义的警示故事，特别是与公民创新和社会科技初创公司密切相关的倡议。
{"title":"Unveiling the layers of data activism: The organising of civic innovation to fight corruption in Brazil","authors":"Fernanda Odilla, Alice Mattoni","doi":"10.1177/20539517231190078","DOIUrl":"https://doi.org/10.1177/20539517231190078","url":null,"abstract":"Developed by tech-savvy citizens, Rosie is a bot that autonomously checks the public spending of elected representatives of the Brazilian Lower Chamber and uses Twitter to engage peoplein discussing suspicious findings. Rosie is the most visible face of Operação Serenata de Amor (Operation Love Serenade), a data-enabled activism initiative that revolves around the creation, use and dissemination of open data to hold politicians accountable and empower citizens to react against the misuse of public funds. The article draws on an original data set – including interviews, participant online observation notes and secondary qualitative materials – to examine Operação Serenata de Amor, focusing on how material and symbolic elements related to both human and non-human actors shape the organisational patterns of this type of initiative. The findings suggest that there are three organisational patterns, each with further specific challenges, based on the presence of three modes of participation that depend on different types of engagement with digital technologies and data. Findings indicate that data-enabled activism can emerge with typical characteristics and values of tech startups, such as the goal of creating a sustainable budget and providing strategic content by validating it with user feedback, while also retaining some traits of online activism, such as ad hoc and temporal networks of highly autonomous actors concerned with specific contentious issues. In this respect, the eventual demobilisation of Operação Serenata de Amor's initiators due to commercial values and struggles to maintain it active and engaging can be seen as a cautionary tale for data-enabled activism, particularly for initiatives closely associated with civic innovation and social tech startups.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857033","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: 10.1177/20539517231213821
Cal Lee Garrett, Claire Laurier Decoteau
Data-driven public health policies were widely implemented to mitigate the uneven impact of COVID-19. In the United States, evidence-based interventions are often employed in “racial equity” initiatives to provide calculable representations of racial disparities. However, disparities in working or living conditions, germane to public health but outside the conventional scope of epidemiology, are seldom measured or addressed. What is the effect of defining racial equity with quantitative health outcomes? Drawing on qualitative analysis of 175 interviews with experts and residents in Chicago during the emergence of COVID-19, we find that these policies link the distribution of public resources to effective participation in state projects of data generation. Bringing together theories of quantification and biosocial citizenship, we argue that a form of data citizenship has emerged where public resources are allocated based on quantitative metrics and the variations they depict. Data citizenship is characterized by at least two mechanisms for governing with statistics. Data fixes produce better numbers through technical adjustments in data collection or analysis based on expert assumptions or expectations. Data drag delays distribution of public relief until numbers are compiled to demonstrate and specify needs or deservingness. This paper challenges the use of racial statistics as a salve for structural racism and illustrates how statistical data can exacerbate racial disparities by promising equity.
{"title":"Data citizenship: Quantifying structural racism in COVID-19 and beyond","authors":"Cal Lee Garrett, Claire Laurier Decoteau","doi":"10.1177/20539517231213821","DOIUrl":"https://doi.org/10.1177/20539517231213821","url":null,"abstract":"Data-driven public health policies were widely implemented to mitigate the uneven impact of COVID-19. In the United States, evidence-based interventions are often employed in “racial equity” initiatives to provide calculable representations of racial disparities. However, disparities in working or living conditions, germane to public health but outside the conventional scope of epidemiology, are seldom measured or addressed. What is the effect of defining racial equity with quantitative health outcomes? Drawing on qualitative analysis of 175 interviews with experts and residents in Chicago during the emergence of COVID-19, we find that these policies link the distribution of public resources to effective participation in state projects of data generation. Bringing together theories of quantification and biosocial citizenship, we argue that a form of data citizenship has emerged where public resources are allocated based on quantitative metrics and the variations they depict. Data citizenship is characterized by at least two mechanisms for governing with statistics. Data fixes produce better numbers through technical adjustments in data collection or analysis based on expert assumptions or expectations. Data drag delays distribution of public relief until numbers are compiled to demonstrate and specify needs or deservingness. This paper challenges the use of racial statistics as a salve for structural racism and illustrates how statistical data can exacerbate racial disparities by promising equity.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857752","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: 10.1177/20539517231203666
Luis Felipe R Murillo, Caitlin Wylie, Phil Bourne
In a moment of heightened ethical questioning concerning data-intensive analytics, “data ethics” has become a site of dispute over its very definition in teaching, research, and practice. In this paper, we contextualize this dispute based on the experience of teaching data ethics. We describe how the field of computer ethics has historically informed the training of computer experts and how, in recent years, the scholarship on science and technology studies has created opportunities for transforming the way we teach with the inclusion of critical scholarship on relational ethics and sociotechnical systems. The emergent literature on “critical data ethics” has created a space for interdisciplinary collaboration that integrates technical and social science research to examine digital systems in their design, implementation, and use through a hands-on approach. As a contribution to the recent efforts to reimagine and transform the field of data science, we conclude with a discussion of the approach we devised to bridge technology/society divides and engage students with questions of social justice, accountability, and openness in their data practices.
