Excavating AI: the politics of images in machine learning training sets

Crawford, Kate; Paglen, Trevor

doi:10.1007/s00146-021-01162-8

Cited by 143 publications

(167 citation statements)

References 15 publications

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“…From Woodrow Wilson Bledsoe's first tinkering in the 1960s 22 to the contemporary pervasiveness of facial detection systems in our daily life, both in the private and the public sphere, passing through the interaction with mobile phones through the border control services, all show the pervasiveness of this phenomenon. During the last twenty years, a considerable number of scholars and artists have focused on the opportunities and limits offered by artificial recognition technologies: the research of Kate Crawford and Trevor Paglen (2021), Kelly Gates (2011), Shoshana Amielle Magnet (2011), among others, together with the works of artists like Hito Steyler, Leonardo Selvaggio and Zach Blas show the relevance of these phenomena.…”

Section: Machine Face Detection and The Future Of Face Recognitionmentioning

confidence: 99%

Notes on the semiotics of face recognition

Gramigna

Voto

2021

SSS

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Perceiving and recognizing others via their faces is of pivotal importance. The ability to perceive others in the environment – to discern between friends and foes, selves and others – as well as to detect and seek to predict their possible moves, plans, and intentions, is a set of skills that has proved to be essential in the evolutionary history of humankind. The aim of this study is to explore the subject of face recognition as a semiotic phenomenon. The scope of this inquiry is limited to face perception by the human species. The human face is analysed on the threshold between biological processes and cultural processes. We argue that the recognition of likenesses has a socio-cultural dimension that should not be overlooked. By drawing on Georg Lichtenberg’s remarks on physiognomy, we discuss the critique of the semiotic bias, the association of ideas, and the mechanism of typification involved in face recognition. Face typification is discussed against the background of face recognition and face identification. We take them as three gradients of meaning that map out a network of relationships concerning different cognitive operations that are at stake when dealing with the recognition of faces.

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Section: Machine Face Detection and The Future Of Face Recognitionmentioning

confidence: 99%

Notes on the semiotics of face recognition

Gramigna

Voto

2021

SSS

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“…Research subjects were then presented with a variety of privacy options in which they traded off which of the two collected kinds of information might be shared to various audience sizes. 4 [12] thus developed multiple data privacy trade-off points for an individual and so could assess whether there was a consistent pattern of tradeoff decisions. [12] found that a majority (63 %) of participants did evince consistent privacy preferences when trading off various privacy scenarios against each other.…”

Section: Related Workmentioning

confidence: 99%

“…Tech companies and other entities who collect data and build machine learning models have likewise routinely violated their own privacy policies [6]. Perceived and actual transgressions of privacy norms have also occurred in the case of data collection for academic research [4], [18]. The resulting widespread discontent has led to consistent calls for legal reform [14], as much behavior found offensive by ordinary people is nonetheless entirely legal.…”

Section: Introductionmentioning

confidence: 99%

Measuring Lay Reactions to Personal Data Markets

Nielsen

2021

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

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The recording, aggregation, and exchange of personal data is necessary to the development of socially-relevant machine learning applications. However, anecdotal and survey evidence show that ordinary people feel discontent and even anger regarding data collection practices that are currently typical and legal. This suggests that personal data markets in their current form do not adhere to the norms applied by ordinary people. The present study experimentally probes whether market transactions in a typical online scenario are accepted when evaluated by lay people. The results show that a high percentage of study participants refused to participate in a data pricing exercise, even in a commercial context where market rules would typically be expected to apply. For those participants who did price the data, the median price was an order of magnitude higher than the market price. These results call into question the notice and consent market paradigm that is used by technology firms and government regulators when evaluating data flows. The results also point to a conceptual mismatch between cultural and legal expectations regarding the use of personal data. CCS CONCEPTS• Security and privacy → Economics of security and privacy; Social aspects of security and privacy; Information accountability and usage control; • Human-centered computing → Laboratory experiments.

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“…The growing number of applications of sentiment-aware systems has led the NLP community in the past decade to develop end-to-end models to examine short- and medium-length text documents (Wilson et al, 2005 ; Feldman, 2013 ), particularly for social media (Pak and Paroubek, 2010 ; Agarwal et al, 2011 ; Korkontzelos et al, 2016 ). Some researchers have considered the many social and political implications of using AI for sentiment detection across media (Crawford, 2019 ; Crawford and Paglen, 2021 ). Recent studies highlight some of the implicit hazards of crowdsourcing text data (Shmueli et al, 2021 ), especially in light of the latest advances in NLP and emerging ethical concerns (Conway and O'Connor, 2016 ; Hovy and Spruit, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning

Alshaabi

Oort

Fudolig

et al. 2022

Front. Artif. Intell.

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Sentiment-aware intelligent systems are essential to a wide array of applications. These systems are driven by language models which broadly fall into two paradigms: Lexicon-based and contextual. Although recent contextual models are increasingly dominant, we still see demand for lexicon-based models because of their interpretability and ease of use. For example, lexicon-based models allow researchers to readily determine which words and phrases contribute most to a change in measured sentiment. A challenge for any lexicon-based approach is that the lexicon needs to be routinely expanded with new words and expressions. Here, we propose two models for automatic lexicon expansion. Our first model establishes a baseline employing a simple and shallow neural network initialized with pre-trained word embeddings using a non-contextual approach. Our second model improves upon our baseline, featuring a deep Transformer-based network that brings to bear word definitions to estimate their lexical polarity. Our evaluation shows that both models are able to score new words with a similar accuracy to reviewers from Amazon Mechanical Turk, but at a fraction of the cost.

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Excavating AI: the politics of images in machine learning training sets

Cited by 143 publications

References 15 publications

Notes on the semiotics of face recognition

Notes on the semiotics of face recognition

Measuring Lay Reactions to Personal Data Markets

Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning

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