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Jonathan Lynn

dblp:217/8829 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-2682-0424ORCID · reported

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

Human-computer interaction and ubiquitous computing · 1 · 1 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 · 62% Design research and methods · 38%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Collaborative and social computing › social computing › platform labor
gig work
0.712023
Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes · CHI 2023
Design research and methods
participatory design
0.712023
Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes · CHI 2023
Collaborative and social computing › socio-technical systems
algorithmic management
0.212023
Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes · CHI 2023
Collaborative and social computing › crowdsourcing › crowd work
worker advocacy
0.212023
Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes · CHI 2023

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

data probes · 0.7co-design · 0.7
YearPublicationVenuePosition
2023 Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes
abstract
AI technologies continue to advance from digital assistants to assisted decision-making. However, designing AI remains a challenge given its unknown outcomes and uses. One way to expand AI design is by centering stakeholders in the design process. We conduct co-design sessions with gig workers to explore the design of gig worker-centered tools as informed by their driving patterns, decisions, and personal contexts. Using workers’ own data as well as city-level data, we create probes—interactive data visuals—that participants explore to surface the well-being and positionalities that shape their work strategies. We describe participant insights and corresponding AI design considerations surfaced from data probes about: 1) workers’ well-being trade-offs and positionality constraints, 2) factors that impact well-being beyond those in the data probes, and 3) instances of unfair algorithmic management. We discuss the implications for designing data probes and using them to elevate worker-centered AI design as well as for worker advocacy.
Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun Wei Wang, Min Kyung Lee
CHI3