A path to understanding the effects of algorithm awareness

Hamilton, Kevin; Karahalios, Karrie; Sandvig, Christian; Eslami, Motahhare

doi:10.1145/2559206.2578883

Cited by 108 publications

(73 citation statements)

References 15 publications

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“…What our respondents described is different from systematic attempts by researchers and journalists to perform experiments intended to discover details about the parameters and weighting used by the News Feed algorithm to rank posts for display. Determining the specifics of the algorithm by comparing users' experiences is nearly impossible, because each user has a different network of Friends who serve as the sources of content [15]. However, each user is familiar with who his or her Friends are and may naturally make attributions about how interactions with the system are related to changes in what they see at a later time.…”

Section: Reverse-engineering the Algorithmmentioning

confidence: 99%

“…Finally, as Hamilton et al point out, there is much debate about whether and to what extent automated system processes should be made visible to the humans who interact with them [15]. Although it may be a sign of good design when users do not detect algorithms at work, the consequences of this invisibility may exacerbate the impact of different user and algorithm goals.…”

Section: Reverse-engineering the Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed

Rader

Gray

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

268

196

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People are becoming increasingly reliant on online sociotechnical systems that employ algorithmic curation to organize, select and present information. We wanted to understand how individuals make sense of the influence of algorithms, and how awareness of algorithmic curation may impact their interaction with these systems. We investigated user understanding of algorithmic curation in Facebook's News Feed, by analyzing open-ended responses to a survey question about whether respondents believe their News Feeds show them every post their Facebook Friends create. Responses included a wide range of beliefs and causal inferences, with different potential consequences for user behavior in the system. Because user behavior is both input for algorithms and constrained by them, these patterns of belief may have tangible consequences for the system as a whole.

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Section: Reverse-engineering the Algorithmmentioning

confidence: 99%

Section: Reverse-engineering the Algorithmmentioning

confidence: 99%

Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed

Rader

Gray

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

268

196

View full text Add to dashboard Cite

show abstract

“…Many researchers have considered curation algorithms and argued that their effects are important while their operation is opaque [2,19,36]. For example, search algorithms structure the online information available to a society, and may function as a gatekeeper [18,20].…”

Section: Algorithmsmentioning

confidence: 99%

“…Some commentators are primarily concerned about friends that "vanish" from the platform [33], and others see an opportunity for profit linked to the position of posts [5]. While other work has attempted to reverse-engineer these algorithmic processes [19] or develop new summaries of algorithmic results [10,13,26], to our knowledge no researchers have developed systems to reveal to users the contrast between algorithmically manipulated and unfiltered results.…”

Section: Algorithmsmentioning

confidence: 99%

"I always assumed that I wasn't really that close to [her]"

Eslami

Rickman

Vaccaro

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

Self Cite

432

154

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Our daily digital life is full of algorithmically selected content such as social media feeds, recommendations and personalized search results. These algorithms have great power to shape users' experiences, yet users are often unaware of their presence. Whether it is useful to give users insight into these algorithms' existence or functionality and how such insight might affect their experience are open questions. To address them, we conducted a user study with 40 Facebook users to examine their perceptions of the Facebook News Feed curation algorithm. Surprisingly, more than half of the participants (62.5%) were not aware of the News Feed curation algorithm's existence at all. Initial reactions for these previously unaware participants were surprise and anger. We developed a system, FeedVis, to reveal the difference between the algorithmically curated and an unadulterated News Feed to users, and used it to study how users perceive this difference. Participants were most upset when close friends and family were not shown in their feeds. We also found participants often attributed missing stories to their friends' decisions to exclude them rather than to Facebook News Feed algorithm. By the end of the study, however, participants were mostly satisfied with the content on their feeds. Following up with participants two to six months after the study, we found that for most, satisfaction levels remained similar before and after becoming aware of the algorithm's presence, however, algorithmic awareness led to more active engagement with Facebook and bolstered overall feelings of control on the site.

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“…We extend Chun's argument to focus on the labor relations and contracts that make code ''work'' both in terms of machine execution and in terms of what it does for humans. We also build on the prior work of critical social scientists who identify how algorithmic work structures power relations by enacting discrimination and social sorting (Barocas and Selbst, 2016;Pasquale, 2015), promulgating labor inequalities (Gray et al, 2016;Irani, 2015) or shaping cultural production through algorithm design choices (Gillespie, 2014;Hamilton et al, 2014;McKelvey, 2014).…”

Section: A Term With Baggagementioning

confidence: 99%

Algorithms as fetish: Faith and possibility in algorithmic work

2018

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Algorithms are powerful because we invest in them the power to do things. With such promise, they can transform the ordinary, say snapshots along a robotic vacuum cleaner's route, into something much more, such as a clean home. Echoing David Graeber's revision of fetishism, we argue that this easy slip from technical capabilities to broader claims betrays not the ''magic'' of algorithms but rather the dynamics of their exchange. Fetishes are not indicators of false thinking, but social contracts in material form. They mediate emerging distributions of power often too nascent, too slippery or too disconcerting to directly acknowledge. Drawing primarily on 2016 ethnographic research with computer vision professionals, we show how faith in what algorithms can do shapes the social encounters and exchanges of their production. By analyzing algorithms through the lens of fetishism, we can see the social and economic investment in some people's labor over others. We also see everyday opportunities for social creativity and change. We conclude that what is problematic about algorithms is not their fetishization but instead their stabilization into full-fledged gods and demonsthe more deserving objects of critique.

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A path to understanding the effects of algorithm awareness

Cited by 108 publications

References 15 publications

Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed

Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed

"I always assumed that I wasn't really that close to [her]"

Algorithms as fetish: Faith and possibility in algorithmic work

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