Pub Date : 2023-01-01DOI: 10.1177/20539517231177617
Bartosz Ślosarski
The article proposes the theoretical category of data arenas as a relational field for strategic actors in diverse areas of the contentious politics of data (Beraldo and Milan, 2019). The paper argues that the conceptualization of data activism needs to be related to the immediate data arena in which the action takes place, in order to select the interactive opportunities and threats for emerging data-driven repertoires of action. To fully work through the relational dynamics of data activism, it is necessary to move from a conceptualization of data infrastructure to the notion of data arenas as an ‘open-ended bundle of rules and resources that allows certain kinds of interaction to proceed’ (Jasper, 2006: 141). Using the case of environmental data activism, I highlight four key dimensions to study: (a) strategic use of data as capital that differentiates and positions actors, as well as influences their further choices; (b) practices of defining the boundaries of the problem on which the arena focuses and outlining the pool of actors who participate in the process of solving it; (3) sets of relationships among the outlined pool of actors which represent opportunities and threats for the actors, related to the position they occupy within an arena; and (4) power as the ability to control and shape an arena. Data arena approach shed new light on data activism as a relational practice, combining the latest developments in research on data contexts and the political situatedness of data with the emerging field of research on data activism.
{"title":"Data arenas: The relational dynamics of data activism","authors":"Bartosz Ślosarski","doi":"10.1177/20539517231177617","DOIUrl":"https://doi.org/10.1177/20539517231177617","url":null,"abstract":"The article proposes the theoretical category of data arenas as a relational field for strategic actors in diverse areas of the contentious politics of data (Beraldo and Milan, 2019). The paper argues that the conceptualization of data activism needs to be related to the immediate data arena in which the action takes place, in order to select the interactive opportunities and threats for emerging data-driven repertoires of action. To fully work through the relational dynamics of data activism, it is necessary to move from a conceptualization of data infrastructure to the notion of data arenas as an ‘open-ended bundle of rules and resources that allows certain kinds of interaction to proceed’ (Jasper, 2006: 141). Using the case of environmental data activism, I highlight four key dimensions to study: (a) strategic use of data as capital that differentiates and positions actors, as well as influences their further choices; (b) practices of defining the boundaries of the problem on which the arena focuses and outlining the pool of actors who participate in the process of solving it; (3) sets of relationships among the outlined pool of actors which represent opportunities and threats for the actors, related to the position they occupy within an arena; and (4) power as the ability to control and shape an arena. Data arena approach shed new light on data activism as a relational practice, combining the latest developments in research on data contexts and the political situatedness of data with the emerging field of research on data activism.","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":"41726502","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: 10.1177/20539517231156518
Rosie Graham
What questions should we ask of Google’s Autocomplete suggestions? This article highlights some of the key ethical issues raised by Google’s automated suggestion tool that provides potential queries below a user’s search box. Much of the discourse surrounding Google’s suggestions has been framed through legal cases in which complex issues can become distilled into black-and-white questions of the law. For example, do Google have to remove a particular suggestion and do they have to pay a settlement for damages? This commentary argues that shaping this discourse along primarily legal lines obscures many of these other moral dimensions raised by Google Autocomplete. Building from existing typologies, this commentary first outlines the legal discourse before exploring five additional ethical challenges, each framed around a particular moral question in which all users have a stake. Written in the form of a commentary, the purpose of this article is not to conclusively answer the ethical questions raised, but rather to give an account of why these particular questions are worth debating. Autocomplete’s suggestions are not simply a mirror of what users are typing into Google’s search bar. Google’s official statement is that “Autocomplete is a time-saving but complex feature. It doesn’t simply display the most common queries on a given topic” but “also predict[s] individual words and phrases that are based on both real searches as well as word patterns found across the web” (Google, 2022). Both its underlying methods and associated terminology have changed throughout time, shifting between providing completions, suggestions, and predictions. In doing so, the grounds for potential critique are ever-changing, which means that Google’s approach to Autocomplete deserves significant scrutiny.
