Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems 2021
DOI: 10.1145/3411763.3441331
|View full text |Cite
|
Sign up to set email alerts
|

“This Seems to Work”: Designing Technological Systems with The Algorithmic Imaginations of Those Who Labor

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
5
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
4
2
1

Relationship

0
7

Authors

Journals

citations
Cited by 9 publications
(5 citation statements)
references
References 12 publications
0
5
0
Order By: Relevance
“…A concern for trust shifted from the fnancial services to communities around them. Like prior work on ride sharing [14,58], participants found additional forms of gatekeeping in their interactions with Cash App customer service personnel. This observation refects a concept of "community-cultural wealth" [46,74] that speaks to shared communal and cultural attributes.…”
Section: Beyond Legacies Of Distrust: From Trustworthy Features Tomentioning
confidence: 83%
“…A concern for trust shifted from the fnancial services to communities around them. Like prior work on ride sharing [14,58], participants found additional forms of gatekeeping in their interactions with Cash App customer service personnel. This observation refects a concept of "community-cultural wealth" [46,74] that speaks to shared communal and cultural attributes.…”
Section: Beyond Legacies Of Distrust: From Trustworthy Features Tomentioning
confidence: 83%
“…Platforms both operate in and inculcate a brutal economy of attention, where only the “top” few options are usually ever seen by viewers. These “top” options are usually decided algorithmically, according to rankings along a variety of metrics (Stark and Pais, 2021), metrics which are often invisible to the people contributing to the platform (Burrell, 2016; Cameron et al, 2021). Algorithmic competition introduces another layer of labor into platform work: not just the labor assigned by the platform, but the work of being on the platform.…”
Section: Discussionmentioning
confidence: 99%
“…A spectrum of organizational control has emerged in the literature on algorithmic managements: as organizations use digital structures to wield increasing surveillance and control over gig workers (Mateescu and Nguyen, 2019; Rosenblat, 2018; Rosenblat et al, 2014; Ticona et al, 2018), workers also enact resistance and reclaim their own autonomy (Cameron, 2020; Jarrahi and Sutherland, 2019; Kellogg et al, 2020). However, little is known about the interstitial tissue in between the micro-meso levels: the behaviors, workers’ navigations of this spectrum of control: the peer-to-peer communications underpinning workers’ own strategies for understanding and negotiating algorithmic systems and their constituent parts, Workers in gig or alternative arrangements must constantly contend with opaque algorithmic systems (Bishop, 2019; Burrell, 2016; Cameron et al, 2021). Yet, unlike workers in traditional employee arrangements, there are no physically close co-workers of whom to ask questions.…”
Section: Introductionmentioning
confidence: 99%
“…Conspiracy theories have long been with us, but the rise of large audiences via social media [43] for phenomena such as QAnon, anti-5G activism, anti-mask and anti-vax beliefs during the COVID pandemic, and revelations of behavioral advertising driven by political actors such as Cambridge Analytica, is coincident with an increase in everyday experiences of hard-to-explain algorithmic or AI-driven decisions. From job allocation in the gig economy [21] to interactions with voice assistants in the home [14], even the grading of school exams [16], the technologies of everyday life become an "echo chamber of conspiracism" [85] amidst perceived breakdowns in explainability-and the intersections with the wider field of efforts in explainable AI (XAI) have largely not yet been explored in design and HCI research.…”
Section: Breakdowns In Explainabilitymentioning
confidence: 99%