“Data Strikes”: Evaluating the Effectiveness of a New Form of Collective Action Against Technology Companies

Vincent, Nicholas; Hecht, Brent; Sen, Shilad

doi:10.1145/3308558.3313742

Cited by 23 publications

(26 citation statements)

References 30 publications

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“…As personal data are increasingly commodified by technology companies and harvested at scale to improve proprietary machine learning systems, often in ways that are by turns inscrutable or distasteful to the general public, 133 recent proposals call for not only re-framing data as labor, 134 but also for ''data strikes'' in which users collectively withhold their data as a means to shift the power imbalance back toward subjects who are not compensated for the ambient collection of their data. 135…”

Section: Labormentioning

confidence: 99%

Data and its (dis)contents: A survey of dataset development and use in machine learning research

et al. 2021

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Section: Labormentioning

confidence: 99%

Data and its (dis)contents: A survey of dataset development and use in machine learning research

et al. 2021

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“…Chow et al [5] propose techniques to estimate the contributions of different data points to the overall recommendation quality. Vincent et al [42] propose 'data strikes' as a form of collective action where users protest by withholding their data from recommendation provider. Note that, while the goal of data strikes is to limit availability of data to reduce recommendation performance, the goal of performance-based data minimization it to limit availability of data while preserving recommendation performance.…”

Section: Related Workmentioning

confidence: 99%

Operationalizing the Legal Principle of Data Minimization for Personalization

Biega

Potash

Daumé

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Article 5(1)(c) of the European Union's General Data Protection Regulation (GDPR) requires that "personal data shall be [...] adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed ('data minimisation')". To date, the legal and computational definitions of 'purpose limitation' and 'data minimization' remain largely unclear. In particular, the interpretation of these principles is an open issue for information access systems that optimize for user experience through personalization and do not strictly require personal data collection for the delivery of basic service.In this paper, we identify a lack of a homogeneous interpretation of the data minimization principle and explore two operational definitions applicable in the context of personalization. The focus of our empirical study in the domain of recommender systems is on providing foundational insights about the (i) feasibility of different data minimization definitions, (ii) robustness of different recommendation algorithms to minimization, and (iii) performance of different minimization strategies.We find that the performance decrease incurred by data minimization might not be substantial, but that it might disparately impact different users-a finding which has implications for the viability of different formal minimization definitions. Overall, our analysis uncovers the complexities of the data minimization problem in the context of personalization and maps the remaining computational and regulatory challenges.

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“…P could try to prevent F from collecting their online images by withholding them. Recent works propose the concept of "data leverage" where users of online platforms work collectively to withhold data or control how their data is used by tech companies [100], [32], [121]. While not specifically aimed at facial recognition, these proposals offer alternative models for online engagement while protecting user data.…”

Section: Data Leverage By Usersmentioning

confidence: 99%

SoK: Anti-Facial Recognition Technology

Wenger¹,

Shan²,

Zheng³

et al. 2021

Preprint

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The rapid adoption of facial recognition (FR) technology by both government and commercial entities in recent years has raised concerns about civil liberties and privacy. In response, a broad suite of so-called "anti-facial recognition" (AFR) tools has been developed to help users avoid unwanted facial recognition. The set of AFR tools proposed in the last few years is wide-ranging and rapidly evolving, necessitating a step back to consider the broader design space of AFR systems and long-term challenges. This paper aims to fill that gap and provides the first comprehensive analysis of the AFR research landscape. Using the operational stages of FR systems as a starting point, we create a systematic framework for analyzing the benefits and tradeoffs of different AFR approaches. We then consider both technical and social challenges facing AFR tools and propose directions for future research in this field.

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“Data Strikes”: Evaluating the Effectiveness of a New Form of Collective Action Against Technology Companies

Cited by 23 publications

References 30 publications

Data and its (dis)contents: A survey of dataset development and use in machine learning research

Data and its (dis)contents: A survey of dataset development and use in machine learning research

Operationalizing the Legal Principle of Data Minimization for Personalization

SoK: Anti-Facial Recognition Technology

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