{"title":"The ethical dimensions of Google autocomplete","authors":"Rosie Graham","doi":"10.1177/20539517231156518","DOIUrl":"https://doi.org/10.1177/20539517231156518","url":null,"abstract":"What questions should we ask of Google’s Autocomplete suggestions? This article highlights some of the key ethical issues raised by Google’s automated suggestion tool that provides potential queries below a user’s search box. Much of the discourse surrounding Google’s suggestions has been framed through legal cases in which complex issues can become distilled into black-and-white questions of the law. For example, do Google have to remove a particular suggestion and do they have to pay a settlement for damages? This commentary argues that shaping this discourse along primarily legal lines obscures many of these other moral dimensions raised by Google Autocomplete. Building from existing typologies, this commentary first outlines the legal discourse before exploring five additional ethical challenges, each framed around a particular moral question in which all users have a stake. Written in the form of a commentary, the purpose of this article is not to conclusively answer the ethical questions raised, but rather to give an account of why these particular questions are worth debating. Autocomplete’s suggestions are not simply a mirror of what users are typing into Google’s search bar. Google’s official statement is that “Autocomplete is a time-saving but complex feature. It doesn’t simply display the most common queries on a given topic” but “also predict[s] individual words and phrases that are based on both real searches as well as word patterns found across the web” (Google, 2022). Both its underlying methods and associated terminology have changed throughout time, shifting between providing completions, suggestions, and predictions. In doing so, the grounds for potential critique are ever-changing, which means that Google’s approach to Autocomplete deserves significant scrutiny.","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":"47412208","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: 10.1177/20539517231182393
Riccardo Pronzato
In recent years, several scholars have highlighted the necessity to scrutinize the practices and material settings in which algorithmic models are designed, in order to unpack the working activities and socio-cultural constructs underlying their production and deployment process. Drawing on a multisited ethnography, this paper investigates the practices of tech workers within the corporate environment of an internet television platform, the hierarchical relationships between different professional figures, and how these individuals frame algorithms and contribute to the enactment of these systems with their activities. Findings highlight the hierarchical organization of tech work and the subordination of operative figures to the goals imposed by business clients and to both internal and external forms of control. Specifically, it emerges how the subalternity of tech workers is materially and discursively constructed and forms of causal, dispositional and facilitative power exerted on them. In this environment, frictions, negotiations as well as concealing strategies by tech workers regarding the design and meaning of algorithms emerge, thus showing their cultural, contingent and multiple composition. Within the framework of Giddens’ structure/agency cycle, it is shown how everyday working activities and relationships contribute to the reproduction of hegemonic arrangements in the workplace, and how these hegemonic arrangements are at the core of algorithmic production, thus playing a key role in the framing, construction and enactment of these systems.
{"title":"Algorithms and hegemony in the workplace: Negotiating design and values in an Italian television platform","authors":"Riccardo Pronzato","doi":"10.1177/20539517231182393","DOIUrl":"https://doi.org/10.1177/20539517231182393","url":null,"abstract":"In recent years, several scholars have highlighted the necessity to scrutinize the practices and material settings in which algorithmic models are designed, in order to unpack the working activities and socio-cultural constructs underlying their production and deployment process. Drawing on a multisited ethnography, this paper investigates the practices of tech workers within the corporate environment of an internet television platform, the hierarchical relationships between different professional figures, and how these individuals frame algorithms and contribute to the enactment of these systems with their activities. Findings highlight the hierarchical organization of tech work and the subordination of operative figures to the goals imposed by business clients and to both internal and external forms of control. Specifically, it emerges how the subalternity of tech workers is materially and discursively constructed and forms of causal, dispositional and facilitative power exerted on them. In this environment, frictions, negotiations as well as concealing strategies by tech workers regarding the design and meaning of algorithms emerge, thus showing their cultural, contingent and multiple composition. Within the framework of Giddens’ structure/agency cycle, it is shown how everyday working activities and relationships contribute to the reproduction of hegemonic arrangements in the workplace, and how these hegemonic arrangements are at the core of algorithmic production, thus playing a key role in the framing, construction and enactment of these systems.","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":"43356507","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: 10.1177/20539517231160528
Shirley Kempeneer, Frederik Heylen
The users, sensors and networks of the Internet of Things generate huge amounts of data. Given the sophisticated (artificially intelligent) algorithms, computing power and software available, we would expect governments to have successfully completed their digital transformation into Jane Fountain's (2001) ‘Virtual State’. In practice, despite heavy investments, governments often fail to enact new digital technologies in an efficient, appropriate or fair way. This article provides an overview of techno-rational and socio-political failures and solutions at the macro-, meso- and micro-level to support digital transformation. The reviewed articles suggest a modest approach to digital transformation, with an emphasis on high-quality in-house IT infrastructure and expertise, but also better collaborative networks and strong leadership ensuring human oversight.
