Pub Date : 2023-01-01DOI: 10.1177/20539517231164115
Thomas Walsh
To better understand the COVID-19 pandemic, public health researchers turned to “big mobility data”—location data collected from mobile devices by companies engaged in surveillance capitalism. Publishing formerly private big mobility datasets, firms trumpeted their efforts to “fight” COVID-19 and researchers highlighted the potential of big mobility data to improve infectious disease models tracking the pandemic. However, these collaborations are defined by asymmetries in information, access, and power. The release of data is characterized by a lack of obligation on the part of the data provider towards public health goals, particularly those committed to a community-based, participatory model. There is a lack of appropriate reciprocities between data company, data subject, researcher, and community. People are de-centered, surveillance is de-linked from action while the agendas of public health and surveillance capitalism grow closer. This article argues that the current use of big mobility data in the COVID-19 pandemic represents a poor approach with respect to community and person-centered frameworks.
{"title":"Modeling COVID-19 with big mobility data: Surveillance and reaffirming the people in the data","authors":"Thomas Walsh","doi":"10.1177/20539517231164115","DOIUrl":"https://doi.org/10.1177/20539517231164115","url":null,"abstract":"To better understand the COVID-19 pandemic, public health researchers turned to “big mobility data”—location data collected from mobile devices by companies engaged in surveillance capitalism. Publishing formerly private big mobility datasets, firms trumpeted their efforts to “fight” COVID-19 and researchers highlighted the potential of big mobility data to improve infectious disease models tracking the pandemic. However, these collaborations are defined by asymmetries in information, access, and power. The release of data is characterized by a lack of obligation on the part of the data provider towards public health goals, particularly those committed to a community-based, participatory model. There is a lack of appropriate reciprocities between data company, data subject, researcher, and community. People are de-centered, surveillance is de-linked from action while the agendas of public health and surveillance capitalism grow closer. This article argues that the current use of big mobility data in the COVID-19 pandemic represents a poor approach with respect to community and person-centered frameworks.","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":"44947327","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: 10.1177/20539517231159629
Jathan Sadowski, Kaitlin Beegle
The self-proclaimed usurper of Web 2.0, Web3 quickly became the center of attention. Not long ago, the public discourse was saturated with projects, promises, and peculiarities of Web3. Now the spotlight has swung around to focus on the many faults, failures, and frauds of Web3. The cycles of technological trends and investment bubbles seem to be accelerating in such a way as to escape any attempt at observing them in motion before they crash, and then everybody moves on to the next thing. Importantly, Web3 was not an anomaly or curiosity in the broader tech industry. It articulates patterns that existed before Web3 and will exist after. Web3 should be understood as a case study of innovation within the dominant model of Silicon Valley venture capitalism. Our focus in this article is on understanding how the movement around Web3 formed through an interplay between (1) normative concepts and contestations related to ideas of “decentralization” and (2) political economic interests and operations related to the dynamics of fictitious capital. By offering a critical analysis of Web3, our goal is also to show how any even potentially progressive (or as we call them “expansive”) forms of Web3 development struggle for success, recognition, and attention due to the wild excesses of hype and investment devoted to “extractive” forms of Web3. In the process, they provide us a better view of how different arrangements of technopolitics can exist at the same time, side-by-side, in complicated ways.
