Explainable recommendation: when design meets trust calibration
<div><p>Human-AI collaborative decision-making tools are being increasingly applied in critical domains such as healthcare. However, these tools are often seen as closed and intransparent for human decision-makers. An essential requirement for their success is the ability to provide expl...
محفوظ في:
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| مؤلفون آخرون: | , , |
| منشور في: |
2021
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513516653772800 |
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| author | Mohammad Naiseh (18513738) |
| author2 | Dena Al-Thani (16864245) Nan Jiang (21252) Raian Ali (12066006) |
| author2_role | author author author |
| author_facet | Mohammad Naiseh (18513738) Dena Al-Thani (16864245) Nan Jiang (21252) Raian Ali (12066006) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohammad Naiseh (18513738) Dena Al-Thani (16864245) Nan Jiang (21252) Raian Ali (12066006) |
| dc.date.none.fl_str_mv | 2021-08-02T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s11280-021-00916-0 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Explainable_recommendation_when_design_meets_trust_calibration/25771947 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Human-centred computing Explainable AI Trust Trust Calibration User Centric AI |
| dc.title.none.fl_str_mv | Explainable recommendation: when design meets trust calibration |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Human-AI collaborative decision-making tools are being increasingly applied in critical domains such as healthcare. However, these tools are often seen as closed and intransparent for human decision-makers. An essential requirement for their success is the ability to provide explanations about themselves that are understandable and meaningful to the users. While explanations generally have positive connotations, studies showed that the assumption behind users interacting and engaging with these explanations could introduce trust calibration errors such as facilitating irrational or less thoughtful agreement or disagreement with the AI recommendation. In this paper, we explore how to help trust calibration through explanation interaction design. Our research method included two main phases. We first conducted a think-aloud study with 16 participants aiming to reveal main trust calibration errors concerning explainability in AI-Human collaborative decision-making tools. Then, we conducted two co-design sessions with eight participants to identify design principles and techniques for explanations that help trust calibration. As a conclusion of our research, we provide five design principles: Design for engagement, challenging habitual actions, attention guidance, friction and support training and learning. Our findings are meant to pave the way towards a more integrated framework for designing explanations with trust calibration as a primary goal.</p><p> </p></div><h2>Other Information</h2> <p> Published in: World Wide Web<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11280-021-00916-0" target="_blank">https://dx.doi.org/10.1007/s11280-021-00916-0</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_bb3698cd24dd012b7cb5a85cd811ad8b |
| identifier_str_mv | 10.1007/s11280-021-00916-0 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25771947 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Explainable recommendation: when design meets trust calibrationMohammad Naiseh (18513738)Dena Al-Thani (16864245)Nan Jiang (21252)Raian Ali (12066006)Information and computing sciencesHuman-centred computingExplainable AITrustTrust CalibrationUser Centric AI<div><p>Human-AI collaborative decision-making tools are being increasingly applied in critical domains such as healthcare. However, these tools are often seen as closed and intransparent for human decision-makers. An essential requirement for their success is the ability to provide explanations about themselves that are understandable and meaningful to the users. While explanations generally have positive connotations, studies showed that the assumption behind users interacting and engaging with these explanations could introduce trust calibration errors such as facilitating irrational or less thoughtful agreement or disagreement with the AI recommendation. In this paper, we explore how to help trust calibration through explanation interaction design. Our research method included two main phases. We first conducted a think-aloud study with 16 participants aiming to reveal main trust calibration errors concerning explainability in AI-Human collaborative decision-making tools. Then, we conducted two co-design sessions with eight participants to identify design principles and techniques for explanations that help trust calibration. As a conclusion of our research, we provide five design principles: Design for engagement, challenging habitual actions, attention guidance, friction and support training and learning. Our findings are meant to pave the way towards a more integrated framework for designing explanations with trust calibration as a primary goal.</p><p> </p></div><h2>Other Information</h2> <p> Published in: World Wide Web<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11280-021-00916-0" target="_blank">https://dx.doi.org/10.1007/s11280-021-00916-0</a></p>2021-08-02T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s11280-021-00916-0https://figshare.com/articles/journal_contribution/Explainable_recommendation_when_design_meets_trust_calibration/25771947CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/257719472021-08-02T03:00:00Z |
| spellingShingle | Explainable recommendation: when design meets trust calibration Mohammad Naiseh (18513738) Information and computing sciences Human-centred computing Explainable AI Trust Trust Calibration User Centric AI |
| status_str | publishedVersion |
| title | Explainable recommendation: when design meets trust calibration |
| title_full | Explainable recommendation: when design meets trust calibration |
| title_fullStr | Explainable recommendation: when design meets trust calibration |
| title_full_unstemmed | Explainable recommendation: when design meets trust calibration |
| title_short | Explainable recommendation: when design meets trust calibration |
| title_sort | Explainable recommendation: when design meets trust calibration |
| topic | Information and computing sciences Human-centred computing Explainable AI Trust Trust Calibration User Centric AI |