{"title":"Virtual state, where are you? A literature review, framework and agenda for failed digital transformation","authors":"Shirley Kempeneer, Frederik Heylen","doi":"10.1177/20539517231160528","DOIUrl":"https://doi.org/10.1177/20539517231160528","url":null,"abstract":"The users, sensors and networks of the Internet of Things generate huge amounts of data. Given the sophisticated (artificially intelligent) algorithms, computing power and software available, we would expect governments to have successfully completed their digital transformation into Jane Fountain's (2001) ‘Virtual State’. In practice, despite heavy investments, governments often fail to enact new digital technologies in an efficient, appropriate or fair way. This article provides an overview of techno-rational and socio-political failures and solutions at the macro-, meso- and micro-level to support digital transformation. The reviewed articles suggest a modest approach to digital transformation, with an emphasis on high-quality in-house IT infrastructure and expertise, but also better collaborative networks and strong leadership ensuring human oversight.","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":"42230082","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: 10.1177/20539517231168101
H. Faxon, Kendra Kintzi, Van Tran, Kay Zak Wine, Swan Ye Htut
Despite perennial hope in the democratic possibilities of the internet, the rise of digital authoritarianism threatens online and offline freedom across much of the world. Yet while critical data studies has expanded its geographic focus, limited work to date has examined digital mobilization in the agrarian communities that comprise much of the Global South. This article advances the concept of “organic online politics,” to demonstrate how digital mobilization grows from specific rural conditions, material concerns, and repertoires of resistance, within the constraints of authoritarian violence and internet control. To do so, we examine social media interaction in the wake of the 2021 military coup in Myanmar, an agrarian nation with recent, rapid digital connection that corresponded with a decade-long democratic turn. Analyzing an original archive of over 2000 Facebook posts collected from popular farming pages and groups, we find a massive drop-off in online activity after the military coup and analyze the shifting temporalities of digital mobilization. Crucially, we highlight the embeddedness of online interaction within the material concerns of farming communities, examining how social media become a key forum for negotiating political crisis in Myanmar's countryside. These findings call attention to rural digital subcultures as fertile sites of investigation and point toward the need for future scholarship on data practices that attends to rooted agrarian struggles.
