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More than trust: compliance in instantaneous human-robot interactions

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conference contribution
posted on 2025-05-19, 14:24 authored by Sachini Weerawardhana, Michael E. Akintunde, Peta Masters, Aaron Roberts, Genovefa Kefalidou, Yang LuYang Lu, Gerard Canal, Nicole Lehchevska, Elisabeth Halvorsen, Wei Wei, Luc Moreau

Compliance is when a human positively responds to a request or a recommendation given by a system. For example, when prompted, providing your thumbprint for an automated biometric scanner at the airport or starting to watch a new TV show on a streaming service ‘we think you will love’. In trust-related research, compliance is frequently used as a behavioural measure of trust. When evaluating the compliance-trust association in experimental settings, typically, the participants agree, when asked, that they complied because they trusted the system. We developed three scenarios in instantaneous settings where compliance with an instruction delivered by a robot would typically be ascribed to trust. However, rather than asking, ‘Did you trust?’, we asked, ‘Why did you comply?’ In a thematic analysis of responses, we discovered robot design characteristics and sources not related to the design that persuade humans to comply with instructions delivered by a robot.

Funding

UKRI Trustworthy Autonomous Systems Node in Verifiability

UK Research and Innovation

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History

School

  • Science

Department

  • Computer Science

Published in

2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)

Pages

1556 - 1563

Source

2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)

Publisher

IEEE

Version

  • AM (Accepted Manuscript)

Rights holder

Accepted manuscript © The Authors; publisher version © IEEE

Publisher statement

For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising.

Publication date

2024-08-26

Copyright date

2024

ISBN

9798350375022 ; 9798350375039

ISSN

1944-9445

eISSN

1944-9437

Language

  • en

Location

Pasadena, CA, USA

Event dates

26th August 2024 - 30th August 2024

Depositor

Dr Yang Lu. Deposit date: 19 April 2025

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