Predicting the Future: Art and Algorithms

Róna‐Tas, Ákos

doi:10.1093/ser/mwaa040

Cited by 19 publications

(8 citation statements)

References 47 publications

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“…Accordingly, an enormous commercial market has emerged for data about individuals (Fourcade and Healy, 2017; Zuboff, 2019). Additionally, state institutions have increased appetite for this data, which is useful for various algorithmic governance measures (Liu, 2019; Rona-Tas, 2020). Today, issues regarding trust, privacy, and surveillance dominate the ethical and social debates on these technologies.…”

Section: Introductionmentioning

confidence: 99%

Making sense of algorithms: Relational perception of contact tracing and risk assessment during COVID-19

Liu

Graham

2021

Big Data & Society

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Governments and citizens of nearly every nation have been compelled to respond to COVID-19. Many measures have been adopted, including contact tracing and risk assessment algorithms, whereby citizen whereabouts are monitored to trace contact with other infectious individuals in order to generate a risk status via algorithmic evaluation. Based on 38 in-depth interviews, we investigate how people make sense of Health Code ( jiankangma), the Chinese contact tracing and risk assessment algorithmic sociotechnical assemblage. We probe how people accept or resist Health Code by examining their ongoing, dynamic, and relational interactions with it. Participants display a rich variety of attitudes toward privacy and surveillance, ranging from fatalism to the possibility of privacy to trade-offs for surveillance in exchange for public health, which is mediated by the perceived effectiveness of Health Code and changing views on the intentions of institutions who deploy it. We show how perceived competency varies not just on how well the technology works, but on the social and cultural enforcement of various non-technical aspects like quarantine, citizen data inputs, and cell reception. Furthermore, we illustrate how perceptions of Health Code are nested in people’s broader interpretations of disease control at the national and global level, and unexpectedly strengthen the Chinese authority’s legitimacy. None of the Chinese public, Health Code, or people’s perceptions toward Health Code are predetermined, fixed, or categorically consistent, but are co-constitutive and dynamic over time. We conclude with a theorization of a relational perception and methodological reflections to study algorithmic sociotechnical assemblages beyond COVID-19.

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Section: Introductionmentioning

confidence: 99%

Making sense of algorithms: Relational perception of contact tracing and risk assessment during COVID-19

Liu

Graham

2021

Big Data & Society

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“…However, these increasing effects and the complexity of digital inequality are often overlooked as digital infrastructural coverage continues to expand and ownership of smart devices continues to increase. One way to address this problem is to make the ignored legible in the state’s algorithmic eyes, similar to the inclusion movements for financial credit records and census projects ( Rodríguez-Muñiz 2017 ; Rona-Tas 2020 ). However, we also need to think beyond the state’s vision and consider citizens beyond their status subjects.…”

Section: Discussionmentioning

confidence: 99%

“…Algorithms seem to fit the state’s goal and approach of increasing legibility, not only due to their ability to scale up but also due to their tendency to centralize data collection and decision-making ( Rona-Tas 2020 ; Zuboff 2019 ). This confluent perception of states and algorithms is particularly common in the authoritarian context, where, after a brief period when the Internet seemed to shake the sociopolitical orders, states have quickly and adaptively equipped themselves with ICTs and algorithms that further advance their vision and control of society ( Liu 2019 ; Michaelsen and Glasius 2018 ).…”

Section: State Legibility Through Algorithmsmentioning

confidence: 99%

“…Furthermore, increasingly, state legibility requirements are connected with citizenship and, in many situations, made a prerequisite for people to receive services from the state. As a result, being seen by the state is a status that people, particularly marginalized social groups, aim to achieve rather than to avoid, in contrast to traditional state-centered analysis ( Fourcade 2021 ; Rodríguez-Muñiz 2017 ; Rona-Tas 2020 ). This further promotes the value of a theoretical perspective that includes heterogeneous social actors in the construction and operation of state legibility projects beyond “the state,” as well as the dynamic interactions among these social actors beyond the “see/control–resistance” framework.…”

Section: State Legibility Through Algorithmsmentioning

confidence: 99%

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Seeing Like a State, Enacting Like an Algorithm: (Re)assembling Contact Tracing and Risk Assessment during the COVID-19 Pandemic

Liu

2021

Science, Technology, & Human Values

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As states increasingly use algorithms to improve the legibility of society, particularly during the COVID-19 pandemic, it is common for concerns about the expanding power of the algorithm or the state to be raised in a deterministic manner. However, how are the algorithms for states’ legibility projects enacted, contested, and reconfigured? Drawing on interviews and media data, this study fills this gap by examining Health Code ( jiankangma), the Chinese contact tracing and risk assessment algorithmic system that serves as the COVID-19 health passport. I first explore the intensive and invisible work and infrastructures that enact and stabilize Health Code’s sociotechnical assemblage. I then show how this assemblage is frequently challenged and destabilized by errors, breakdowns, and exclusions. Facing unintended engagements from heterogeneous social actors, local interests, and power hierarchies, Health Code reassembles into multiple and contradictory assemblages at different periods and social localities. Finally, I examine how people game and bypass the algorithm’s surveillance with their agencies. Recognizing this messiness and heterogeneity contributes to a more nuanced and realistic understanding of states’ use of algorithms, including the risks. Doing so also urges us to rethink the politics of citizenship and inequality in the digital age beyond inclusion.

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“…11 11 Such technical problems can quickly become societal problems, as when such incorrect or racially biasedmetadata tags are used to retrieve personal credit information data in AI‐assisted loan processing systems (Rona‐Tas, 2019). …”

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confidence: 99%

Upgrading strategies for the digital economy

Sturgeon¹

2019

Global Strategy Journal

295

129

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Research Summary Although computerization has been enabling changes in the structure and economic geography of industries for decades, recent public discourse has become focused on a set of “new” advanced digital technologies and technology applications that appear poised to dramatically reduce demand for routine tasks and transform the organization and content of work. How are these changes shaping the strategic options for companies and policy‐makers in less‐developed economies? This paper disentangles the old and new features of the digital economy; distills three key business strategies underpinning its organization: modularity, open innovation, and platforms; and summarizes some of the benefits and risks for society. It explores the strategy and policy options available for firms and policy‐makers in less developed places, with a focus on innovation and market positioning. Managerial Summary How is the digital economy shaping strategic options for managers in less‐developed economies? This paper disentangles the old and new features of the digital economy and distills three key business strategies underpinning its organization: modularity, open innovation, and platforms. It then explores strategic options available for firms in less developed places, with a focus on innovation and market positioning. While core platform owners will have the capability and authority to accumulate, access, and analyze large pools of data, access to all of the world's relevant data is not required to speed innovation or carve out new market space in the digital economy. The opportunities may be greatest in industrial applications, where the continued importance of physical systems and industry‐specific domain knowledge drives adaptation and customization.

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Predicting the Future: Art and Algorithms

Cited by 19 publications

References 47 publications

Making sense of algorithms: Relational perception of contact tracing and risk assessment during COVID-19

Making sense of algorithms: Relational perception of contact tracing and risk assessment during COVID-19

Seeing Like a State, Enacting Like an Algorithm: (Re)assembling Contact Tracing and Risk Assessment during the COVID-19 Pandemic

Upgrading strategies for the digital economy

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