{"title":"Organic online politics: Farmers, Facebook, and Myanmar's military coup","authors":"H. Faxon, Kendra Kintzi, Van Tran, Kay Zak Wine, Swan Ye Htut","doi":"10.1177/20539517231168101","DOIUrl":"https://doi.org/10.1177/20539517231168101","url":null,"abstract":"Despite perennial hope in the democratic possibilities of the internet, the rise of digital authoritarianism threatens online and offline freedom across much of the world. Yet while critical data studies has expanded its geographic focus, limited work to date has examined digital mobilization in the agrarian communities that comprise much of the Global South. This article advances the concept of “organic online politics,” to demonstrate how digital mobilization grows from specific rural conditions, material concerns, and repertoires of resistance, within the constraints of authoritarian violence and internet control. To do so, we examine social media interaction in the wake of the 2021 military coup in Myanmar, an agrarian nation with recent, rapid digital connection that corresponded with a decade-long democratic turn. Analyzing an original archive of over 2000 Facebook posts collected from popular farming pages and groups, we find a massive drop-off in online activity after the military coup and analyze the shifting temporalities of digital mobilization. Crucially, we highlight the embeddedness of online interaction within the material concerns of farming communities, examining how social media become a key forum for negotiating political crisis in Myanmar's countryside. These findings call attention to rural digital subcultures as fertile sites of investigation and point toward the need for future scholarship on data practices that attends to rooted agrarian struggles.","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":"44149784","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: 10.1177/20539517221149097
Rodrigo De La Fabián, Álvaro Jiménez-Molina, Francisco Pizarro Obaid
This article critically examines the emergence and uses of digital phenotyping in contemporary psychiatry. From an analysis of its discourses and practices, we show that digital phenotyping diffusion is directly related to its promise to solve some of the major impasses of the so-called "neuro-turn" in contemporary psychiatry. However, more than a new tool to address old objects of pre-digital psychiatry, we consider digital phenotyping as participating from a new onto-epistemological matrix, the “neuro-digital complex,” which entails the redefinition of psychiatric objects (e.g., brain and mind), diagnostic categories and procedures, subjectivities (e.g., users of mental health apps), and the emergence of a new regime of truth which promises to reveal the neuropsychological core at the individual scale. Despite this techno-utopia, digital phenotyping does not produce neutral mirrors for self-knowledge. We show that it resorts to population statistics, grounded truth data sets built with pre-digital neuropsychological assumptions, and human categorization processes. Nevertheless, we propose not to approach this gap as a misleading ideological fact but to emphasize its productive possibilities. From this perspective, the gap becomes the measure between whom we think we are and who we really are, working as a guide to conduct our lives in neuropsychological terms. Thus, we conclude that, rather than providing personalized diagnoses and treatments, digital phenotyping produces individualized pathways to normalization and neuropsychologization.
{"title":"A critical analysis of digital phenotyping and the neuro-digital complex in psychiatry","authors":"Rodrigo De La Fabián, Álvaro Jiménez-Molina, Francisco Pizarro Obaid","doi":"10.1177/20539517221149097","DOIUrl":"https://doi.org/10.1177/20539517221149097","url":null,"abstract":"This article critically examines the emergence and uses of digital phenotyping in contemporary psychiatry. From an analysis of its discourses and practices, we show that digital phenotyping diffusion is directly related to its promise to solve some of the major impasses of the so-called \"neuro-turn\" in contemporary psychiatry. However, more than a new tool to address old objects of pre-digital psychiatry, we consider digital phenotyping as participating from a new onto-epistemological matrix, the “neuro-digital complex,” which entails the redefinition of psychiatric objects (e.g., brain and mind), diagnostic categories and procedures, subjectivities (e.g., users of mental health apps), and the emergence of a new regime of truth which promises to reveal the neuropsychological core at the individual scale. Despite this techno-utopia, digital phenotyping does not produce neutral mirrors for self-knowledge. We show that it resorts to population statistics, grounded truth data sets built with pre-digital neuropsychological assumptions, and human categorization processes. Nevertheless, we propose not to approach this gap as a misleading ideological fact but to emphasize its productive possibilities. From this perspective, the gap becomes the measure between whom we think we are and who we really are, working as a guide to conduct our lives in neuropsychological terms. Thus, we conclude that, rather than providing personalized diagnoses and treatments, digital phenotyping produces individualized pathways to normalization and neuropsychologization.","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":"45840469","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: 10.1177/20539517231153811
Troels Krarup, Maja Horst
Rapid innovation in digital services relying on artificial intelligence (AI) challenges existing regulations across a wide array of policy fields. The European Union (EU) has pursued a position as global leader on ethical AI regulation in explicit contrast to US laissez-faire and Chinese state surveillance approaches. This article asks how the seemingly heterogeneous approaches of market making and ethical AI are woven together at a deeper level in EU regulation. Combining quantitative analysis of all official EU documents on AI with in-depth reading of key reports, communications, and legislative corpora, we demonstrate that single market integration constitutes a fundamental but overlooked engine and structuring principle of new AI regulation. Under the influence of this principle, removing barriers to competition and the free flow of data, on the one hand, and securing ethical and responsible AI, on the other hand, are seen as compatible and even mutually reinforcing.
