No Cover Image

Conference Paper/Proceeding/Abstract 183 views

A position on establishing effective explanations from human-centred counterfactuals for automated financial decisions

Daniele Doneddu Orcid Logo, Matt Roach Orcid Logo, Matt Jones Orcid Logo, Jen Pearson Orcid Logo, Alex Blandin, David Sullivan

Proceedings of the AISB 2023 Convention, Swansea University, Swansea, Wales, April 13-14 2023, Pages: 89 - 97

Swansea University Authors: Daniele Doneddu Orcid Logo, Matt Roach Orcid Logo, Matt Jones Orcid Logo, Jen Pearson Orcid Logo, Alex Blandin

Abstract

There has been a historic focus among explainable artificial intelligence practitioners to increase user trust through the provision of explanation traces from algorithmic decisions, either by interpretable agents and expert systems or from transparent surrogate models. Beyond this deterministic cau...

Full description

Published in: Proceedings of the AISB 2023 Convention, Swansea University, Swansea, Wales, April 13-14 2023
ISBN: 978-1-908187-85-7
Published: AISB 2023
Online Access: https://aisb.org.uk/wp-content/uploads/2023/05/aisb2023.pdf
URI: https://cronfa.swan.ac.uk/Record/cronfa64477
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2023-09-08T10:13:17Z
last_indexed 2023-09-08T10:13:17Z
id cronfa64477
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>64477</id><entry>2023-09-08</entry><title>A position on establishing effective explanations from human-centred counterfactuals for automated financial decisions</title><swanseaauthors><author><sid>b1b5db525b5dbd5713e33d143f3d5d60</sid><ORCID>0000-0003-2173-302X</ORCID><firstname>Daniele</firstname><surname>Doneddu</surname><name>Daniele Doneddu</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>9722c301d5bbdc96e967cdc629290fec</sid><ORCID>0000-0002-1486-5537</ORCID><firstname>Matt</firstname><surname>Roach</surname><name>Matt Roach</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>10b46d7843c2ba53d116ca2ed9abb56e</sid><ORCID>0000-0001-7657-7373</ORCID><firstname>Matt</firstname><surname>Jones</surname><name>Matt Jones</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>6d662d9e2151b302ed384b243e2a802f</sid><ORCID>0000-0002-1960-1012</ORCID><firstname>Jen</firstname><surname>Pearson</surname><name>Jen Pearson</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>c21144b9e53f16ca8354576250c9562d</sid><firstname>Alex</firstname><surname>Blandin</surname><name>Alex Blandin</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2023-09-08</date><deptcode>CBAE</deptcode><abstract>There has been a historic focus among explainable artificial intelligence practitioners to increase user trust through the provision of explanation traces from algorithmic decisions, either by interpretable agents and expert systems or from transparent surrogate models. Beyond this deterministic causal reasoning, significant developments were made in probabilistic post-hoc explanations, often presented as percentage confidence or importance and contribution. However, simultaneous work in the social sciences revealed that typical users and experts both preferred and generated explanations that differed in conception with those often employed on artificial intelligence, with the existing deterministic traces and probabilistic reasoning being found less satisfactory, particularly when the best explanations for a given user were not the most likely.In this piece, we hold the position that incorporating an understanding of explanations as a model and process — inspired by social science research in human-centred explanations — will improve user satisfaction and trust in both the given decision and the overall model. We consider how practitioners may design explainable artificial intelligence that enables typical users to interface and interact with counterfactual explanations and therein develop more appropriate explanations for that specific user and decision. Specifically, we argue in favour of satisfying design desiderata for explanations that are causal, contrastive, contextual, and interactively selected with the user. This piece is based on ongoing work that demonstrates the practicability of interactive human-centred explanatory models and is inspired by previous work to uncover design characteristics for counterfac- tual explanations that enhance user trust and understanding in algorithmic decisions for automated financial decisions.