No Cover Image

Journal article 685 views 108 downloads

Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations

Joseph E Alderman, Joanne Palmer, Elinor Laws, Melissa D McCradden, Johan Ordish, Marzyeh Ghassemi, Stephen R Pfohl, Negar Rostamzadeh, Heather Cole-Lewis, Ben Glocker, Melanie Calvert, Tom J Pollard, Jaspret Gill, Jacqui Gath, Adewale Adebajo, Jude Beng, Cassandra H Leung, Stephanie Kuku, Lesley-Anne Farmer, Rubeta N Matin, Bilal A Mateen, Francis McKay, Katherine Heller, Alan Karthikesalingam, Darren Treanor, Maxine Mackintosh, Lauren Oakden-Rayner, Russell Pearson, Arjun K Manrai, Puja Myles, Judit Kumuthini, Zoher Kapacee, Neil J Sebire, Lama H Nazer, Jarrel Seah, Ashley Akbari Orcid Logo, Lew Berman, Judy W Gichoya, Lorenzo Righetto, Diana Samuel, William Wasswa, Maria Charalambides, Anmol Arora, Sameer Pujari, Charlotte Summers, Elizabeth Sapey, Sharon Wilkinson, Vishal Thakker, Alastair Denniston, Xiaoxuan Liu

The Lancet Digital Health, Volume: 7, Issue: 1, Pages: e64 - e88

Swansea University Author: Ashley Akbari Orcid Logo

  • 68621.VOR.pdf

    PDF | Version of Record

    © 2024 World Health Organization. Published by Elsevier Ltd. This is an Open Access article published under the CC BY 3.0 IGO license.

    Download (1.19MB)

Check full text

DOI (Published version): 10.1016/s2589-7500(24)00224-3

Abstract

Without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. One major source of bias is the data that underpins such technologies. The STANDING Together recommendat...

