The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms

Bucher, Taina

doi:10.1080/1369118x.2016.1154086

Cited by 811 publications

(462 citation statements)

References 20 publications

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“…Most people do not and cannot know everything about how a given technology functions. At some point, people use what they do know to extrapolate imaginaries, both about how technology does function and about how it might be able to function (Bucher, 2017). Such imaginaries-beliefs about what is easy, difficult, or even possible-may not align with current thinking, either technical or philosophical.…”

Section: Frame Reflection On Action: Reflextmentioning

confidence: 99%

Toward human-centered algorithm design

Baumer

2017

Big Data & Society

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As algorithms pervade numerous facets of daily life, they are incorporated into systems for increasingly diverse purposes. These systems' results are often interpreted differently by the designers who created them than by the lay persons who interact with them. This paper offers a proposal for human-centered algorithm design, which incorporates human and social interpretations into the design process for algorithmically based systems. It articulates three specific strategies for doing so: theoretical, participatory, and speculative. Drawing on the author's work designing and deploying multiple related systems, the paper provides a detailed example of using a theoretical approach. It also discusses findings pertinent to participatory and speculative design approaches. The paper addresses both strengths and challenges for each strategy in helping to center the process of designing algorithmically based systems around humans.

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Section: Frame Reflection On Action: Reflextmentioning

confidence: 99%

Toward human-centered algorithm design

Baumer

2017

Big Data & Society

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“…The idea of providing more choice and making services more responsive to users' wishes is not a contested idea, and it is also what many recommendation systems already do. Nevertheless, the interests of service providers and users are not always the samealgorithmic filtering is influenced by many other interests, including advertisers', so providers do not always register the 'real interests' or choices of consumers (e.g., Bucher, 2016).…”

Section: Individual Autonomy Perspectivementioning

confidence: 99%

Exposure diversity as a design principle for recommender systems

Helberger

Karppinen

D'Acunto

2016

Information, Communication & Society

247

143

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Exposure diversity as a design principle for recommender systemsHelberger, N.; Karpinnen, K.; D'Acunto, L. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. ABSTRACTPersonalized recommendations in search engines, social media and also in more traditional media increasingly raise concerns over potentially negative consequences for diversity and the quality of public discourse. The algorithmic filtering and adaption of online content to personal preferences and interests is often associated with a decrease in the diversity of information to which users are exposed. Notwithstanding the question of whether these claims are correct or not, this article discusses whether and how recommendations can also be designed to stimulate more diverse exposure to information and to break potential 'filter bubbles' rather than create them. Combining insights from democratic theory, computer science and law, the article makes suggestions for design principles and explores the potential and possible limits of 'diversity sensitive design'. IntroductionRecommendation systems increasingly influence our information choices: the information that is ultimately being presented to us has been filtered through the lens of our personal preferences, our previous choices and the preferences of our friends. There are also commercial and strategic decisions behind the algorithms that determine which information we will see, which information is prioritized and which information is excluded (Bozdag, 2013;Foster, 2012;Schulz, Dreyer, & Hagemeier, 2012;Webster, 2010). Search engines and social networks, in particular, increasingly rely on recommender systems, which are a class of information filtering systems that study patterns of user behaviour to determine what someone will prefer from among a collection of 'information'. By doing so, recommender systems essentially personalize the list of content that is offered to a user. With the emerging trends of higher interactivity and user orientation, the use of recommender systems is not limited to search engines and social networks, as media organizations are also increasingly incorporating recommenders into their own services.The impact of personalized recommendations on the realization of media and information diversity is currently a central questi...

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“…One example is online social networks wherein the value of the network comes from continued sharing and engagement. The design, operation, and continual changing of Facebook's algorithms, for example, are attempting to continually produce affective encounters, where the sentiment or mood is heightened to encourage further interaction and engagement (Bucher, 2017). For Dean, the underlying exchange taking place, wherein the companies behind these platforms and apps derive value from the data generated through use of their platform, is deliberately obscured by the affective dimensions users enjoy.…”

Section: Introductionmentioning

confidence: 99%

Intimate Surveillance: Normalizing Parental Monitoring and Mediation of Infants Online

Leaver

2017

Social Media + Society

100

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Parents are increasingly sharing information about infants online in various forms and capacities. To more meaningfully understand the way parents decide what to share about young people and the way those decisions are being shaped, this article focuses on two overlapping areas: parental monitoring of babies and infants through the example of wearable technologies and parental mediation through the example of the public sharing practices of celebrity and influencer parents. The article begins by contextualizing these parental practices within the literature on surveillance, with particular attention to online surveillance and the increasing importance of affect. It then gives a brief overview of work on pregnancy mediation, monitoring on social media, and via pregnancy apps, which is the obvious precursor to examining parental sharing and monitoring practices regarding babies and infants. The examples of parental monitoring and parental mediation will then build on the idea of "intimate surveillance" which entails close and seemingly invasive monitoring by parents. Parental monitoring and mediation contribute to the normalization of intimate surveillance to the extent that surveillance is (re)situated as a necessary culture of care. The choice to not survey infants is thus positioned, worryingly, as a failure of parenting.

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The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms

Cited by 811 publications

References 20 publications

Toward human-centered algorithm design

Toward human-centered algorithm design

Exposure diversity as a design principle for recommender systems

Intimate Surveillance: Normalizing Parental Monitoring and Mediation of Infants Online

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