{"title":"European artificial intelligence policy as digital single market making","authors":"Troels Krarup, Maja Horst","doi":"10.1177/20539517231153811","DOIUrl":"https://doi.org/10.1177/20539517231153811","url":null,"abstract":"Rapid innovation in digital services relying on artificial intelligence (AI) challenges existing regulations across a wide array of policy fields. The European Union (EU) has pursued a position as global leader on ethical AI regulation in explicit contrast to US laissez-faire and Chinese state surveillance approaches. This article asks how the seemingly heterogeneous approaches of market making and ethical AI are woven together at a deeper level in EU regulation. Combining quantitative analysis of all official EU documents on AI with in-depth reading of key reports, communications, and legislative corpora, we demonstrate that single market integration constitutes a fundamental but overlooked engine and structuring principle of new AI regulation. Under the influence of this principle, removing barriers to competition and the free flow of data, on the one hand, and securing ethical and responsible AI, on the other hand, are seen as compatible and even mutually reinforcing.","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":"48671953","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: 10.1177/20539517231164120
M. Leese, Silvan Pollozek
In this paper, we investigate the temporal implications of data in law enforcement and border control. We start from the assumption that the velocity of knowledge and action is defined by heterogeneous formations and interactions of various actors, sites, and materials. To analyze these formations and interactions, we introduce and unpack the concept of “data temporality.” Data temporality explicates how the speed of knowledge and action in datafied environments unfolds in close correspondence with (1) variegated social rhythms, (2) technological inscriptions, and (3) the balancing of speed with other priorities. Specifically, we use the notion of data temporality as a heuristic tool to explore the entanglements of data and time within two case studies: Frontex’ Joint Operation Reporting Application and the predictive policing software PRECOBS. The analysis identifies two key themes in the empirical constitution of data temporalities. The first one pertains to the creation of events as reference points for temporally situated knowledge and action. And the second one pertains to timing and actionability, that is, the question of when interventions based on data analysis should be triggered.
{"title":"Not so fast! Data temporalities in law enforcement and border control","authors":"M. Leese, Silvan Pollozek","doi":"10.1177/20539517231164120","DOIUrl":"https://doi.org/10.1177/20539517231164120","url":null,"abstract":"In this paper, we investigate the temporal implications of data in law enforcement and border control. We start from the assumption that the velocity of knowledge and action is defined by heterogeneous formations and interactions of various actors, sites, and materials. To analyze these formations and interactions, we introduce and unpack the concept of “data temporality.” Data temporality explicates how the speed of knowledge and action in datafied environments unfolds in close correspondence with (1) variegated social rhythms, (2) technological inscriptions, and (3) the balancing of speed with other priorities. Specifically, we use the notion of data temporality as a heuristic tool to explore the entanglements of data and time within two case studies: Frontex’ Joint Operation Reporting Application and the predictive policing software PRECOBS. The analysis identifies two key themes in the empirical constitution of data temporalities. The first one pertains to the creation of events as reference points for temporally situated knowledge and action. And the second one pertains to timing and actionability, that is, the question of when interventions based on data analysis should be triggered.","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":"48139171","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: 10.1177/20539517231169731
Xuenan Cao, Roozbeh Yousefzadeh
The right to artificial intelligence (AI) explainability has consolidated as a consensus in the research community and policy-making. However, a key component of explainability has been missing: extrapolation, which can reveal whether a model is making inferences beyond the boundaries of its training. We report that AI models extrapolate outside their range of familiar data, frequently and without notifying the users and stakeholders. Knowing whether a model has extrapolated or not is a fundamental insight that should be included in explaining AI models in favor of transparency, accountability, and fairness. Instead of dwelling on the negatives, we offer ways to clear the roadblocks in promoting AI transparency. Our commentary accompanies practical clauses useful to include in AI regulations such as the AI Bill of Rights, the National AI Initiative Act in the United States, and the AI Act by the European Commission.
