This chapter starts from three assumptions: (1) that digital technologies (DTs) are products of humans, and reversibly that such technologies have effects on and consequences for humans; (2) that DTs have profound, long-term effects on culture and social interaction; and (3) that research on such effects often disregards inherent social and cultural biases in DTs and discourses on digitalization and innovation. DTs tend to be depicted as “objective” and void of cultural contents and underpinnings. Therefore, and with an emphasis on the usefulness of combining different research methodologies, this chapter sheds light upon a number of discursive blind spots in these domains: technocentrism and normativism, homo- and heterocentrism, ego- and ethnocentrism, and what I call the reversed problem imperative. Drawing upon intercultural communication studies (ICCS), these blind spots are discussed in the light of DTs, scientific theories, and research methodologies. Moreover, the case is made that digital human sciences (DHV) offers a valuable contribution to the scientific understanding of the manifestations and consequences of digitalization. In particular, this chapter argues for the usefulness of “intermethodological,” interdisciplinary, intercultural, and integrative approaches in DHV.
{"title":"Revisiting the Human–Society–Technology Nexus: Intercultural Communication Studies as a Looking Glass for Scientific Self-Scrutiny in the Digital Human Sciences","authors":"J. Stier","doi":"10.16993/BBK.C","DOIUrl":"https://doi.org/10.16993/BBK.C","url":null,"abstract":"This chapter starts from three assumptions: (1) that digital technologies (DTs) are products of humans, and reversibly that such technologies have effects on and consequences for humans; (2) that DTs have profound, long-term effects on culture and social interaction; and (3) that research on such effects often disregards inherent social and cultural biases in DTs and discourses on digitalization and innovation. DTs tend to be depicted as “objective” and void of cultural contents and underpinnings. Therefore, and with an emphasis on the usefulness of combining different research methodologies, this chapter sheds light upon a number of discursive blind spots in these domains: technocentrism and normativism, homo- and heterocentrism, ego- and ethnocentrism, and what I call the reversed problem imperative. Drawing upon intercultural communication studies (ICCS), these blind spots are discussed in the light of DTs, scientific theories, and research methodologies. Moreover, the case is made that digital human sciences (DHV) offers a valuable contribution to the scientific understanding of the manifestations and consequences of digitalization. In particular, this chapter argues for the usefulness of “intermethodological,” interdisciplinary, intercultural, and integrative approaches in DHV.","PeriodicalId":332163,"journal":{"name":"Digital Human Sciences: New Objects – New Approaches","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115568228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The chapter investigates a new kind of YouTube phenomenon, the extended dialogue podcast, by combining three transdisciplinary approaches: (1) an intermedial analysis, (2) a media-historical analysis, and (3) a communication-theoretical analysis. The objects of study are the YouTube shows The Joe Rogan Experience and Hur kan vi? (a Swedish version of Rogan’s show). The shows air live on YouTube, which means that they are inherently multimodal, combining sound, moving images, occasional expositions of websites, and the viewers’ written commentaries. The shows are characterized by a strong emphasis on dialogue between the hosts and the invited guests, and by an extended duration, sometimes spanning over three hours of talk. Both shows are politically controversial, since the intention behind them is to challenge political correctness and self-censorship in mainstream media and public discourse. In order to understand the shows as digital objects, the chapter analyzes their specific mix of medialities and modalities. To understand them as historical phenomena, the chapter investigates how the shows relate to earlier media types, focusing on orality and its relation to literacy. And, finally, to understand the shows as political agents, their role in contemporary debates on media, power, and knowledge is the target of analysis.