{"title":"Critical data ethics pedagogies: Three (non-rival) approaches","authors":"Luis Felipe R Murillo, Caitlin Wylie, Phil Bourne","doi":"10.1177/20539517231203666","DOIUrl":"https://doi.org/10.1177/20539517231203666","url":null,"abstract":"In a moment of heightened ethical questioning concerning data-intensive analytics, “data ethics” has become a site of dispute over its very definition in teaching, research, and practice. In this paper, we contextualize this dispute based on the experience of teaching data ethics. We describe how the field of computer ethics has historically informed the training of computer experts and how, in recent years, the scholarship on science and technology studies has created opportunities for transforming the way we teach with the inclusion of critical scholarship on relational ethics and sociotechnical systems. The emergent literature on “critical data ethics” has created a space for interdisciplinary collaboration that integrates technical and social science research to examine digital systems in their design, implementation, and use through a hands-on approach. As a contribution to the recent efforts to reimagine and transform the field of data science, we conclude with a discussion of the approach we devised to bridge technology/society divides and engage students with questions of social justice, accountability, and openness in their data practices.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":"340 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135856121","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: 10.1177/20539517231210244
Ahmad Wali Ahmad Yar, Tuba Bircan
International migration statistics suffer from extensive gaps and shortcomings. Recently, national statistical institutions (NSIs) have started using big data to complement traditional statistics, including on migration. Although these are promising developments, we still lack answers on the extent to which NSIs are currently using big data for migration and to what extent it complements the gaps in traditional data. We gathered data by interviewing experts from 29 NSIs to investigate how big data is used for official migration statistics. We show that 15 out of 29 NSIs either used big data for migration, had a pilot project or have been involved in joint initiatives. We reveal the specific implications of big data in human migration (e.g. internal mobility, stocks, flows and mobility patterns, among others and the most common sources used to extract official statistics). Moreover, we discuss the challenges and barriers preventing NSIs from using such data. Factors deterring countries from utilising big data include limited data accessibility, an absence of legal frameworks for big data usage, ethical concerns, the possession of already high-quality data, a deficit in expertise and methodologies and a lack of perceived necessity for supplementary data or approaches. Moreover, many countries did not know which data to use and were concerned about the quality and accuracy of such data. Legal barriers were more of an issue than the ethical aspects, and overall, participating countries believe that there is a high potential for big data in the future.
