The seer and the seen: Surveying Palantir’s surveillance platform

Iliadis, Andrew; Acker, Amelia

doi:10.1080/01972243.2022.2100851

Cited by 17 publications

(4 citation statements)

References 85 publications

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“…to the patent filings of Palantir Technologies, a notable actor in the surveillance industry (Iliadis & Acker, 2022), as well as more generally in attempts to classify patents automatically and to determine the growth rates of particular classes of patents (Kim & Kim, 2022;Yun & Geum, 2020;Suominen et al, 2017). Our work differs from these in that it focuses on changepoints in patenting rates, as well as the emergence of new terms and topics, but has similarities in its use of topic modelling to assess and group patents via automatic means.…”

Section: Methodsmentioning

confidence: 99%

Identifying potential emerging human rights implications in Chinese smart cities via machine-learning aided patent analysis

Wright¹,

Weber²,

Walton³

2023

Internet Policy Review

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Section: Methodsmentioning

confidence: 99%

Identifying potential emerging human rights implications in Chinese smart cities via machine-learning aided patent analysis

Wright¹,

Weber²,

Walton³

2023

Internet Policy Review

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“…Such structures may do this by developing labeling or classification schemes that divide the world into categories that are then offered as an enormous, open store of data that others can query for various purposes, such as retrieving facts and sharing information. Recently, several scholars have elaborated on the political nature of such infrastructural processes of digitization and datafication, including in the domains of archiving and preservation (Thylstrup, 2019, 2022), governance and management (Flyverbom & Murray, 2018), metrics and sorting (Alaimo & Kallinikos, 2021), and the creation of global ontologies for things like web search (Iliadis et al, 2023) and surveillance services (Iliadis & Acker, 2022).…”

Section: Wikidata As Semantic Infrastructurementioning

confidence: 99%

“…Drawing from work on infrastructure and platform studies (Helmond, 2015;Plantin et al, 2018), as well as from research on semantic platformization (Iliadis, 2022;Iliadis & Acker, 2022;Iliadis et al, 2023), we articulate Wikidata as a key actant in the remaking of social and political relations in the context of datafication and artificial intelligence (AI) across the broader web in media products like search engines and virtual assistants. While Wikidata is studied in fields, such as information and computer science (Erxleben et al, 2014;Pellissier Tanon et al, 2016), up to now, Wikidata and its politics are studied by only a few media and communications scholars relative to its central role in search (Ford, 2020(Ford, , 2022Iliadis, 2022;McDowell & Vetter, 2022, forthcoming), though some studies that reference Wikidata are beginning to appear in this domain, such as those seeking to analyze gender inequality in Wikipedia (Konieczny & Klein, 2018;Tripodi, 2023).…”

Section: Introductionmentioning

confidence: 99%

Wikidata as Semantic Infrastructure: Knowledge Representation, Data Labor, and Truth in a More-Than-Technical Project

Ford,

Iliadis

2023

Social Media + Society

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Wikidata is a knowledge base (i.e., a database of facts) project of the Wikimedia Foundation and a sister project to Wikipedia that contains billions of facts that anyone can edit. Platform companies, such as Amazon, Google, and Microsoft use Wikidata to retrieve and transmit facts across the internet, while researchers rely on Wikidata as a data source for facts. Various Wikipedia researchers have commended Wikidata for its collaborative nature and liberatory potential, yet less attention has been paid to the social and political implications of Wikidata. This article aims to advance work in this context by introducing the concept of semantic infrastructure and outlining how Wikidata’s role as semantic infrastructure is the primary vehicle by which Wikipedia has become infrastructural for digital platforms. We develop two key themes that build on questions of power that arise in infrastructure studies and apply to Wikidata: knowledge representation and data labor. We argue that considering these issues about Wikidata helps contextualize such infrastructural technologies within existing media and communication studies debates and highlights the contingencies upon which their outcomes depend.

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“…While many other wellknown companies log patents for emotion recognition applications, Amazon and Spotify were selected due to their scale of operation and interest in new forms of recommender systems. Patent analysis is a useful tool for understanding the ambition of secretive corporations engaged in surveillant capitalism (Iliadis and Acker 2022;McStay 2018). Patent filings afford critical scholars insight into the hopes, technical intentions, and worldviews of companies and owners, potentially unachievable by other means.…”

Section: Analysing Patentsmentioning

confidence: 99%

Human-First, Please: Assessing Citizen Views and Industrial Ambition for Emotional AI in Recommender Systems

Bakir

Laffer

McStay

2023

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This paper qualitatively explores the views of diverse members of the British public on applications of biometric emotional AI technologies patented by two globally dominant consumer-facing recommender systems, Amazon and Spotify. Examining Amazon and Spotify patents for biometric profiling of users’ emotions, disposition, and behaviour to offer them tailored services, ads, and products from their wider platforms, this paper points to industrial ambition regarding emotional AI. Little is known about ordinary people’s views on deployment of such technology, and given the complex, abstract, and future-facing nature of such technologies, ascertaining informed lay views is hard. We address this through our innovative, qualitative study of diverse British-based adults (n=46) that presents to them near-horizon use cases in an interactive fictional narrative that deploys design fiction principles and ContraVision techniques. We find the themes of “usefulness,” “resignation,” “uneasy terms of engagement,” and “human-first,” adding rich and nuanced insights to prior survey work on users’ views towards biometric-based emotional AI technologies. In contributing to a richer understanding of whether emotional AI technologies should be deployed in consumer-facing recommender systems, and if so, on what terms, we find that well-established policy-friendly criticisms apply to global emotional AI recommender systems. We conclude, however, that problems of alienation and need for a human-first approach to emerging AI technology are the most significant criticisms.

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The seer and the seen: Surveying Palantir’s surveillance platform

Cited by 17 publications

References 85 publications

Identifying potential emerging human rights implications in Chinese smart cities via machine-learning aided patent analysis

Identifying potential emerging human rights implications in Chinese smart cities via machine-learning aided patent analysis

Wikidata as Semantic Infrastructure: Knowledge Representation, Data Labor, and Truth in a More-Than-Technical Project

Human-First, Please: Assessing Citizen Views and Industrial Ambition for Emotional AI in Recommender Systems

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