本章研究了一种新的YouTube现象，即扩展对话播客，通过结合三种跨学科方法:(1)中间分析，(2)媒体历史分析，(3)传播理论分析。研究对象是YouTube上的节目《Joe Rogan Experience》和《Hur kan vi?》(罗根节目的瑞典版)。这些节目在YouTube上直播，这意味着它们本质上是多模式的，结合了声音、动态图像、偶尔的网站展示和观众的书面评论。这些节目的特点是非常强调主持人和受邀嘉宾之间的对话，并且持续时间很长，有时谈话超过三个小时。这两部剧在政治上都有争议，因为它们背后的意图是挑战主流媒体和公共话语中的政治正确和自我审查。为了理解作为数字对象的表演，本章分析了它们具体的媒介和形式组合。为了把它们理解为历史现象，本章调查了这些节目与早期媒体类型的关系，重点是口语及其与识字的关系。最后，为了理解这些节目作为政治媒介，它们在当代关于媒体、权力和知识的辩论中的作用是分析的目标。
{"title":"YouTube Podcasting, the New Orality, and Diversity of Thought: Intermediality, Media History, and Communication Theory as Methodological Approaches","authors":"Christer Johansson","doi":"10.16993/BBK.K","DOIUrl":"https://doi.org/10.16993/BBK.K","url":null,"abstract":"The chapter investigates a new kind of YouTube phenomenon, the extended dialogue podcast, by combining three transdisciplinary approaches: (1) an intermedial analysis, (2) a media-historical analysis, and (3) a communication-theoretical analysis. The objects of study are the YouTube shows The Joe Rogan Experience and Hur kan vi? (a Swedish version of Rogan’s show). The shows air live on YouTube, which means that they are inherently multimodal, combining sound, moving images, occasional expositions of websites, and the viewers’ written commentaries. The shows are characterized by a strong emphasis on dialogue between the hosts and the invited guests, and by an extended duration, sometimes spanning over three hours of talk. Both shows are politically controversial, since the intention behind them is to challenge political correctness and self-censorship in mainstream media and public discourse. In order to understand the shows as digital objects, the chapter analyzes their specific mix of medialities and modalities. To understand them as historical phenomena, the chapter investigates how the shows relate to earlier media types, focusing on orality and its relation to literacy. And, finally, to understand the shows as political agents, their role in contemporary debates on media, power, and knowledge is the target of analysis.","PeriodicalId":332163,"journal":{"name":"Digital Human Sciences: New Objects – New Approaches","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132980249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital media infrastructures give rise to texts that are socially interconnected in various forms of complex networks. These mediated phenomena can be analyzed through methods that trace relational data. Social network analysis (SNA) traces interconnections between social nodes, while natural language processing (NLP) traces intralinguistic properties of the text. These methods can be bracketed under the header “social big data.” Empirical and theoretical rigor begs a constructionist understanding of such data. Analysis is inherently perspective-bound; it is rarely a purely objective statistical exercise. Some kind of selection is always made, primarily out of practical necessity. Moreover, the agents observed (network participants producing the texts in question) all tend to make their own encodings, based on observational inferences, situated in the network topology. Recent developments in such methods have, for example, provided social scientific scholars with innovative means to address inconsistencies in comparative surveys in different languages, addressing issues of comparability and measurement equivalence. NLP provides novel, inductive ways of understanding word meanings as a function of their relational placement in syntagmatic and paradigmatic relations, thereby identifying biases in the relative meanings of words. Reflecting on current research projects, the chapter addresses key epistemological challenges in order to improve contextual understanding.
{"title":"Not a Mirror, but an Engine: Digital Methods for Contextual Analysis of “Social Big Data”","authors":"Jonas Andersson Schwarz","doi":"10.16993/BBK.B","DOIUrl":"https://doi.org/10.16993/BBK.B","url":null,"abstract":"Digital media infrastructures give rise to texts that are socially interconnected in various forms of complex networks. These mediated phenomena can be analyzed through methods that trace relational data. Social network analysis (SNA) traces interconnections between social nodes, while natural language processing (NLP) traces intralinguistic properties of the text. These methods can be bracketed under the header “social big data.” Empirical and theoretical rigor begs a constructionist understanding of such data. Analysis is inherently perspective-bound; it is rarely a purely objective statistical exercise. Some kind of selection is always made, primarily out of practical necessity. Moreover, the agents observed (network participants producing the texts in question) all tend to make their own encodings, based on observational inferences, situated in the network topology. Recent developments in such methods have, for example, provided social scientific scholars with innovative means to address inconsistencies in comparative surveys in different languages, addressing issues of comparability and measurement equivalence. NLP provides novel, inductive ways of understanding word meanings as a function of their relational placement in syntagmatic and paradigmatic relations, thereby identifying biases in the relative meanings of words. Reflecting on current research projects, the chapter addresses key epistemological challenges in order to improve contextual understanding.","PeriodicalId":332163,"journal":{"name":"Digital Human Sciences: New Objects – New Approaches","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133799222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information visualization has become a prevailing part of our visual culture, a research field, and a line of work for designers. The literature on information visualization is diverse, dominated by handbooks aimed at designers and illustrated surveys, sometimes with an emphasis on historical examples. This chapter makes a survey of the field of information visualization in order to map out and assess its analytical vocabulary. First, there is a need to refer to some different definitions and general concepts, such as the naming of phases in the visualization process, the naming of different visualization types, and their components. In order for scholars of human sciences to be able to identify, understand, and interpret information visualization as visual objects, some tools for suitable visual analysis of these objects would be useful. To meet this end, the chapter explores two particular interpretative frameworks. The first framework discusses social semiotics as an analytical tool. The second one analyzes the ethics and emotional appeal of information visualization.