{"title":"Big data for official migration statistics: Evidence from 29 national statistical institutions","authors":"Ahmad Wali Ahmad Yar, Tuba Bircan","doi":"10.1177/20539517231210244","DOIUrl":"https://doi.org/10.1177/20539517231210244","url":null,"abstract":"International migration statistics suffer from extensive gaps and shortcomings. Recently, national statistical institutions (NSIs) have started using big data to complement traditional statistics, including on migration. Although these are promising developments, we still lack answers on the extent to which NSIs are currently using big data for migration and to what extent it complements the gaps in traditional data. We gathered data by interviewing experts from 29 NSIs to investigate how big data is used for official migration statistics. We show that 15 out of 29 NSIs either used big data for migration, had a pilot project or have been involved in joint initiatives. We reveal the specific implications of big data in human migration (e.g. internal mobility, stocks, flows and mobility patterns, among others and the most common sources used to extract official statistics). Moreover, we discuss the challenges and barriers preventing NSIs from using such data. Factors deterring countries from utilising big data include limited data accessibility, an absence of legal frameworks for big data usage, ethical concerns, the possession of already high-quality data, a deficit in expertise and methodologies and a lack of perceived necessity for supplementary data or approaches. Moreover, many countries did not know which data to use and were concerned about the quality and accuracy of such data. Legal barriers were more of an issue than the ethical aspects, and overall, participating countries believe that there is a high potential for big data in the future.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857504","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: 10.1177/20539517231203667
Caitlin Bentley, Chisenga Muyoya, Sara Vannini, Susan Oman, Andrea Jimenez
Data's increasing role in society and high profile reproduction of inequalities is in tension with traditional methods of using social data for social justice. Alongside this, ‘intersectionality’ has increased in prominence as a critical social theory and praxis to address inequalities. Yet, there is not a comprehensive review of how intersectionality is operationalized in research data practice. In this study, we examined how intersectionality researchers across a range of disciplines conduct intersectional analysis as a means of unpacking how intersectional praxis may advance an intersectional data science agenda. To explore how intersectionality researchers collect and analyze data, we conducted a critical discourse analysis approach in a review of 172 articles that stated using an intersectional approach in some way. We contemplated whether and how Collins’ three frames of relationality were evident in their approach. We found an over-reliance on the additive thinking frame in quantitative research, which poses limits on the potential for this research to address structural inequality. We suggest ways in which intersectional data science could adopt an articulation mindset to improve on this tendency.
{"title":"Intersectional approaches to data: The importance of an articulation mindset for intersectional data science","authors":"Caitlin Bentley, Chisenga Muyoya, Sara Vannini, Susan Oman, Andrea Jimenez","doi":"10.1177/20539517231203667","DOIUrl":"https://doi.org/10.1177/20539517231203667","url":null,"abstract":"Data's increasing role in society and high profile reproduction of inequalities is in tension with traditional methods of using social data for social justice. Alongside this, ‘intersectionality’ has increased in prominence as a critical social theory and praxis to address inequalities. Yet, there is not a comprehensive review of how intersectionality is operationalized in research data practice. In this study, we examined how intersectionality researchers across a range of disciplines conduct intersectional analysis as a means of unpacking how intersectional praxis may advance an intersectional data science agenda. To explore how intersectionality researchers collect and analyze data, we conducted a critical discourse analysis approach in a review of 172 articles that stated using an intersectional approach in some way. We contemplated whether and how Collins’ three frames of relationality were evident in their approach. We found an over-reliance on the additive thinking frame in quantitative research, which poses limits on the potential for this research to address structural inequality. We suggest ways in which intersectional data science could adopt an articulation mindset to improve on this tendency.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135852126","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 : 2023-01-01DOI: 10.1177/20539517221145372
Benjamin N. Jacobsen
Machine-learning algorithms have become deeply embedded in contemporary society. As such, ample attention has been paid to the contents, biases, and underlying assumptions of the training datasets that many algorithmic models are trained on. Yet, what happens when algorithms are trained on data that are not real, but instead data that are ‘synthetic’, not referring to real persons, objects, or events? Increasingly, synthetic data are being incorporated into the training of machine-learning algorithms for use in various societal domains. There is currently little understanding, however, of the role played by and the ethicopolitical implications of synthetic training data for machine-learning algorithms. In this article, I explore the politics of synthetic data through two central aspects: first, synthetic data promise to emerge as a rich source of exposure to variability for the algorithm. Second, the paper explores how synthetic data promise to place algorithms beyond the realm of risk. I propose that an analysis of these two areas will help us better understand the ways in which machine-learning algorithms are envisioned in the light of synthetic data, but also how synthetic training data actively reconfigure the conditions of possibility for machine learning in contemporary society.
