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Senya Wong

dblp:379/0207 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0003-1270-7815ORCID · 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
Human-AI interaction · 100%

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

TopicWeightPapersLastEvidence papers
Human-AI interaction › AI-mediated communication
AI disclosure
1.012026
"Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Work · CHI 2026
Human-AI interaction
AI-mediated work
0.312026
"Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Work · CHI 2026

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

survey study · 1.0interview study · 1.0
YearPublicationVenuePosition
2026 "Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Work
abstract
The growing use of AI applications among freelance workers is reshaping trust and relationships with clients. This paper investigates how both workers and clients perceive AI use and disclosure in the freelance economy through a three-stage study: interviews with workers and two survey studies with workers and clients. Findings first reveal a key expectation gap around disclosure: Workers often adopt passive disclosure practices, revealing AI use only when asked, as they assume clients can already detect it. Clients, however, are far less confident in recognizing AI-assisted work and prefer proactive disclosure. A second finding highlights the role of unclear or absent client AI policies, which leave workers consistently misinterpreting clients’ expectations for AI use and disclosure. Together, these gaps point to the need for clearer guidelines and practices for AI disclosure. Insights extend beyond freelancing, offering implications for trust, accountability, and policy design in other AI-mediated work domains.
Angel Hwang, Senya Wong, Baixiao Chen, Jessica He, Hyo Jin Do
CHI2