{"title":"Interpreting Information Visualization","authors":"Karolina Uggla","doi":"10.16993/BBK.E","DOIUrl":"https://doi.org/10.16993/BBK.E","url":null,"abstract":"Information visualization has become a prevailing part of our visual culture, a research field, and a line of work for designers. The literature on information visualization is diverse, dominated by handbooks aimed at designers and illustrated surveys, sometimes with an emphasis on historical examples. This chapter makes a survey of the field of information visualization in order to map out and assess its analytical vocabulary. First, there is a need to refer to some different definitions and general concepts, such as the naming of phases in the visualization process, the naming of different visualization types, and their components. In order for scholars of human sciences to be able to identify, understand, and interpret information visualization as visual objects, some tools for suitable visual analysis of these objects would be useful. To meet this end, the chapter explores two particular interpretative frameworks. The first framework discusses social semiotics as an analytical tool. The second one analyzes the ethics and emotional appeal of information visualization.","PeriodicalId":332163,"journal":{"name":"Digital Human Sciences: New Objects – New Approaches","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132510314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Text mining in art history scholarship can tell us about the discipline itself, as well as artistic concerns at any given moment. The aim of this study is to develop and test a strategy for text mining from PDFs of journal articles that have nonstandard formatting and/or use notes rather than full bibliographies for references. While articles in the natural and social sciences typically adhere to standard formats, art history journals employ a variety of formatting styles that make bulk capture of citation and other textual data from the articles challenging. This study outlines a method by which researchers can extract data from journals articles, using a sample set from art history. Once extracted, the data from PDFs can be used to compare frequently used terms across samples and determine which scholars are most cited in either bibliographies or the main body text of articles. If the structure and layout of individual journals are carefully considered and the data is properly cleaned, a clear picture of the disciplinary influences and dependencies of the scholarship through citations and key terms can be obtained.
{"title":"Mining Art History: Bulk Converting Nonstandard PDFs to Text to Determine the Frequency of Citations and Key Terms in Humanities Articles","authors":"A. Wasielewski, A. Dahlgren","doi":"10.16993/BBK.L","DOIUrl":"https://doi.org/10.16993/BBK.L","url":null,"abstract":"Text mining in art history scholarship can tell us about the discipline itself, as well as artistic concerns at any given moment. The aim of this study is to develop and test a strategy for text mining from PDFs of journal articles that have nonstandard formatting and/or use notes rather than full bibliographies for references. While articles in the natural and social sciences typically adhere to standard formats, art history journals employ a variety of formatting styles that make bulk capture of citation and other textual data from the articles challenging. This study outlines a method by which researchers can extract data from journals articles, using a sample set from art history. Once extracted, the data from PDFs can be used to compare frequently used terms across samples and determine which scholars are most cited in either bibliographies or the main body text of articles. If the structure and layout of individual journals are carefully considered and the data is properly cleaned, a clear picture of the disciplinary influences and dependencies of the scholarship through citations and key terms can be obtained.","PeriodicalId":332163,"journal":{"name":"Digital Human Sciences: New Objects – New Approaches","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123131786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julia Pennlert, Björn Ekström, David Gunnarsson Lorentzen
Computer-assisted tools have introduced new ways to conduct research in the social sciences and the humanities. Digital methods, as an umbrella term for this line of methodology, have presented new vocabularies that affect research communities from different disciplines. The aim of this chapter is to discuss how digital methods can be understood and scrutinized as procedures of collecting, analyzing, visualizing, and interpreting born-digital and digitized material. We aim to problematize how the embracing of digital methods in the research process paves the way for certain knowledge claims. By adopting a teleoptical metaphor in order to scrutinize three case studies, our aim is to discuss the limitations and the possibilities for digital methods as a way of conducting science and research. The contribution addresses how and to what extent digital methods direct the researcher’s gaze toward particular focal points.