{"title":"Machine learning and the politics of synthetic data","authors":"Benjamin N. Jacobsen","doi":"10.1177/20539517221145372","DOIUrl":"https://doi.org/10.1177/20539517221145372","url":null,"abstract":"Machine-learning algorithms have become deeply embedded in contemporary society. As such, ample attention has been paid to the contents, biases, and underlying assumptions of the training datasets that many algorithmic models are trained on. Yet, what happens when algorithms are trained on data that are not real, but instead data that are ‘synthetic’, not referring to real persons, objects, or events? Increasingly, synthetic data are being incorporated into the training of machine-learning algorithms for use in various societal domains. There is currently little understanding, however, of the role played by and the ethicopolitical implications of synthetic training data for machine-learning algorithms. In this article, I explore the politics of synthetic data through two central aspects: first, synthetic data promise to emerge as a rich source of exposure to variability for the algorithm. Second, the paper explores how synthetic data promise to place algorithms beyond the realm of risk. I propose that an analysis of these two areas will help us better understand the ways in which machine-learning algorithms are envisioned in the light of synthetic data, but also how synthetic training data actively reconfigure the conditions of possibility for machine learning in contemporary society.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":" ","pages":""},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48004371","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 : 2023-01-01DOI: 10.1177/20539517231172422
R. Gallagher, Robert J. Topinka
The acronym ‘NPC’ originates from videogame culture, where it refers to computer-controlled drones whose behaviour is dictated by their programming. By 2018 the term had gained traction within right-wing subcultural spaces as shorthand for individuals apparently incapable of thinking for themselves. By the autumn of 2018, these spaces were awash with NPC memes accusing liberals and leftists of uncritically accepting progressive doxa and parroting left-wing catchphrases. In mid-October, with midterm elections looming in the US, Twitter banned over 1000 NPC roleplay accounts created by supporters of Donald Trump, citing concerns over disinformation. This event was much discussed both within right-wing subcultural spaces and by mainstream media outlets, serving as an occasion to reassess the political effects of digital media in general and reactionary memes in particular. Here we use a combination of computational analysis and theoretically informed close reading to trace the NPC meme's trajectory and explore its role in entrenching affectively charged political and (sub)cultural faultlines. We show how mainstream attention at once amplified the meme and attenuated its affective resonance in the subcultural spaces where it originated. We also contend that while the NPC meme has served as a vehicle for antidemocratic bigotry, it may yet harbour critical potential, providing a vocabulary for theorising the cultural and political impacts of communicative capitalism.
{"title":"The politics of the NPC meme: Reactionary subcultural practice and vernacular theory","authors":"R. Gallagher, Robert J. Topinka","doi":"10.1177/20539517231172422","DOIUrl":"https://doi.org/10.1177/20539517231172422","url":null,"abstract":"The acronym ‘NPC’ originates from videogame culture, where it refers to computer-controlled drones whose behaviour is dictated by their programming. By 2018 the term had gained traction within right-wing subcultural spaces as shorthand for individuals apparently incapable of thinking for themselves. By the autumn of 2018, these spaces were awash with NPC memes accusing liberals and leftists of uncritically accepting progressive doxa and parroting left-wing catchphrases. In mid-October, with midterm elections looming in the US, Twitter banned over 1000 NPC roleplay accounts created by supporters of Donald Trump, citing concerns over disinformation. This event was much discussed both within right-wing subcultural spaces and by mainstream media outlets, serving as an occasion to reassess the political effects of digital media in general and reactionary memes in particular. Here we use a combination of computational analysis and theoretically informed close reading to trace the NPC meme's trajectory and explore its role in entrenching affectively charged political and (sub)cultural faultlines. We show how mainstream attention at once amplified the meme and attenuated its affective resonance in the subcultural spaces where it originated. We also contend that while the NPC meme has served as a vehicle for antidemocratic bigotry, it may yet harbour critical potential, providing a vocabulary for theorising the cultural and political impacts of communicative capitalism.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":" ","pages":""},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44305370","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 : 2023-01-01DOI: 10.1177/20539517231173901
Ville Aula, Jameson Bowles
Various ‘Data for Good’ and ‘AI for Good’ initiatives have emerged in recent years to promote and organise efforts to use new computational techniques to solve societal problems. The initiatives exercise ongoing influence on how the capabilities of computational techniques are understood as vehicles of social and political change. This paper analyses the development of the initiatives from a rhetorical slogan into a research program that understands itself as a ‘field’ of applications. It discusses recent academic literature on the topic to show a problematic entanglement between the promotion of initiatives and prescriptions of what ‘good’ ought to be. In contrast, we call researchers to take a practical and analytical step back. The paper provides a framework for future research by calling for descriptive research on the composition of the initiatives and critical research that draws from broader social science debates on computational techniques. The empirical part of the paper provides first steps towards this direction by positioning Data and AI for Good initiatives as part of a single continuum and situating it within a historical trajectory that has its immediate precursor in ICT for Development initiatives.