{"title":"Expansive and extractive networks of Web3","authors":"Jathan Sadowski, Kaitlin Beegle","doi":"10.1177/20539517231159629","DOIUrl":"https://doi.org/10.1177/20539517231159629","url":null,"abstract":"The self-proclaimed usurper of Web 2.0, Web3 quickly became the center of attention. Not long ago, the public discourse was saturated with projects, promises, and peculiarities of Web3. Now the spotlight has swung around to focus on the many faults, failures, and frauds of Web3. The cycles of technological trends and investment bubbles seem to be accelerating in such a way as to escape any attempt at observing them in motion before they crash, and then everybody moves on to the next thing. Importantly, Web3 was not an anomaly or curiosity in the broader tech industry. It articulates patterns that existed before Web3 and will exist after. Web3 should be understood as a case study of innovation within the dominant model of Silicon Valley venture capitalism. Our focus in this article is on understanding how the movement around Web3 formed through an interplay between (1) normative concepts and contestations related to ideas of “decentralization” and (2) political economic interests and operations related to the dynamics of fictitious capital. By offering a critical analysis of Web3, our goal is also to show how any even potentially progressive (or as we call them “expansive”) forms of Web3 development struggle for success, recognition, and attention due to the wild excesses of hype and investment devoted to “extractive” forms of Web3. In the process, they provide us a better view of how different arrangements of technopolitics can exist at the same time, side-by-side, in complicated ways.","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":"46524146","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: 10.1177/20539517231153808
Petter Törnberg
The rise of digital platforms has in recent years redefined contemporary capitalism—provoking discussions on whether platformization should be understood as bringing an altogether new form of capitalism, or as merely a continuation and intensification of existing neoliberal trends. This paper draws on regulation theory to examine social regulation in digital capitalism, arguing for understanding digital capitalism as continuities of existing capitalist trends coming to produce discontinuities. The paper makes three main arguments. First, it situates digital capitalism as a continuation of longer running post-Fordist trends of financialization, digitalization, and privatization—converging in the emergence of digital proprietary markets, owned and regulated by transnational platform companies. Second, as the platform model is founded on monopolizing regulation, platforms come into direct competition with states and public institutions, which they pursue through a set of distinct technopolitical strategies to claim power to govern—resulting in a geographically variegated process of institutional transformation. Third, while the digital proprietary markets are continuities of existing trends, they bring new pressures and affordances, thus producing discontinuities in social regulation. We examine such discontinuities in relation to three aspects of social regulation: (a) from neoliberalism to techno-feudalism; (b) from Taylorist hierarchies toward algorithmic herds and technoliberal subjectivity; and (c) from postmodernity toward an automated consumer culture.
{"title":"How platforms govern: Social regulation in digital capitalism","authors":"Petter Törnberg","doi":"10.1177/20539517231153808","DOIUrl":"https://doi.org/10.1177/20539517231153808","url":null,"abstract":"The rise of digital platforms has in recent years redefined contemporary capitalism—provoking discussions on whether platformization should be understood as bringing an altogether new form of capitalism, or as merely a continuation and intensification of existing neoliberal trends. This paper draws on regulation theory to examine social regulation in digital capitalism, arguing for understanding digital capitalism as continuities of existing capitalist trends coming to produce discontinuities. The paper makes three main arguments. First, it situates digital capitalism as a continuation of longer running post-Fordist trends of financialization, digitalization, and privatization—converging in the emergence of digital proprietary markets, owned and regulated by transnational platform companies. Second, as the platform model is founded on monopolizing regulation, platforms come into direct competition with states and public institutions, which they pursue through a set of distinct technopolitical strategies to claim power to govern—resulting in a geographically variegated process of institutional transformation. Third, while the digital proprietary markets are continuities of existing trends, they bring new pressures and affordances, thus producing discontinuities in social regulation. We examine such discontinuities in relation to three aspects of social regulation: (a) from neoliberalism to techno-feudalism; (b) from Taylorist hierarchies toward algorithmic herds and technoliberal subjectivity; and (c) from postmodernity toward an automated consumer culture.","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":"49443114","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: 10.1177/20539517231158996
Rolien Hoyng
The corporate discourse on the circular economy holds that the growth of the electronics industry, driven by continuous innovation, does not imperil ecological sustainability. To achieve sustainable growth, its advocates propose optimizing recycling by means of artificial intelligence and sets of interrelated datacentric and algorithmic technologies. Drawing on critical data and algorithm studies, theories of waste, and empirical research, this paper investigates ecological ethics in the context of the datacentric and algorithmically mediated circular economy. It foregrounds the indeterminate and fickle material nature of waste as well as the uncertainties inherent in, and stemming from, datafication and computation. My question is: how do the rationalities, affordances, and dispositions of datacentric and algorithmic technologies perform and displace notions of corporate responsibility and transparency? In order to answer this question, I compare the smart circular economy to the informal recycling practices that it claims to replace, and I analyze relations between waste matter and data as well as distributions of agency. Specifically, I consider transitions and slippages between response-ability and responsibility. Conceptually, I bring process-relation or immanence-based philosophies such as Bergson's and Deleuze's into a debate about relations between waste matter and data and the ambition of algorithmic control over waste. My aim is not to demand heightened corporate responsibility enacted through control but to rethink responsibility in the smart circular economy along the lines of Amoore's cloud ethics to carve out a position of critique beyond either a deontological perspective that reinforces corporate agency or new-materialist denunciation of the concept.
