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Evan Metsky

dblp:161/3427 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

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

Human-computer interaction and ubiquitous computing · 1

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 · 77% Human-AI interaction · 23%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational social science and digital humanities
future of work
0.212015
Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers · CHI 2015
Collaborative and social computing › socio-technical systems
algorithmic management
0.212015
Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers · CHI 2015

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

thematic analysis · 0.4qualitative study · 0.4
YearPublicationVenuePosition
2015 Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers
abstract
Software algorithms are changing how people work in an ever-growing number of fields, managing distributed human workers at a large scale. In these work settings, human jobs are assigned, optimized, and evaluated through algorithms and tracked data. We explore the impact of this algorithmic, data-driven management on human workers and work practices in the context of Uber and Lyft, new ridesharing services. Our findings from a qualitative study describe how drivers responded when algorithms assigned work, provided informational support, and evaluated their performance, and how drivers used online forums to socially make sense of the algorithm features. Implications and future work are discussed.
Min Kyung Lee, Daniel Kusbit, Evan Metsky, Laura A. Dabbish
CHI3