{"title":"Stepping back from Data and AI for Good – current trends and ways forward","authors":"Ville Aula, Jameson Bowles","doi":"10.1177/20539517231173901","DOIUrl":"https://doi.org/10.1177/20539517231173901","url":null,"abstract":"Various ‘Data for Good’ and ‘AI for Good’ initiatives have emerged in recent years to promote and organise efforts to use new computational techniques to solve societal problems. The initiatives exercise ongoing influence on how the capabilities of computational techniques are understood as vehicles of social and political change. This paper analyses the development of the initiatives from a rhetorical slogan into a research program that understands itself as a ‘field’ of applications. It discusses recent academic literature on the topic to show a problematic entanglement between the promotion of initiatives and prescriptions of what ‘good’ ought to be. In contrast, we call researchers to take a practical and analytical step back. The paper provides a framework for future research by calling for descriptive research on the composition of the initiatives and critical research that draws from broader social science debates on computational techniques. The empirical part of the paper provides first steps towards this direction by positioning Data and AI for Good initiatives as part of a single continuum and situating it within a historical trajectory that has its immediate precursor in ICT for Development initiatives.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":" ","pages":""},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47353993","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 : 2023-01-01DOI: 10.1177/20539517231156123
Yao‐Tai Li, Katherine Whitworth
The Russian government's narrative about the Russia-Ukraine war has raised concerns about disinformation, fake news and freedom of information. In response, websites have been developed that allow people across the world to call or send emails and texts with information about the war to individuals based in Russia. To facilitate this person-to-person communication between strangers, automated data processing has been used to collect personal data from the internet and compile it into publicly accessible mailing lists. This side-stepping of consent coupled with the nature of information being transmitted and the motivation behind its transmission poses important questions of an ethical nature: What is an appropriate balance between the data subjects’ right to freedom of information and their right to privacy? Can data processing without the consent of the data subject be justified in certain circumstances? This commentary does not seek to provide definitive answers to these questions, rather it canvases some key issues in the hope of starting further dialogue on the topic.
{"title":"The right to information or data sovereignty? Sending unsolicited messages to Russians about the war in Ukraine","authors":"Yao‐Tai Li, Katherine Whitworth","doi":"10.1177/20539517231156123","DOIUrl":"https://doi.org/10.1177/20539517231156123","url":null,"abstract":"The Russian government's narrative about the Russia-Ukraine war has raised concerns about disinformation, fake news and freedom of information. In response, websites have been developed that allow people across the world to call or send emails and texts with information about the war to individuals based in Russia. To facilitate this person-to-person communication between strangers, automated data processing has been used to collect personal data from the internet and compile it into publicly accessible mailing lists. This side-stepping of consent coupled with the nature of information being transmitted and the motivation behind its transmission poses important questions of an ethical nature: What is an appropriate balance between the data subjects’ right to freedom of information and their right to privacy? Can data processing without the consent of the data subject be justified in certain circumstances? This commentary does not seek to provide definitive answers to these questions, rather it canvases some key issues in the hope of starting further dialogue on the topic.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":" ","pages":""},"PeriodicalIF":8.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47502834","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 : 2023-01-01DOI: 10.1177/20539517231171058
Renée Ridgway
Google dominates around 92% of the search market worldwide (as of November 2022), with most of its revenue derived from search advertising. However, Google's hegemony over search and the resulting implications are not necessarily accidental, arbitrary or (un)intentional. This article revisits Brin and Page's original paper, drawing on six of their key innovations, concerns and design choices (counting citations or backlinks, trusted user, advertising, personalization, usage data, smart algorithms) to explain the evolution of Google's hypertext search engine technologies through ‘moments of contingency’, which led to corporate lock-ins. Underpinned by analyses of patents, statements and secondary sources, it elucidates how early Google considerations and certain affordances not only came to shape the web (backlinks, trusted user, advertising) but subsequently facilitated contemporary surveillance capitalism. Building upon Zuboff's ‘Big Other’, it describes the ways in which Google as an infrastructure is intertwined with Big Data's platformization and the ad infinitum collection of usage data, beyond just personalization. This extraction and refinement of usage data as ‘behavioural surplus’ results in ‘deleterious consequences’: a ‘habit of automaticity,’ which shapes the trusted user through ‘ubiquitous googling’ and smart algorithms, whilst simultaneously generating prediction products for surveillance capitalism. Advancing Latour's ‘predicting the path’ of technological innovation, this cause-and-effect story contributes a new taxonomy of Google sociotechnical affordances to critical STS, media history and web search literature.
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