{"title":"Ecological ethics and the smart circular economy","authors":"Rolien Hoyng","doi":"10.1177/20539517231158996","DOIUrl":"https://doi.org/10.1177/20539517231158996","url":null,"abstract":"The corporate discourse on the circular economy holds that the growth of the electronics industry, driven by continuous innovation, does not imperil ecological sustainability. To achieve sustainable growth, its advocates propose optimizing recycling by means of artificial intelligence and sets of interrelated datacentric and algorithmic technologies. Drawing on critical data and algorithm studies, theories of waste, and empirical research, this paper investigates ecological ethics in the context of the datacentric and algorithmically mediated circular economy. It foregrounds the indeterminate and fickle material nature of waste as well as the uncertainties inherent in, and stemming from, datafication and computation. My question is: how do the rationalities, affordances, and dispositions of datacentric and algorithmic technologies perform and displace notions of corporate responsibility and transparency? In order to answer this question, I compare the smart circular economy to the informal recycling practices that it claims to replace, and I analyze relations between waste matter and data as well as distributions of agency. Specifically, I consider transitions and slippages between response-ability and responsibility. Conceptually, I bring process-relation or immanence-based philosophies such as Bergson's and Deleuze's into a debate about relations between waste matter and data and the ambition of algorithmic control over waste. My aim is not to demand heightened corporate responsibility enacted through control but to rethink responsibility in the smart circular economy along the lines of Amoore's cloud ethics to carve out a position of critique beyond either a deontological perspective that reinforces corporate agency or new-materialist denunciation of the concept.","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":"45912563","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: 10.1177/20539517231163174
A. Bernier, Maili Raven-Adams, D. Zaccagnini, B. Knoppers
Health organisations use numerous different mechanisms to collect biomedical data, to determine the applicable ethical, legal and institutional conditions of use, and to reutilise the data in accordance with the relevant rules. These methods and mechanisms differ from one organisation to another, and involve considerable specialised human labour, including record-keeping functions and decision-making committees. In reutilising data at scale, however, organisations struggle to meet demands for data interoperability and for rapid inter-organisational data exchange due to reliance on legacy paper-based records and on the human-initiated administration of accompanying permissions in data. The adoption of permissions-recording, and permissions-administration tools that can be implemented at scale across numerous organisations is imperative. Further, these must be implemented in a manner that does not compromise the nuanced and contextual adjudicative processes of research ethics committees, data access committees, and biomedical research organisations. The tools required to implement a streamlined system of biomedical data exchange have in great part been developed. Indeed, there remains but a small core of functions that must further be standardised and automated to enable the recording and administration of permissions in biomedical research data with minimal human effort. Recording ethical provenance in this manner would enable biomedical data exchange to be performed at scale, in full respect of the ethical, legal, and institutional rules applicable to different datasets. This despite foundational differences between the distinct legal and normative frameworks is applicable to distinct communities and organisations that share data between one another.
