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How Mock Model Training Enhances User Perceptions of AI Systems [article]

Amama Mahmood, Gopika Ajaykumar, Chien-Ming Huang
2021 arXiv   pre-print
Artificial Intelligence (AI) is an integral part of our daily technology use and will likely be a critical component of emerging technologies. However, negative user preconceptions may hinder adoption of AI-based decision making. Prior work has highlighted the potential of factors such as transparency and explainability in improving user perceptions of AI. We further contribute to work on improving user perceptions of AI by demonstrating that bringing the user in the loop through mock model
more » ... ning can improve their perceptions of an AI agent's capability and their comfort with the possibility of using technology employing the AI agent.
arXiv:2111.08830v1 fatcat:ur3dpzndzrcibmml6qoylmusmi

A Survey on End-User Robot Programming [article]

Gopika Ajaykumar, Maureen Steele, Chien-Ming Huang
2021 arXiv   pre-print
Ajaykumar, Steele, and Huang  ... 
arXiv:2105.01757v1 fatcat:wx27myh5gffhxf3usmxzxbv4kq

FACT: A Full-body Ad-hoc Collaboration Testbed for Modeling Complex Teamwork [article]

Gopika Ajaykumar, Annie Mao, Jeremy Brown, Chien-Ming Huang
2021 arXiv   pre-print
Robots are envisioned to work alongside humans in applications ranging from in-home assistance to collaborative manufacturing. Research on human-robot collaboration (HRC) has helped develop various aspects of social intelligence necessary for robots to participate in effective, fluid collaborations with humans. However, HRC research has focused on dyadic, structured, and minimal collaborations between humans and robots that may not fully represent the large scale and emergent nature of more
more » ... lex, unstructured collaborative activities. Thus, there remains a need for shared testbeds, datasets, and evaluation metrics that researchers can use to better model natural, ad-hoc human collaborative behaviors and develop robot capabilities intended for large scale emergent collaborations. We present one such shared resource - FACT (Full-body Ad-hoc Collaboration Testbed), an openly accessible testbed for researchers to obtain an expansive view of the individual and team-based behaviors involved in complex, co-located teamwork. We detail observations from a preliminary exploration with teams of various sizes and discuss potential research questions that may be investigated using the testbed. Our goal is for FACT to be an initial resource that supports a more holistic investigation of human-robot collaboration.
arXiv:2106.03290v1 fatcat:fqvrezonr5dgxoc43fkuywxtoy

Object Permanence Through Audio-Visual Representations [article]

Fanjun Bu, Chien-Ming Huang
2021 arXiv   pre-print
ACKNOWLEDGMENTS We would like to thank Gopika Ajaykumar for proofreading this paper and the Johns Hopkins University for supporting this work.  ... 
arXiv:2010.09948v2 fatcat:mno64mjzifd65hpsfwmm3za5cq