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"Offenhuber, Dietmar"
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Shapes and frictions of synthetic data
Synthetic data are computer-generated data that mimic and substitute empirical observations without directly corresponding to real-world phenomena. Widely used in privacy protection, machine learning, and simulation, synthetic data is an emerging field only just beginning to be explored in the social sciences and critical data studies. However, recent developments, such as the use of synthetic data in the US Census and American Community Survey, make a reflection on the nature and implications of synthetic data urgent. While earlier work focused mostly on training data for machine-learning models, this paper presents a broad typology of synthetic data and discusses its frictions. The main argument presented is that the traditional representational model of data as symbolic references to corresponding physical or conceptual objects is insufficient for understanding and critically engaging with issues and implications of synthetic data. The paper discusses an alternative relational model, which defines data not through an object of reference but based on “who uses them, how and for which purposes”. The relational model is more productive for capturing the fact that synthetic data are defined through their purpose; their performance in a particular situation (such as training a machine learning model); and a context-dependent operationalization of evidence. The post-representational anything-goes epistemology of synthetic data can be productively challenged through a forensic approach that foregrounds the outliers, artifacts, and gaps in datasets as meaningful information.
Journal Article
Decoding the City
The MIT based SENSEable City Lab under Carlo Ratti is one of the research centers that deal with the flow of people and goods, but also of refuse that moves around the world. Experience with large-scale infrastructure projects suggest that more complex and above all flexible answers must be sought to questions of transportation or disposal. This edition, edited by Dietmar Offenhuber and Carlo Ratti, shows how Big Data change reality and, hence, the way we deal with the city. It discusses the impact of real-time data on architecture and urban planning, using examples developed in the SENSEable City Lab. They demonstrate how the Lab interprets digital data as material that can be used for the formulation of a different urban future. It also looks at the negative aspects of the city-related data acquisition and control. The authors address issues with which urban planning disciplines will work intensively in the future: questions that not only radically and critically review, but also change fundamentally, the existing tasks and how the professions view their own roles.
eBook
Uncharted Territoriality in Coproduction: The Motivations for 311 Reporting
A central question for programs that involve constituents in the coproduction of government services is: what motivates constituents to participate? This study compares two perspectives on this question: the traditional public-as-citizen model treats participation as a function of a general civic disposition that extends to many forms of civic and political participation (e.g. volunteering and voting); and we introduce the public-as-partner model, which argues that a given program might rely on any of the diverse array of human motivations, depending on the specific nature of participation required. We compare these using 311 systems, which provide a hotline and online tools for requesting nonemergency government services (e.g. graffiti removal), evaluating whether using 311 to contribute to neighborhood maintenance primarily reflects a civic disposition or is additionally motivated by a capacity for territoriality (i.e. identifying with and claiming responsibility for spaces), per the public-as-partner model. The study links three forms of information at the individual level for a sample of 311 users from Boston, Massachusetts (n = 722): objective reporting activity, derived from the 311 archive; a user survey including self-reports of civic activities and territorial motives; and voter registration records. Controlling for demographics and the contextual effects of home neighborhood, higher territorial motives predicted a greater likelihood of a person reporting any issues of public concern and reporting more issues over a broader geographical range in one's home neighborhood (where >80% of reports are made). Civic activities and voting predicted a greater likelihood of reporting in nonhome neighborhoods (e.g. work). This dichotomy highlights the distinction between the two models in conceptualizing the motivations for participation in coproduction.The article explores how to extend this logic to the assessment of participation, outreach, and disparities in access across programs.
Journal Article
Patterns of Choice: The Prix Ars Electronica Jury Sessions
This article investigates the social structures reflected in the annual jury sessions of the Prix Ars Electronica, a major media art competition— the composition, the temporal evolution and ultimately the decisions of these juries. The author focuses on three different structures: the network of co-jurors across different categories and years, the co-artist network formed by the jury decisions, and finally the interaction between these two networks. The results not only reveal different roles of individuals in the jury process but also reflect the evolution of the field in general. Based solely on public data, the results show a multifaceted picture of a dynamic field.