{"title":"Teleoptical Perspectives on Digital Methods: Scientific Claims and Consequences","authors":"Julia Pennlert, Björn Ekström, David Gunnarsson Lorentzen","doi":"10.16993/BBK.D","DOIUrl":"https://doi.org/10.16993/BBK.D","url":null,"abstract":"Computer-assisted tools have introduced new ways to conduct research in the social sciences and the humanities. Digital methods, as an umbrella term for this line of methodology, have presented new vocabularies that affect research communities from different disciplines. The aim of this chapter is to discuss how digital methods can be understood and scrutinized as procedures of collecting, analyzing, visualizing, and interpreting born-digital and digitized material. We aim to problematize how the embracing of digital methods in the research process paves the way for certain knowledge claims. By adopting a teleoptical metaphor in order to scrutinize three case studies, our aim is to discuss the limitations and the possibilities for digital methods as a way of conducting science and research. The contribution addresses how and to what extent digital methods direct the researcher’s gaze toward particular focal points.","PeriodicalId":332163,"journal":{"name":"Digital Human Sciences: New Objects – New Approaches","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133945933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the growing digitalization of the education sector, the availability of significant amounts of data, “big data,” creates possibilities for the use of artificial intelligence technologies to gain valuable insight into how students learn in higher education. Learning analytics technologies are examples of how deep learning algorithms can identify patterns in data and incorporate this “knowledge” into a model that is eventually integrated into the digital platforms used for interacting with students. This chapter introduces learning analytics as an emerging sociotechnical phenomenon in higher education. We situate the promises and expectations associated with learning analytics technologies, map their ties to emerging data-driven practices, and unpack the ethical concerns that are related to such practices via examples.Following this, we discuss three insights that we hope will provoke discussions among educators, researchers, and practitioners in higher education: (1) educational data-driven practices are highly context sensitive, (2) educational data-driven practices are not synonymous with evidence-based practices, and (3) innovative educational data-driven practices are not sustainable per se. This chapter calls for debating the role of emerging data-driven practices in higher education in relation to academic freedom and educational values embedded in critical pedagogy.
{"title":"Be Careful What You Wish For! Learning Analytics and the Emergence of Data-Driven Practices in Higher Education","authors":"T. Cerratto Pargman, C. McGrath","doi":"10.16993/BBK.I","DOIUrl":"https://doi.org/10.16993/BBK.I","url":null,"abstract":"With the growing digitalization of the education sector, the availability of significant amounts of data, “big data,” creates possibilities for the use of artificial intelligence technologies to gain valuable insight into how students learn in higher education. Learning analytics technologies are examples of how deep learning algorithms can identify patterns in data and incorporate this “knowledge” into a model that is eventually integrated into the digital platforms used for interacting with students. This chapter introduces learning analytics as an emerging sociotechnical phenomenon in higher education. We situate the promises and expectations associated with learning analytics technologies, map their ties to emerging data-driven practices, and unpack the ethical concerns that are related to such practices via examples.Following this, we discuss three insights that we hope will provoke discussions among educators, researchers, and practitioners in higher education: (1) educational data-driven practices are highly context sensitive, (2) educational data-driven practices are not synonymous with evidence-based practices, and (3) innovative educational data-driven practices are not sustainable per se. This chapter calls for debating the role of emerging data-driven practices in higher education in relation to academic freedom and educational values embedded in critical pedagogy.","PeriodicalId":332163,"journal":{"name":"Digital Human Sciences: New Objects – New Approaches","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132965857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
What is meant by “digital human sciences,” what types of research objects and topics does it circumscribe, and how does it overlap with or depart from related fields of research? This chapter introduces the digital human sciences as a new field of study, distinguishes it from the digital humanities, and discusses its multidisciplinary character.
