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Rylie Spiegel

dblp:316/5796 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0003-9088-4187ORCID · corroborated

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

Human-computer interaction and ubiquitous computing · 2 · 2 since 2021Artificial intelligence and machine learning · 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
2 papers
Human-robot interaction · 62% Design research and methods · 33% Collaborative and social computing · 6%

Topics — the 3 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Design research and methods
participatory design
1.012026
Bridging Technology and Policy Design: A Robot Policy Design Toolkit to Support Collaborations in Policymaking · CHI 2026
Human-robot interaction
child-robot interaction
0.612022
Children's Perspectives of Advertising with Social Robots: A Policy Investigation · HRI 2022
Human-robot interaction
stakeholder collaboration
0.312026
Bridging Technology and Policy Design: A Robot Policy Design Toolkit to Support Collaborations in Policymaking · CHI 2026

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

design toolkit · 1.0participatory design · 0.6
YearPublicationVenuePosition
2026 Bridging Technology and Policy Design: A Robot Policy Design Toolkit to Support Collaborations in Policymaking
Anastasia K. Ostrowski, Daniella DiPaola, Rylie Spiegel, Zandra H. Feland, Zeynep Yalcin, Cynthia Breazeal
CHI3
2022 Children's Perspectives of Advertising with Social Robots: A Policy Investigation
abstract
Children are beginning to interact and develop rapport with social robots in their homes. These devices pose new concerns around marketing to children. These include questions of how advertisements can and should be embedded in a robot and the robot's persona and which methods of conveying advertisements to the user are deceptive. In this paper, we engage with 62 children ages 9–12 in an activity to design future robot advertising policies. Results demonstrate that children prefer robots to advertise to them through casual conversations, citing a more positive user experience and the benefit of personalized and conversationally relevant advertising. These findings illuminate a tension between child preferences and more deceptive advertising policies. Overall, the work presented in this paper prompts new design and legal policy questions for how and if robots should advertise to children.
Daniella DiPaola, Anastasia K. Ostrowski, Rylie Spiegel, Kate Darling, Cynthia Breazeal
HRI3