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Yuheng Wu 0004

dblp:73/9686-4 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0001-8473-0999ORCID · verified

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 · 56% Human-AI interaction · 44%

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

TopicWeightPapersLastEvidence papers
Collaborative and social computing › cooperative work
group decision-making
0.712023
Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making · CHI 2023
Human-AI interaction › AI-assisted decision-making
human-AI collaborative decision making
0.712023
Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making · CHI 2023
Collaborative and social computing › social interaction
group dynamics
0.212023
Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making · CHI 2023

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

wizard-of-oz study · 0.7speculative design · 0.7
YearPublicationVenuePosition
2023 Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making
abstract
Existing research on human-AI collaborative decision-making focuses mainly on the interaction between AI and individual decision-makers. There is a limited understanding of how AI may perform in group decision-making. This paper presents a wizard-of-oz study in which two participants and an AI form a committee to rank three English essays. One novelty of our study is that we adopt a speculative design by endowing AI equal power to humans in group decision-making. We enable the AI to discuss and vote equally with other human members. We find that although the voice of AI is considered valuable, AI still plays a secondary role in the group because it cannot fully follow the dynamics of the discussion and make progressive contributions. Moreover, the divergent opinions of our participants regarding an “equal AI” shed light on the possible future of human-AI relations.
Chengbo Zheng, Yuheng Wu 0004, Chuhan Shi, Shuai Ma 0005, Jiehui Luo, Xiaojuan Ma
CHI2