{"title":"Introduction: Digital Human Sciences as a Field of Research","authors":"Sonya Petersson, U. Fors","doi":"10.16993/BBK.A","DOIUrl":"https://doi.org/10.16993/BBK.A","url":null,"abstract":"What is meant by “digital human sciences,” what types of research objects and topics does it circumscribe, and how does it overlap with or depart from related fields of research? This chapter introduces the digital human sciences as a new field of study, distinguishes it from the digital humanities, and discusses its multidisciplinary character.","PeriodicalId":332163,"journal":{"name":"Digital Human Sciences: New Objects – New Approaches","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133210613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital methodologies that revolve around the study of text have become popular in humanities disciplines such as literature and history. The potential for studying large groups of text automatically through techniques like text mining has meant that Franco Moretti’s “distant reading” has found more and more proponents. Art history, however, presents some unique barriers to uptake in computational techniques, not least the resistance of art historians, who have raised legitimate concerns about the relevance of such techniques. Many so-called “digital art history” projects focus only on formal characteristics while ignoring context, which does not reflect the nature of art historical study in the last 60 years. The technical challenges of using digital methodologies in the study of art and visual culture have limited the potential benefits of such techniques as well: the methodologies used for images are more complex than text recognition and there is simply not enough preexisting data that needs to be sorted in this way.
{"title":"The Growing Pains of Digital Art History: Issues for the Study of Art Using Computational Methods","authors":"A. Wasielewski","doi":"10.16993/BBK.F","DOIUrl":"https://doi.org/10.16993/BBK.F","url":null,"abstract":"Digital methodologies that revolve around the study of text have become popular in humanities disciplines such as literature and history. The potential for studying large groups of text automatically through techniques like text mining has meant that Franco Moretti’s “distant reading” has found more and more proponents. Art history, however, presents some unique barriers to uptake in computational techniques, not least the resistance of art historians, who have raised legitimate concerns about the relevance of such techniques. Many so-called “digital art history” projects focus only on formal characteristics while ignoring context, which does not reflect the nature of art historical study in the last 60 years. The technical challenges of using digital methodologies in the study of art and visual culture have limited the potential benefits of such techniques as well: the methodologies used for images are more complex than text recognition and there is simply not enough preexisting data that needs to be sorted in this way.","PeriodicalId":332163,"journal":{"name":"Digital Human Sciences: New Objects – New Approaches","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130126854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A major starting point is that transparency is a condition for privacy in the context of personal data processing, especially when based on artificial intelligence (AI) methods. A major keyword here is openness, which however is not equivalent to transparency. This is explained by the fact that an organization may very well be governed by principles of openness but still not provide transparency due to insufficient access rights and lacking implementation of those rights. Given these hypotheses, the chapter investigates and illuminates ways forward in recognition of algorithms, machine learning, and big data as critical success factors of personal data processing based on AI—that is, if privacy is to be preserved. In these circumstances, autonomy of technology calls for attention and needs to be challenged from a variety of perspectives. Not least, a legal approach to digital human sciences appears to be a resource to examine further. This applies, for instance, when data subjects in the public as well as in the private sphere are exposed to AI for better or for worse. Providing what may be referred to as a legal shield between user and application might be one remedy to shortcomings in this context.
{"title":"Legal AI from a Privacy Point of View: Data Protection and Transparency in Focus","authors":"Cecilia Magnusson Sjöberg","doi":"10.16993/BBK.H","DOIUrl":"https://doi.org/10.16993/BBK.H","url":null,"abstract":"A major starting point is that transparency is a condition for privacy in the context of personal data processing, especially when based on artificial intelligence (AI) methods. A major keyword here is openness, which however is not equivalent to transparency. This is explained by the fact that an organization may very well be governed by principles of openness but still not provide transparency due to insufficient access rights and lacking implementation of those rights. Given these hypotheses, the chapter investigates and illuminates ways forward in recognition of algorithms, machine learning, and big data as critical success factors of personal data processing based on AI—that is, if privacy is to be preserved. In these circumstances, autonomy of technology calls for attention and needs to be challenged from a variety of perspectives. Not least, a legal approach to digital human sciences appears to be a resource to examine further. This applies, for instance, when data subjects in the public as well as in the private sphere are exposed to AI for better or for worse. Providing what may be referred to as a legal shield between user and application might be one remedy to shortcomings in this context.","PeriodicalId":332163,"journal":{"name":"Digital Human Sciences: New Objects – New Approaches","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131272145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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