{"title":"Recording the ethical provenance of data and automating data stewardship","authors":"A. Bernier, Maili Raven-Adams, D. Zaccagnini, B. Knoppers","doi":"10.1177/20539517231163174","DOIUrl":"https://doi.org/10.1177/20539517231163174","url":null,"abstract":"Health organisations use numerous different mechanisms to collect biomedical data, to determine the applicable ethical, legal and institutional conditions of use, and to reutilise the data in accordance with the relevant rules. These methods and mechanisms differ from one organisation to another, and involve considerable specialised human labour, including record-keeping functions and decision-making committees. In reutilising data at scale, however, organisations struggle to meet demands for data interoperability and for rapid inter-organisational data exchange due to reliance on legacy paper-based records and on the human-initiated administration of accompanying permissions in data. The adoption of permissions-recording, and permissions-administration tools that can be implemented at scale across numerous organisations is imperative. Further, these must be implemented in a manner that does not compromise the nuanced and contextual adjudicative processes of research ethics committees, data access committees, and biomedical research organisations. The tools required to implement a streamlined system of biomedical data exchange have in great part been developed. Indeed, there remains but a small core of functions that must further be standardised and automated to enable the recording and administration of permissions in biomedical research data with minimal human effort. Recording ethical provenance in this manner would enable biomedical data exchange to be performed at scale, in full respect of the ethical, legal, and institutional rules applicable to different datasets. This despite foundational differences between the distinct legal and normative frameworks is applicable to distinct communities and organisations that share data between one another.","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":"47782589","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: 10.1177/20539517231182391
Le Anh Nguyen Long, S. V. D. Graaf, Athanasios Votsis
Borrowing from insights produced in urban planning, media and governance studies thereby leveraging the Ostrom-nian ideas of institutions and polycentricity, this paper examines how to govern commons in the smart city. It offers a reflection upon whether Distributed Ledger Technologies (DLTs) could be a key notion for the commons discourse which centers around stakeholders, self-organization, and a rights-based framework. By decentralizing ledgers and enabling the interoperability of the various interfaces, DLTs make records more accessible, exchanges more transparent, and reduce costs while increasing efficiency, and permit automation, therefore commoning interactions both offline and online are facilitated. We argue that the use of DLTs to preserve the spatiotemporal integrity of key urban spaces is a common value question that needs to be elucidated or renegotiated in order to provide any useful guidance to DLTs integrity-preserving potential. In doing so, we draw attention to DLT-based urban commons and urban governance, and point to inherent incompatibilities that may lead to radical and not-so-smooth changes in urban institutions, while providing a way of thinking which can move the smart city closer towards a values-centered process and away from a preoccupation with technology and efficiency.
{"title":"“Governing the urban commons”: DLT, institutions, and citizens in perspective","authors":"Le Anh Nguyen Long, S. V. D. Graaf, Athanasios Votsis","doi":"10.1177/20539517231182391","DOIUrl":"https://doi.org/10.1177/20539517231182391","url":null,"abstract":"Borrowing from insights produced in urban planning, media and governance studies thereby leveraging the Ostrom-nian ideas of institutions and polycentricity, this paper examines how to govern commons in the smart city. It offers a reflection upon whether Distributed Ledger Technologies (DLTs) could be a key notion for the commons discourse which centers around stakeholders, self-organization, and a rights-based framework. By decentralizing ledgers and enabling the interoperability of the various interfaces, DLTs make records more accessible, exchanges more transparent, and reduce costs while increasing efficiency, and permit automation, therefore commoning interactions both offline and online are facilitated. We argue that the use of DLTs to preserve the spatiotemporal integrity of key urban spaces is a common value question that needs to be elucidated or renegotiated in order to provide any useful guidance to DLTs integrity-preserving potential. In doing so, we draw attention to DLT-based urban commons and urban governance, and point to inherent incompatibilities that may lead to radical and not-so-smooth changes in urban institutions, while providing a way of thinking which can move the smart city closer towards a values-centered process and away from a preoccupation with technology and efficiency.","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":"41879785","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: 10.1177/20539517231168100
Jonathan Gruber, E. Hargittai
Using the Internet means encountering algorithmic processes that influence what information a user sees or hears. Existing research has shown that people's algorithm skills vary considerably, that they develop individual theories to explain these processes, and that their online behavior can reflect these understandings. Yet, there is little research on how algorithm skills enable people to use algorithms to their own benefit and to avoid harms they may elicit. To fill this gap in the literature, we explore the extent to which people understand how the online systems and services they use may be influenced by personal data that algorithms know about them, and whether users change their behavior based on this understanding. Analyzing 83 in-depth interviews from five countries about people's experiences with researching and searching for products and services online, we show how being aware of personal data collection helps people understand algorithmic processes. However, this does not necessarily enable users to influence algorithmic output, because currently, options that help users control the level of customization they encounter online are limited. Besides the empirical contributions, we discuss research design implications based on the diversity of the sample and our findings for studying algorithm skills.
