Perspectives on algorithmic normativities: engineers, objects, activities

Grosman, Jérémy; Reigeluth, Tyler

doi:10.1177/2053951719858742

Cited by 31 publications

(34 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Essentially, those who disagreed with the initial suggestion of countering hate speech perceived the implemented system as representative of undemocratic values. The critics, thus, acknowledged quite forthrightly the potential normativities and power structures embedded in algorithms (Ziewitz, 2016;Grosman and Reigeluth, 2019). The responses are indicative of an interesting dichotomy spanning the whole range of the project: while technology was used as a tool to deal with online emotions, simultaneously the responses tapped into the same emotions as a means to deal with the technological unknown, the mysterious algorithm conducting the monitoring or surveillance process.…”

Section: Discussionmentioning

confidence: 99%

The Datafication of Hate: Expectations and Challenges in Automated Hate Speech Monitoring

Laaksonen

Haapoja

Kinnunen³

et al. 2020

Front. Big Data

View full text Add to dashboard Cite

Hate speech has been identified as a pressing problem in society and several automated approaches have been designed to detect and prevent it. This paper reports and reflects upon an action research setting consisting of multi-organizational collaboration conducted during Finnish municipal elections in 2017, wherein a technical infrastructure was designed to automatically monitor candidates' social media updates for hate speech. The setting allowed us to engage in a 2-fold investigation. First, the collaboration offered a unique view for exploring how hate speech emerges as a technical problem. The project developed an adequately well-working algorithmic solution using supervised machine learning. We tested the performance of various feature extraction and machine learning methods and ended up using a combination of Bag-of-Words feature extraction with Support-Vector Machines. However, an automated approach required heavy simplification, such as using rudimentary scales for classifying hate speech and a reliance on word-based approaches, while in reality hate speech is a linguistic and social phenomenon with various tones and forms. Second, the action-research-oriented setting allowed us to observe affective responses, such as the hopes, dreams, and fears related to machine learning technology. Based on participatory observations, project artifacts and documents, interviews with project participants, and online reactions to the detection project, we identified participants' aspirations for effective automation as well as the level of neutrality and objectivity introduced by an algorithmic system. However, the participants expressed more critical views toward the system after the monitoring process. Our findings highlight how the powerful expectations related to technology can easily end up dominating a project dealing with a contested, topical social issue. We conclude by discussing the problematic aspects of datafying hate and suggesting some practical implications for hate speech recognition.

show abstract

Section: Discussionmentioning

confidence: 99%

The Datafication of Hate: Expectations and Challenges in Automated Hate Speech Monitoring

Laaksonen

Haapoja

Kinnunen³

et al. 2020

Front. Big Data

View full text Add to dashboard Cite

show abstract

“…Here is another tension regarding the person produced by algorithmic prediction, and the necessity for the actors to interpret and contextualize the results of algorithms, to establish their value. It is linked to a structural limitation of predictive scoring, which necessarily depends on the data available to train the algorithm and the choices made by data scientists in defining its desired outcomes (Grosman and Reigeluth, 2019). By measuring the likelihood to subscribe by comparison with actual policyholders, the algorithm may have assigned the highest scores to people who resembled them too strongly for them not to have a policy elsewhere.…”

Section: The Perfect Client: When the Algorithm Predicts Too Wellmentioning

confidence: 99%

Mass personalization: Predictive marketing algorithms and the reshaping of consumer knowledge

Kotras

2020

Big Data & Society

View full text Add to dashboard Cite

This paper focuses on the conception and use of machine-learning algorithms for marketing. In the last years, specialized service providers as well as in-house data scientists have been increasingly using machine learning to predict consumer behavior for large companies. Predictive marketing thus revives the old dream of one-to-one, perfectly adjusted selling techniques, now at an unprecedented scale. How do predictive marketing devices change the way corporations know and model their customers? Drawing from STS and the sociology of quantification, I propose to study the original ambivalence that characterizes the promise of a mass personalization, i.e. algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets. By studying algorithms in practice, I show how the active embedding of local preexisting consumer knowledge and punctual de-personalization mechanisms are keys to the epistemic and organizational success of predictive marketing. This paper argues for the study of algorithms in their contexts and suggests new perspectives on algorithmic objectivity.

show abstract

“…The materiality of the machine is a text to be deciphered. The machine is an artifact, and therefore it is the embodiment of a set of different types of normativities (imaginative, technical, social, socio-technical, and behavioral), as Grosman and Reigeluth (2019) show. It is interesting that these normativities can compete, collaborate or coexist without interacting inside the machine.…”

Section: The Difficulty Of Being An Aimentioning

confidence: 99%

Algorithmic unconscious: why psychoanalysis helps in understanding AI

Possati

2020

Palgrave Commun

View full text Add to dashboard Cite

The central hypothesis of this paper is that the concepts and methods of psychoanalysis can be applied to the study of AI and human/AI interaction. The paper connects three research fields: machine behavior approach, psychoanalysis and anthropology of science. In the "Machine behavior: research perspectives" section, I argue that the behavior of AI systems cannot be studied only in a logical-mathematical or engineering perspective. We need to study AI systems not merely as engineering artifacts, but as a class of social actors with particular behavioral patterns and ecology. Hence, AI behavior cannot be fully understood without human and social sciences. In the "Why an unconscious for AI? What this paper is about" section, I give some clarifications about the aims of the paper. In the "Unconscious and technology. Lacan and Latour" section, I introduce the central thesis. I propose a re-interpretation of Lacan's psychoanalysis through Latour's anthropology of sciences. The aim of this re-interpretation is to show that the concept of unconscious is not so far from technique and technology. In the "The difficulty of being an AI" section, I argue that AI is a new stage in the human identification process, namely, a new development of the unconscious identification. After the imaginary and symbolic registers, AI is the third register of identification. Therefore, AI extends the movement that is at work in the Lacanian interpretation of the mirror stage and Oedipus complex and which Latour's reading helps us to clarify. From this point of view, I describe an AI system as a set of three contrasting forces: the human desire for identification, logic and machinery. In the "Miscomputation and information" section, I show how this interpretative model improves our understanding of AI.

show abstract

Perspectives on algorithmic normativities: engineers, objects, activities

Abstract: This contribution aims at proposing a framework for articulating different kinds of '

Cited by 31 publications

References 32 publications

The Datafication of Hate: Expectations and Challenges in Automated Hate Speech Monitoring

The Datafication of Hate: Expectations and Challenges in Automated Hate Speech Monitoring

Mass personalization: Predictive marketing algorithms and the reshaping of consumer knowledge

Algorithmic unconscious: why psychoanalysis helps in understanding AI

Contact Info

Product

Resources

About