University of Hertfordshire

By the same authors

Larger, anonymous and machine-driven collectives

Research output: Contribution to conferenceAbstractpeer-review

Standard

Larger, anonymous and machine-driven collectives. / Carta, Silvio.

2019. Abstract from 16th Annual International Conference of the Architectural Humanities Research Association, Dundee, United Kingdom.

Research output: Contribution to conferenceAbstractpeer-review

Harvard

Carta, S 2019, 'Larger, anonymous and machine-driven collectives', 16th Annual International Conference of the Architectural Humanities Research Association, Dundee, United Kingdom, 21/11/19 - 23/11/19.

APA

Carta, S. (Accepted/In press). Larger, anonymous and machine-driven collectives. Abstract from 16th Annual International Conference of the Architectural Humanities Research Association, Dundee, United Kingdom.

Vancouver

Carta S. Larger, anonymous and machine-driven collectives. 2019. Abstract from 16th Annual International Conference of the Architectural Humanities Research Association, Dundee, United Kingdom.

Author

Carta, Silvio. / Larger, anonymous and machine-driven collectives. Abstract from 16th Annual International Conference of the Architectural Humanities Research Association, Dundee, United Kingdom.

Bibtex

@conference{d33fbfe123d64ea7840fa24bc985adc6,
title = "Larger, anonymous and machine-driven collectives",
abstract = "This paper presents an insight into how collectives are built around technology, and more specifically, around the use of online apps and social networks. With this study, I claim that large collectives emerge as the accidental result of individuals{\textquoteright} quest for self-affirmation and social recognition amongst a usually limited group of contacts. The individual user of social media apps shares his/her daily activities and achievements with a small community of followers in order to receive their appreciation in the form of comments, likes and shares. Whilst the user and his/her community operate under the presumption that such data are limited and restricted to boundaries of their own network, the software that runs the entire digital system harvests and manipulates their data, aggregating them with other networks to generate trends and improve the system on offer. Such machine learning systems operate as overarching agencies working across communities, contacts and individuals, constructing larger, interrelated and anonymous collectives that are not visible or accessible to the individuals who form them. The software is the only entity with utter control of such collectives. Through a number of case studies, this work explains how such systems work and operate silently in the background of our daily activities, and provides an account of how the private and the public life of individuals are becoming increasingly mediated by software without their full awareness. This paper presents an analysis of some the algorithmic logics that run the transition between private and public lives of individuals and construct large software-driven collectives. This contribution aims at exposing and commenting some the mechanisms that underpin the silent making of the public life as controlled by machines. ",
author = "Silvio Carta",
year = "2019",
language = "English",
note = "16th Annual International Conference of the Architectural Humanities Research Association : Architecture & Collective Life, ahra2019 ; Conference date: 21-11-2019 Through 23-11-2019",
url = "https://ahra2019.com/",

}

RIS

TY - CONF

T1 - Larger, anonymous and machine-driven collectives

AU - Carta, Silvio

PY - 2019

Y1 - 2019

N2 - This paper presents an insight into how collectives are built around technology, and more specifically, around the use of online apps and social networks. With this study, I claim that large collectives emerge as the accidental result of individuals’ quest for self-affirmation and social recognition amongst a usually limited group of contacts. The individual user of social media apps shares his/her daily activities and achievements with a small community of followers in order to receive their appreciation in the form of comments, likes and shares. Whilst the user and his/her community operate under the presumption that such data are limited and restricted to boundaries of their own network, the software that runs the entire digital system harvests and manipulates their data, aggregating them with other networks to generate trends and improve the system on offer. Such machine learning systems operate as overarching agencies working across communities, contacts and individuals, constructing larger, interrelated and anonymous collectives that are not visible or accessible to the individuals who form them. The software is the only entity with utter control of such collectives. Through a number of case studies, this work explains how such systems work and operate silently in the background of our daily activities, and provides an account of how the private and the public life of individuals are becoming increasingly mediated by software without their full awareness. This paper presents an analysis of some the algorithmic logics that run the transition between private and public lives of individuals and construct large software-driven collectives. This contribution aims at exposing and commenting some the mechanisms that underpin the silent making of the public life as controlled by machines.

AB - This paper presents an insight into how collectives are built around technology, and more specifically, around the use of online apps and social networks. With this study, I claim that large collectives emerge as the accidental result of individuals’ quest for self-affirmation and social recognition amongst a usually limited group of contacts. The individual user of social media apps shares his/her daily activities and achievements with a small community of followers in order to receive their appreciation in the form of comments, likes and shares. Whilst the user and his/her community operate under the presumption that such data are limited and restricted to boundaries of their own network, the software that runs the entire digital system harvests and manipulates their data, aggregating them with other networks to generate trends and improve the system on offer. Such machine learning systems operate as overarching agencies working across communities, contacts and individuals, constructing larger, interrelated and anonymous collectives that are not visible or accessible to the individuals who form them. The software is the only entity with utter control of such collectives. Through a number of case studies, this work explains how such systems work and operate silently in the background of our daily activities, and provides an account of how the private and the public life of individuals are becoming increasingly mediated by software without their full awareness. This paper presents an analysis of some the algorithmic logics that run the transition between private and public lives of individuals and construct large software-driven collectives. This contribution aims at exposing and commenting some the mechanisms that underpin the silent making of the public life as controlled by machines.

M3 - Abstract

T2 - 16th Annual International Conference of the Architectural Humanities Research Association

Y2 - 21 November 2019 through 23 November 2019

ER -