{"title":"The importance of algorithm skills for informed Internet use","authors":"Jonathan Gruber, E. Hargittai","doi":"10.1177/20539517231168100","DOIUrl":"https://doi.org/10.1177/20539517231168100","url":null,"abstract":"Using the Internet means encountering algorithmic processes that influence what information a user sees or hears. Existing research has shown that people's algorithm skills vary considerably, that they develop individual theories to explain these processes, and that their online behavior can reflect these understandings. Yet, there is little research on how algorithm skills enable people to use algorithms to their own benefit and to avoid harms they may elicit. To fill this gap in the literature, we explore the extent to which people understand how the online systems and services they use may be influenced by personal data that algorithms know about them, and whether users change their behavior based on this understanding. Analyzing 83 in-depth interviews from five countries about people's experiences with researching and searching for products and services online, we show how being aware of personal data collection helps people understand algorithmic processes. However, this does not necessarily enable users to influence algorithmic output, because currently, options that help users control the level of customization they encounter online are limited. Besides the empirical contributions, we discuss research design implications based on the diversity of the sample and our findings for studying algorithm skills.","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":"41901578","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: 10.1177/20539517231179199
A. de Manuel, Janet Delgado, Iris Parra Jounou, T. Ausín, D. Casacuberta, Maite Cruz, Ariel Guersenzvaig, Cristian Moyano, D. Rodríguez-Arias, J. Rueda, Á. Puyol
The main aim of this article is to reflect on the impact of biases related to artificial intelligence (AI) systems developed to tackle issues arising from the COVID-19 pandemic, with special focus on those developed for triage and risk prediction. A secondary aim is to review assessment tools that have been developed to prevent biases in AI systems. In addition, we provide a conceptual clarification for some terms related to biases in this particular context. We focus mainly on non-racial biases that may be less considered when addressing biases in AI systems in the existing literature. In the manuscript, we found that the existence of bias in AI systems used for COVID-19 can result in algorithmic justice and that the legal frameworks and strategies developed to prevent the apparition of bias have failed to adequately consider social determinants of health. Finally, we make some recommendations on how to include more diverse professional profiles in order to develop AI systems that increase the epistemic diversity needed to tackle AI biases during the COVID-19 pandemic and beyond.
{"title":"Ethical assessments and mitigation strategies for biases in AI-systems used during the COVID-19 pandemic","authors":"A. de Manuel, Janet Delgado, Iris Parra Jounou, T. Ausín, D. Casacuberta, Maite Cruz, Ariel Guersenzvaig, Cristian Moyano, D. Rodríguez-Arias, J. Rueda, Á. Puyol","doi":"10.1177/20539517231179199","DOIUrl":"https://doi.org/10.1177/20539517231179199","url":null,"abstract":"The main aim of this article is to reflect on the impact of biases related to artificial intelligence (AI) systems developed to tackle issues arising from the COVID-19 pandemic, with special focus on those developed for triage and risk prediction. A secondary aim is to review assessment tools that have been developed to prevent biases in AI systems. In addition, we provide a conceptual clarification for some terms related to biases in this particular context. We focus mainly on non-racial biases that may be less considered when addressing biases in AI systems in the existing literature. In the manuscript, we found that the existence of bias in AI systems used for COVID-19 can result in algorithmic justice and that the legal frameworks and strategies developed to prevent the apparition of bias have failed to adequately consider social determinants of health. Finally, we make some recommendations on how to include more diverse professional profiles in order to develop AI systems that increase the epistemic diversity needed to tackle AI biases during the COVID-19 pandemic and beyond.","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":"44023224","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: 10.1177/20539517231165490
Mackenzie Lemieux, Rebecca Zhang, F. Tripodi
While research has explored the extent of gender bias and the barriers to women's inclusion on English-language Wikipedia, very little research has focused on the problem of racial bias within the encyclopedia. Despite advocacy groups' efforts to incrementally improve representation on Wikipedia, much is unknown regarding how biographies are assessed after creation. Applying a combination of web-scraping, deep learning, natural language processing, and qualitative analysis to pages of academics nominated for deletion on Wikipedia, we demonstrate how Wikipedia's notability guidelines are unequally applied across race and gender. We find that online presence predicts whether a Wikipedia page is kept or deleted for white male academics but that this metric is idiosyncratically applied for female and BIPOC academics. Further, women's pages, regardless of race, were more likely to be deemed “too soon” for Wikipedia. A deeper analysis of the deletion archives reveals that when the tag is used on a woman's biography it is done so outside of the community guidelines, referring to one's career stage rather than media/online coverage. We argue that awareness of hidden biases on Wikipedia is critical to the objective and equitable application of the notability criteria across race and gender both on the encyclopedia and beyond.