{"title":"Extrapolation and AI transparency: Why machine learning models should reveal when they make decisions beyond their training","authors":"Xuenan Cao, Roozbeh Yousefzadeh","doi":"10.1177/20539517231169731","DOIUrl":"https://doi.org/10.1177/20539517231169731","url":null,"abstract":"The right to artificial intelligence (AI) explainability has consolidated as a consensus in the research community and policy-making. However, a key component of explainability has been missing: extrapolation, which can reveal whether a model is making inferences beyond the boundaries of its training. We report that AI models extrapolate outside their range of familiar data, frequently and without notifying the users and stakeholders. Knowing whether a model has extrapolated or not is a fundamental insight that should be included in explaining AI models in favor of transparency, accountability, and fairness. Instead of dwelling on the negatives, we offer ways to clear the roadblocks in promoting AI transparency. Our commentary accompanies practical clauses useful to include in AI regulations such as the AI Bill of Rights, the National AI Initiative Act in the United States, and the AI Act by the European Commission.","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":"46691380","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: 10.1177/20539517231158636
T. Deruelle, Veronika Kalouguina, Philipp Trein, J. Wagner
A crucial challenge for personalized health is the handling of individuals’ data and specifically the protection of their privacy. Secure storage of personal health data is of paramount importance to convince citizens to collect personal health data. In this survey experiment, we test individuals’ willingness to produce and store personal health data, based on different storage options and whether this data is presented as common good or private good. In this paper, we focus on the nonmedical context with two means to self-produce data: connected devices that record physical activity and genetic tests that appraise risks of diseases. We use data from a survey experiment fielded in Switzerland in March 2020 and perform regression analyses on a representative sample of Swiss citizens in the French- and German-speaking cantons. Our analysis shows that respondents are more likely to use both apps and tests when their data is framed as a private good to be stored by individuals themselves. Our results demonstrate that concerns regarding the privacy of personal heath data storage trumps any other variable when it comes to the willingness to use personalized health technologies. Individuals prefer a data storage format where they retain control over the data. Ultimately, this study presents results susceptible to inform decision-makers in designing privacy in personalized health initiatives.
{"title":"Designing privacy in personalized health: An empirical analysis","authors":"T. Deruelle, Veronika Kalouguina, Philipp Trein, J. Wagner","doi":"10.1177/20539517231158636","DOIUrl":"https://doi.org/10.1177/20539517231158636","url":null,"abstract":"A crucial challenge for personalized health is the handling of individuals’ data and specifically the protection of their privacy. Secure storage of personal health data is of paramount importance to convince citizens to collect personal health data. In this survey experiment, we test individuals’ willingness to produce and store personal health data, based on different storage options and whether this data is presented as common good or private good. In this paper, we focus on the nonmedical context with two means to self-produce data: connected devices that record physical activity and genetic tests that appraise risks of diseases. We use data from a survey experiment fielded in Switzerland in March 2020 and perform regression analyses on a representative sample of Swiss citizens in the French- and German-speaking cantons. Our analysis shows that respondents are more likely to use both apps and tests when their data is framed as a private good to be stored by individuals themselves. Our results demonstrate that concerns regarding the privacy of personal heath data storage trumps any other variable when it comes to the willingness to use personalized health technologies. Individuals prefer a data storage format where they retain control over the data. Ultimately, this study presents results susceptible to inform decision-makers in designing privacy in personalized health 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":"45290123","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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