</abstract><type>Conference Paper/Proceeding/Abstract</type><journal>Proceedings of the AISB 2023 Convention, Swansea University, Swansea, Wales, April 13-14 2023</journal><volume/><journalNumber/><paginationStart>89</paginationStart><paginationEnd>97</paginationEnd><publisher>AISB</publisher><placeOfPublication/><isbnPrint/><isbnElectronic>978-1-908187-85-7</isbnElectronic><issnPrint/><issnElectronic/><keywords>Explainable AI, Human-centred XAI, Human Computer Interaction, Counterfactual Explanations</keywords><publishedDay>14</publishedDay><publishedMonth>4</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-04-14</publishedDate><doi/><url>https://aisb.org.uk/wp-content/uploads/2023/05/aisb2023.pdf</url><notes>Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB) Convention 2023, Swansea University, Swansea, Wales, April 13-14 2023</notes><college>COLLEGE NANME</college><department>Management School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>CBAE</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>EPSRC</funders><projectreference>EP/S021892/1</projectreference><lastEdited>2024-06-27T13:37:02.4215425</lastEdited><Created>2023-09-08T10:56:20.5794081</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Daniele</firstname><surname>Doneddu</surname><orcid>0000-0003-2173-302X</orcid><order>1</order></author><author><firstname>Matt</firstname><surname>Roach</surname><orcid>0000-0002-1486-5537</orcid><order>2</order></author><author><firstname>Matt</firstname><surname>Jones</surname><orcid>0000-0001-7657-7373</orcid><order>3</order></author><author><firstname>Jen</firstname><surname>Pearson</surname><orcid>0000-0002-1960-1012</orcid><order>4</order></author><author><firstname>Alex</firstname><surname>Blandin</surname><order>5</order></author><author><firstname>David</firstname><surname>Sullivan</surname><order>6</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling v2 64477 2023-09-08 A position on establishing effective explanations from human-centred counterfactuals for automated financial decisions b1b5db525b5dbd5713e33d143f3d5d60 0000-0003-2173-302X Daniele Doneddu Daniele Doneddu true false 9722c301d5bbdc96e967cdc629290fec 0000-0002-1486-5537 Matt Roach Matt Roach true false 10b46d7843c2ba53d116ca2ed9abb56e 0000-0001-7657-7373 Matt Jones Matt Jones true false 6d662d9e2151b302ed384b243e2a802f 0000-0002-1960-1012 Jen Pearson Jen Pearson true false c21144b9e53f16ca8354576250c9562d Alex Blandin Alex Blandin true false 2023-09-08 CBAE There has been a historic focus among explainable artificial intelligence practitioners to increase user trust through the provision of explanation traces from algorithmic decisions, either by interpretable agents and expert systems or from transparent surrogate models. Beyond this deterministic causal reasoning, significant developments were made in probabilistic post-hoc explanations, often presented as percentage confidence or importance and contribution. However, simultaneous work in the social sciences revealed that typical users and experts both preferred and generated explanations that differed in conception with those often employed on artificial intelligence, with the existing deterministic traces and probabilistic reasoning being found less satisfactory, particularly when the best explanations for a given user were not the most likely.In this piece, we hold the position that incorporating an understanding of explanations as a model and process — inspired by social science research in human-centred explanations — will improve user satisfaction and trust in both the given decision and the overall model. We consider how practitioners may design explainable artificial intelligence that enables typical users to interface and interact with counterfactual explanations and therein develop more appropriate explanations for that specific user and decision. Specifically, we argue in favour of satisfying design desiderata for explanations that are causal, contrastive, contextual, and interactively selected with the user. This piece is based on ongoing work that demonstrates the practicability of interactive human-centred explanatory models and is inspired by previous work to uncover design characteristics for counterfac- tual explanations that enhance user trust and understanding in algorithmic decisions for automated financial decisions. Conference Paper/Proceeding/Abstract Proceedings of the AISB 2023 Convention, Swansea University, Swansea, Wales, April 13-14 2023 89 97 AISB 978-1-908187-85-7 Explainable AI, Human-centred XAI, Human Computer Interaction, Counterfactual Explanations 14 4 2023 2023-04-14 https://aisb.org.uk/wp-content/uploads/2023/05/aisb2023.pdf Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB) Convention 2023, Swansea University, Swansea, Wales, April 13-14 2023 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University Another institution paid the OA fee EPSRC EP/S021892/1 2024-06-27T13:37:02.4215425 2023-09-08T10:56:20.5794081 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Daniele Doneddu 0000-0003-2173-302X 1 Matt Roach 0000-0002-1486-5537 2 Matt Jones 0000-0001-7657-7373 3 Jen Pearson 0000-0002-1960-1012 4 Alex Blandin 5 David Sullivan 6