{"title":"“Too Soon” to count? How gender and race cloud notability considerations on Wikipedia","authors":"Mackenzie Lemieux, Rebecca Zhang, F. Tripodi","doi":"10.1177/20539517231165490","DOIUrl":"https://doi.org/10.1177/20539517231165490","url":null,"abstract":"While research has explored the extent of gender bias and the barriers to women's inclusion on English-language Wikipedia, very little research has focused on the problem of racial bias within the encyclopedia. Despite advocacy groups' efforts to incrementally improve representation on Wikipedia, much is unknown regarding how biographies are assessed after creation. Applying a combination of web-scraping, deep learning, natural language processing, and qualitative analysis to pages of academics nominated for deletion on Wikipedia, we demonstrate how Wikipedia's notability guidelines are unequally applied across race and gender. We find that online presence predicts whether a Wikipedia page is kept or deleted for white male academics but that this metric is idiosyncratically applied for female and BIPOC academics. Further, women's pages, regardless of race, were more likely to be deemed “too soon” for Wikipedia. A deeper analysis of the deletion archives reveals that when the tag is used on a woman's biography it is done so outside of the community guidelines, referring to one's career stage rather than media/online coverage. We argue that awareness of hidden biases on Wikipedia is critical to the objective and equitable application of the notability criteria across race and gender both on the encyclopedia and beyond.","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":"45865978","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: 10.1177/20539517221146122
Edward B. Kang
There is a gap in existing critical scholarship that engages with the ways in which current “machine listening” or voice analytics/biometric systems intersect with the technical specificities of machine learning. This article examines the sociotechnical assemblage of machine learning techniques, practices, and cultures that underlie these technologies. After engaging with various practitioners working in companies that develop machine listening systems, ranging from CEOs, machine learning engineers, data scientists, and business analysts, among others, I bring attention to the centrality of “learnability” as a malleable conceptual framework that bends according to various “ground-truthing” practices in formalizing certain listening-based prediction tasks for machine learning. In response, I introduce a process I call Ground Truth Tracings to examine the various ontological translations that occur in training a machine to “learn to listen.” Ultimately, by further examining this notion of learnability through the aperture of power, I take insights acquired through my fieldwork in the machine listening industry and propose a strategically reductive heuristic through which the epistemological and ethical soundness of machine learning, writ large, can be contemplated.
现有的批判性学术在当前的“机器聆听”或语音分析/生物识别系统与机器学习的技术特性交叉的方式方面存在差距。本文考察了机器学习技术、实践和文化的社会技术组合，这些技术是这些技术的基础。在与开发机器监听系统的公司的各种从业者接触后，包括首席执行官、机器学习工程师、数据科学家和商业分析师等，我提请注意“可学习性”的中心地位，它是一个可塑的概念框架，在为机器学习正式化某些基于听力的预测任务时，它会根据各种“基本事实”实践而弯曲。作为回应，我介绍了一个我称之为Ground Truth Tracings的过程，以检查在训练机器“学会倾听”时发生的各种本体论翻译，我从机器听力行业的实地工作中获得了一些见解，并提出了一种战略性的简化启发式方法，通过该方法可以全面考虑机器学习的认识论和伦理合理性。
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