Maya De Los Santos

dblp:314/7514 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2024
0009-0008-2068-3255ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Collaborative and social computing · 100%
Network and information security
1 paper
Privacy and data protection · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Collaborative and social computing
gig economy
0.812024
Designing Gig Worker Sousveillance Tools · CHI 2024
Privacy and data protection › surveillance
workplace surveillance
0.812024
Designing Gig Worker Sousveillance Tools · CHI 2024

Methods — techniques the papers use, named apart from their topics

semi-structured interviews · 1.5co-design · 1.5
YearPublicationVenuePosition
2024 Designing Gig Worker Sousveillance Tools
abstract
As independently-contracted employees, gig workers disproportionately suffer the consequences of workplace surveillance, which include increased pressures to work, breaches of privacy, and decreased digital autonomy. Despite the negative impacts of workplace surveillance, gig workers lack the tools, strategies, and workplace social support to protect themselves against these harms. Meanwhile, some critical theorists have proposed sousveillance as a potential means of countering such abuses of power, whereby those under surveillance monitor those in positions of authority (e.g., gig workers collect data about requesters/platforms). To understand the benefits of sousveillance systems in the gig economy, we conducted semi-structured interviews and led co-design activities with gig workers. We use “care ethics” as a guiding concept to understand our interview and co-design data, while also focusing on empathic sousveillance technology design recommendations. Through our study we identify gig workers’ attitudes towards and past experiences with sousveillance. We also uncover the type of sousveillance technologies imagined by workers, provide design recommendations, and finish by discussing how to create empowering, empathic spaces on gig platforms.
Kimberly Do, Maya De Los Santos, Saiph Savage
CHI2
2021 The TikTok Tradeoff: Compelling Algorithmic Content at the Expense of Personal Privacy
abstract
This paper presents the results of an interview study with twelve TikTok users to explore user awareness, perception, and experiences with the app’s algorithm in the context of privacy. The social media entertainment app TikTok collects user data to cater individualized video feeds based on users’ engagement with presented content which is regulated in a complex and overly long privacy policy. Our results demonstrate that participants generally have very little knowledge of the actual privacy regulations which is argued for with the benefit of receiving free entertaining content. However, participants experienced privacy-related downsides when algorithmically catered video content increasingly adapted to their biography, interests, or location and they in turn realized the detail of personal data that TikTok had access to. This illustrates the tradeoff users have to make between allowing TikTok to access their personal data and having favorable video consumption experiences on the app.
Daniel Klug, Maya De Los Santos
MUM2