title A position on establishing effective explanations from human-centred counterfactuals for automated financial decisions
spellingShingle A position on establishing effective explanations from human-centred counterfactuals for automated financial decisions
Daniele Doneddu
Matt Roach
Matt Jones
Jen Pearson
Alex Blandin
title_short A position on establishing effective explanations from human-centred counterfactuals for automated financial decisions
title_full A position on establishing effective explanations from human-centred counterfactuals for automated financial decisions
title_fullStr A position on establishing effective explanations from human-centred counterfactuals for automated financial decisions
title_full_unstemmed A position on establishing effective explanations from human-centred counterfactuals for automated financial decisions
title_sort A position on establishing effective explanations from human-centred counterfactuals for automated financial decisions
author_id_str_mv b1b5db525b5dbd5713e33d143f3d5d60
9722c301d5bbdc96e967cdc629290fec
10b46d7843c2ba53d116ca2ed9abb56e
6d662d9e2151b302ed384b243e2a802f
c21144b9e53f16ca8354576250c9562d
author_id_fullname_str_mv b1b5db525b5dbd5713e33d143f3d5d60_***_Daniele Doneddu
9722c301d5bbdc96e967cdc629290fec_***_Matt Roach
10b46d7843c2ba53d116ca2ed9abb56e_***_Matt Jones
6d662d9e2151b302ed384b243e2a802f_***_Jen Pearson
c21144b9e53f16ca8354576250c9562d_***_Alex Blandin
author Daniele Doneddu
Matt Roach
Matt Jones
Jen Pearson
Alex Blandin
author2 Daniele Doneddu
Matt Roach
Matt Jones
Jen Pearson
Alex Blandin
David Sullivan
format Conference Paper/Proceeding/Abstract
container_title Proceedings of the AISB 2023 Convention, Swansea University, Swansea, Wales, April 13-14 2023
container_start_page 89
publishDate 2023
institution Swansea University
isbn 978-1-908187-85-7
publisher AISB
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url https://aisb.org.uk/wp-content/uploads/2023/05/aisb2023.pdf
document_store_str 0
active_str 0
description There has been a historic focus among explainable artificial intelligence practitioners to increase user trust through the provision of explanation traces from algorithmic decisions, either by interpretable agents and expert systems or from transparent surrogate models. Beyond this deterministic causal reasoning, significant developments were made in probabilistic post-hoc explanations, often presented as percentage confidence or importance and contribution. However, simultaneous work in the social sciences revealed that typical users and experts both preferred and generated explanations that differed in conception with those often employed on artificial intelligence, with the existing deterministic traces and probabilistic reasoning being found less satisfactory, particularly when the best explanations for a given user were not the most likely.In this piece, we hold the position that incorporating an understanding of explanations as a model and process — inspired by social science research in human-centred explanations — will improve user satisfaction and trust in both the given decision and the overall model. We consider how practitioners may design explainable artificial intelligence that enables typical users to interface and interact with counterfactual explanations and therein develop more appropriate explanations for that specific user and decision. Specifically, we argue in favour of satisfying design desiderata for explanations that are causal, contrastive, contextual, and interactively selected with the user. This piece is based on ongoing work that demonstrates the practicability of interactive human-centred explanatory models and is inspired by previous work to uncover design characteristics for counterfac- tual explanations that enhance user trust and understanding in algorithmic decisions for automated financial decisions.
published_date 2023-04-14T13:37:02Z
_version_ 1803017857300168704
score 11.012678

Similar Items