Adversariality in Machine Learning Systems: On Neural Networks and the Limits of Knowledge

Lepage-Richer, Théo

doi:10.1007/978-3-030-56286-1_7

Cited by 2 publications

(3 citation statements)

References 35 publications

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“…Rather, such scholars have argued that data-intensive technologies are implicated deeply in existing social relations, depend for their existence on a rich array of material factors, and cannot be considered in abstraction from the humans who design, deploy, and use them. More specifically, critical scholars have demonstrated how data-intensive technologies reinforce existing forms of discrimination (Katz, 2020;Noble, 2018) while undermining and complicating democratic processes (Sudmann, 2019), reconfiguring epistemologies (Kitchin, 2014;Lepage-Richer, 2021), and bolstering the pathologies of the capitalist mode of production, from ecological devastation to colonial predation of labor and resources (Chun, 2018;Crawford, 2021;Dyer-Witheford et al, 2019;Halpern, 2021;Verdegem, 2021). Others have pointed out how a self-reinforcing dynamic has developed between data and capital.…”

Section: Context: Data-intensive Capital and Surveillancementioning

confidence: 99%

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Toward a political economy of synthetic data: A data-intensive capitalism that is not a surveillance capitalism?

Steinhoff

2022

New Media & Society

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Surveillance of human subjects is how data-intensive companies obtain much of their data, yet surveillance increasingly meets with social and regulatory resistance. Data-intensive companies are thus seeking other ways to meet their data needs. This article explores one of these: the creation of synthetic data, or data produced artificially as an alternative to real-world data. I show that capital is already heavily invested in synthetic data. I argue that its appeal goes beyond circumventing surveillance to accord with a structural tendency within capitalism toward the autonomization of the circuit of capital. By severing data from human subjectivity, synthetic data contributes to the automation of the production of automation technologies like machine learning. A shift from surveillance to synthesis, I argue, has epistemological, ontological, and political economic consequences for a society increasingly structured around data-intensive capital.

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Section: Context: Data-intensive Capital and Surveillancementioning

confidence: 99%

“…. that are only marginally different from those that they adequately classify” (Lepage-Richer, 2021: 215). Over the course of such adversarial exchanges a model can be trained that can extract and reproduce the statistical properties (and labels) of interest of a real dataset in a new synthetic dataset.…”

Section: Generative Modelsmentioning

confidence: 99%

Toward a political economy of synthetic data: A data-intensive capitalism that is not a surveillance capitalism?

Steinhoff

2022

New Media & Society

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“…The first formal neuron, developed by McCulloch and Pitts, was a mathematical model of a biological neuron. A physiological approach to understanding neuron functioning reached this milestone in artificial intelligence (AI) [23].…”

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confidence: 99%

The Effects of Artificial Intelligence (AI) on Company Performance Including the Value of AI-based Business Transformation Initiatives

Domini,

Dewi,

Cesna

2023

itsdi

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The research paper discusses the impact of Artificial Intelligence (AI) on organizational and process-level performance. The study follows a four-step process: analyzing AI technologies, exploring case studies, collecting data, and reviewing AI literature. The findings indicate that AI has various technologies, such as machine translation, chatbots, and self-learning algorithms, that can enhance automation, information, and transformation effects. Organizations can use AI to improve processes, optimize operations, and develop strategic and competitive advantages. The study provides insights into how organizations can enhance the business value of their transformation projects by leveraging AI's attributes. The research framework proposes a more comprehensive approach to account for the intangible benefits of AI in organizations. The study highlights that AI combines several configurations of IT in various industries, and organizations should reconfigure their processes to achieve performance through AI capabilities. The research also provides tangible evidence about the business value of AI-based projects and their impact on firm performance, considering it not as a single technology but as a set/combination of several different configurations of IT in various industries. The study has scientific and managerial interests and proposes a model for analyzing the impact of AI on firm performance, providing managers with insights to improve their organizations' performance, profitability, and competitive advantage.

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Adversariality in Machine Learning Systems: On Neural Networks and the Limits of Knowledge

Cited by 2 publications

References 35 publications

Toward a political economy of synthetic data: A data-intensive capitalism that is not a surveillance capitalism?

Toward a political economy of synthetic data: A data-intensive capitalism that is not a surveillance capitalism?

The Effects of Artificial Intelligence (AI) on Company Performance Including the Value of AI-based Business Transformation Initiatives

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