Journal Article
Visual Anecdote
The discourse on information visualization often remains limited to the exploratory function - its potential for discovering patterns in the data. However, visual representations also have a rhetorical function: they demonstrate, persuade, and facilitate communication. In observing how visualization is used in presentations and discussions, I often notice the use of what could be called \"visual anecdotes.\" Small narratives are tied to individual data points in the visualization, giving human context to the data and rooting the abstract representation in personal experience. This paper argues that these narratives are more than just illustrations of the dataset; they constitute a central epistemological element of the visualization. By considering these narrative elements as parts of the visualization, its design and knowledge organization appear in a different light. This paper investigates how the \"story\" of data representation is delivered. By means of ethnographic interviews and observations, the author highlights the different aspects of the visual anecdote, a specific point where the exploratory and the rhetorical functions of visualization meet.
Journal Article
Synthetic Data and the Shifting Ground of Truth
The emergence of synthetic data for privacy protection, training data generation, or simply convenient access to quasi-realistic data in any shape or volume complicates the concept of ground truth. Synthetic data mimic real-world observations, but do not refer to external features. This lack of a representational relationship, however, not prevent researchers from using synthetic data as training data for AI models and ground truth repositories. It is claimed that the lack of data realism is not merely an acceptable tradeoff, but often leads to better model performance than realistic data: compensate for known biases, prevent overfitting and support generalization, and make the models more robust in dealing with unexpected outliers. Indeed, injecting noisy and outright implausible data into training sets can be beneficial for the model. This greatly complicates usual assumptions based on which representational accuracy determines data fidelity (garbage in - garbage out). Furthermore, ground truth becomes a self-referential affair, in which the labels used as a ground truth repository are themselves synthetic products of a generative model and as such not connected to real-world observations. My paper examines how ML researchers and practitioners bootstrap ground truth under such paradoxical circumstances without relying on the stable ground of representation and real-world reference. It will also reflect on the broader implications of a shift from a representational to what could be described as a mimetic or iconic concept of data.
Paper
What we talk about when we talk about data physicality
Data physicalizations \"map data to physical form,\" yet many canonical examples are not based on data sets. To address this contradiction, I argue that the practice of physicalization forces us to rethink traditional notions of data. This paper proposes a conceptual framework to examine how physicalizations relate to data. This paper develops a two-dimensional conceptual space for comparing different perspectives on data used in physicalization, drawing from design theory and critical data studies literature. One axis distinguishes between epistemological and ontological perspectives, focusing on the relationship between data and the mind. The second axis distinguishes how data relate to the world, differentiating between representational and relational perspectives. To clarify the aesthetic and conceptual implications of these different perspectives, the paper discusses examples of data physicalization for each quadrant of the continuous space. It further uses the framework to examine the explicit and implicit assumptions about data in physicalization literature. As a theoretical paper, it encourages practitioners to think about how data relate to the manifestations and the phenomena they try to capture. It invites exploration of the relationship between data and the world as a generative source of creative tension.
Paper
Data by Proxy -- Material Traces as Autographic Visualizations
Information visualization limits itself, per definition, to the domain of symbolic information. This paper discusses arguments why the field should also consider forms of data that are not symbolically encoded, including physical traces and material indicators. Continuing a provocation presented by Pat Hanrahan in his 2004 IEEE Vis capstone address, this paper compares physical traces to visualizations and describes the techniques and visual practices for producing, revealing, and interpreting them. By contrasting information visualization with a speculative counter model of autographic visualization, this paper examines the design principles for material data. Autographic visualization addresses limitations of information visualization, such as the inability to directly reflect the material circumstances of data generation. The comparison between the two models allows probing the epistemic assumptions behind information visualization and uncovers linkages with the rich history of scientific visualization and trace reading.
Paper