The Democratization of Artificial Intelligence

Sudmann, Andreas

doi:10.1515/9783839447192-001

Cited by 8 publications

(8 citation statements)

References 39 publications

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“…The hyperplane for separating outcomes can be written as the set of points X : where “∙” denotes the dot product and the vector W is a normal vector. In two-category classification, given a training set of instance-label pairs ( x i , y i ), the SVM model needs to solve the optimization problem 29 : where ξ are slack variables measuring the misclassification errors, and C is the penalty factor to errors introducing additional capacity control within the classifier. The constraint along with the objective of minimizing function can be solved using Lagrange multipliers.…”

Section: Methodsmentioning

confidence: 99%

“…The authenticity, integrality and reliability of the road test environment significantly influence the performance of the intelligent and connected vehicle 27 . Comparing the implementation cost of real road tests, most tests adopt schemes combining software simulation, virtual-real interaction 28 and advanced learning algorithms 29 . The environment includes roads, roadblocks, trees, houses, humans, other vehicles, motorcycles, bicycles, etc.…”

Section: Introductionmentioning

confidence: 99%

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Using millimeter-wave radar to evaluate the performance of dummy models for advanced driving assistance systems test

Lv,

Yuan,

Bai

2024

Sci Rep

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With the rapid development of intelligent and connected vehicles, the experimental road test for the advanced driving assistance system (ADAS) is dramatically increasing around the world. Considering its high cost and hazardous situations, simulation test based on a dummy model is becoming a promising way for ADAS road test practice to reduce the experiment expanses. This study proposed a methodology for the evaluation of the performance of human and dummies with distinct designed materials based on the data extracted from the Doppler effect of millimeter-wave radar. Echo data of 8 different angles from 0 to 360 degrees, with the an interval of 45 degrees, at the same distance between the test object and the signal source is collected. Meanwhile, the echo energy is collected for correlation modeling and analysis among groups. By evaluating the performance of humans and dummies via statistical analysis, a close correlation was found which results verified the substitutability of the dummy for the ADAS experiment test. The correlation coefficient between human and dummies ranges from 0.75 to 0.93. The support vector machine (SVM) model was developed and fitted to predict the echo energy in diverse environments. The mean average error (MAE) is 5.42–11.42 in the training and testing datasets while root mean square error (RMSE) is 0.43–0.90. The methods developed in the study can simulate the real ADAS road test environment and support future experimental research.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using millimeter-wave radar to evaluate the performance of dummy models for advanced driving assistance systems test

Lv,

Yuan,

Bai

2024

Sci Rep

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“…The ideal of "democratizing" AI systems, and the resulting AI innovation policy, is another effort premised on the assumed functionality of AI. This is the argument that access to AI tooling and AI skills should be expanded [14,70,83,181]-with the corollary claim that it is problematic that only certain institutions, nations, or individuals have access to the ability to build these systems [8]. A recent example of democratization efforts was the global push for the relaxation of oversight in data sharing in order to allow for more innovation in AI tool development in the wake of the COVID-19 pandemic [11,49,122,137,196].…”

Section: The Functionality Assumptionmentioning

confidence: 99%

The Fallacy of AI Functionality

Raji

Kumar

Horowitz

et al. 2022

2022 ACM Conference on Fairness, Accountability, and Transparency

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Deployed AI systems often do not work. They can be constructed haphazardly, deployed indiscriminately, and promoted deceptively. However, despite this reality, scholars, the press, and policymakers pay too little attention to functionality. This leads to technical and policy solutions focused on "ethical" or value-aligned deployments, often skipping over the prior question of whether a given system functions, or provides any benefits at all. To describe the harms of various types of functionality failures, we analyze a set of case studies to create a taxonomy of known AI functionality issues. We then point to policy and organizational responses that are often overlooked and become more readily available once functionality is drawn into focus. We argue that functionality is a meaningful AI policy challenge, operating as a necessary first step towards protecting affected communities from algorithmic harm.

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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%

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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The Democratization of Artificial Intelligence

Cited by 8 publications

References 39 publications

Using millimeter-wave radar to evaluate the performance of dummy models for advanced driving assistance systems test

Using millimeter-wave radar to evaluate the performance of dummy models for advanced driving assistance systems test

The Fallacy of AI Functionality

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

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