Full description

Published in: The Lancet Digital Health
ISSN: 2589-7500 2589-7500
Published: Elsevier BV 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa68621
first_indexed 2025-01-09T20:33:59Z
last_indexed 2025-01-23T20:50:01Z
id cronfa68621
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2025-01-23T16:13:45.1271298</datestamp><bib-version>v2</bib-version><id>68621</id><entry>2024-12-27</entry><title>Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations</title><swanseaauthors><author><sid>aa1b025ec0243f708bb5eb0a93d6fb52</sid><ORCID>0000-0003-0814-0801</ORCID><firstname>Ashley</firstname><surname>Akbari</surname><name>Ashley Akbari</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-12-27</date><deptcode>MEDS</deptcode><abstract>Without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. One major source of bias is the data that underpins such technologies. The STANDING Together recommendations aim to encourage transparency regarding limitations of health datasets and proactive evaluation of their effect across population groups. Draft recommendation items were informed by a systematic review and stakeholder survey. The recommendations were developed using a Delphi approach, supplemented by a public consultation and international interview study. Overall, more than 350 representatives from 58 countries provided input into this initiative. 194 Delphi participants from 25 countries voted and provided comments on 32 candidate items across three electronic survey rounds and one in-person consensus meeting. The 29 STANDING Together consensus recommendations are presented here in two parts. Recommendations for Documentation of Health Datasets provide guidance for dataset curators to enable transparency around data composition and limitations. Recommendations for Use of Health Datasets aim to enable identification and mitigation of algorithmic biases that might exacerbate health inequalities. These recommendations are intended to prompt proactive inquiry rather than acting as a checklist. We hope to raise awareness that no dataset is free of limitations, so transparent communication of data limitations should be perceived as valuable, and absence of this information as a limitation. We hope that adoption of the STANDING Together recommendations by stakeholders across the AI health technology lifecycle will enable everyone in society to benefit from technologies which are safe and effective.</abstract><type>Journal Article</type><journal>The Lancet Digital Health</journal><volume>7</volume><journalNumber>1</journalNumber><paginationStart>e64</paginationStart><paginationEnd>e88</paginationEnd><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2589-7500</issnPrint><issnElectronic>2589-7500</issnElectronic><keywords/><publishedDay>1</publishedDay><publishedMonth>1</publishedMonth><publishedYear>2025</publishedYear><publishedDate>2025-01-01</publishedDate><doi>10.1016/s2589-7500(24)00224-3</doi><url/><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>STANDING Together was funded by The NHS AI Lab and The Health Foundation and was supported by the National Institute for Health and Care Research (NIHR; AI_HI200014).</funders><projectreference/><lastEdited>2025-01-23T16:13:45.1271298</lastEdited><Created>2024-12-27T19:28:56.2676141</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Health Data Science</level></path><authors><author><firstname>Joseph E</firstname><surname>Alderman</surname><order>1</order></author><author><firstname>Joanne</firstname><surname>Palmer</surname><order>2</order></author><author><firstname>Elinor</firstname><surname>Laws</surname><order>3</order></author><author><firstname>Melissa D</firstname><surname>McCradden</surname><order>4</order></author><author><firstname>Johan</firstname><surname>Ordish</surname><order>5</order></author><author><firstname>Marzyeh</firstname><surname>Ghassemi</surname><order>6</order></author><author><firstname>Stephen R</firstname><surname>Pfohl</surname><order>7</order></author><author><firstname>Negar</firstname><surname>Rostamzadeh</surname><order>8</order></author><author><firstname>Heather</firstname><surname>Cole-Lewis</surname><order>9</order></author><author><firstname>Ben</firstname><surname>Glocker</surname><order>10</order></author><author><firstname>Melanie</firstname><surname>Calvert</surname><order>11</order></author><author><firstname>Tom J</firstname><surname>Pollard</surname><order>12</order></author><author><firstname>Jaspret</firstname><surname>Gill</surname><order>13</order></author><author><firstname>Jacqui</firstname><surname>Gath</surname><order>14</order></author><author><firstname>Adewale</firstname><surname>Adebajo</surname><order>15</order></author><author><firstname>Jude</firstname><surname>Beng</surname><order>16</order></author><author><firstname>Cassandra H</firstname><surname>Leung</surname><order>17</order></author><author><firstname>Stephanie</firstname><surname>Kuku</surname><order>18</order></author><author><firstname>Lesley-Anne</firstname><surname>Farmer</surname><order>19</order></author><author><firstname>Rubeta N</firstname><surname>Matin</surname><order>20</order></author><author><firstname>Bilal A</firstname><surname>Mateen</surname><order>21</order></author><author><firstname>Francis</firstname><surname>McKay</surname><order>22</order></author><author><firstname>Katherine</firstname><surname>Heller</surname><order>23</order></author><author><firstname>Alan</firstname><surname>Karthikesalingam</surname><order>24</order></author><author><firstname>Darren</firstname><surname>Treanor</surname><order>25</order></author><author><firstname>Maxine</firstname><surname>Mackintosh</surname><order>26</order></author><author><firstname>Lauren</firstname><surname>Oakden-Rayner</surname><order>27</order></author><author><firstname>Russell</firstname><surname>Pearson</surname><order>28</order></author><author><firstname>Arjun K</firstname><surname>Manrai</surname><order>29</order></author><author><firstname>Puja</firstname><surname>Myles</surname><order>30</order></author><author><firstname>Judit</firstname><surname>Kumuthini</surname><order>31</order></author><author><firstname>Zoher</firstname><surname>Kapacee</surname><order>32</order></author><author><firstname>Neil J</firstname><surname>Sebire</surname><order>33</order></author><author><firstname>Lama H</firstname><surname>Nazer</surname><order>34</order></author><author><firstname>Jarrel</firstname><surname>Seah</surname><order>35</order></author><author><firstname>Ashley</firstname><surname>Akbari</surname><orcid>0000-0003-0814-0801</orcid><order>36</order></author><author><firstname>Lew</firstname><surname>Berman</surname><order>37</order></author><author><firstname>Judy W</firstname><surname>Gichoya</surname><order>38</order></author><author><firstname>Lorenzo</firstname><surname>Righetto</surname><order>39</order></author><author><firstname>Diana</firstname><surname>Samuel</surname><order>40</order></author><author><firstname>William</firstname><surname>Wasswa</surname><order>41</order></author><author><firstname>Maria</firstname><surname>Charalambides</surname><order>42</order></author><author><firstname>Anmol</firstname><surname>Arora</surname><order>43</order></author><author><firstname>Sameer</firstname><surname>Pujari</surname><order>44</order></author><author><firstname>Charlotte</firstname><surname>Summers</surname><order>45</order></author><author><firstname>Elizabeth</firstname><surname>Sapey</surname><order>46</order></author><author><firstname>Sharon</firstname><surname>Wilkinson</surname><order>47</order></author><author><firstname>Vishal</firstname><surname>Thakker</surname><order>48</order></author><author><firstname>Alastair</firstname><surname>Denniston</surname><order>49</order></author><author><firstname>Xiaoxuan</firstname><surname>Liu</surname><order>50</order></author></authors><documents><document><filename>68621__33229__2d70771f35904f9c979cdb14da62d05a.pdf</filename><originalFilename>68621.VOR.pdf</originalFilename><uploaded>2025-01-02T13:28:58.8509050</uploaded><type>Output</type><contentLength>1243134</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2024 World Health Organization. Published by Elsevier Ltd. This is an Open Access article published under the CC BY 3.0 IGO license.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/3.0/igo/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2025-01-23T16:13:45.1271298 v2 68621 2024-12-27 Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false 2024-12-27 MEDS Without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. One major source of bias is the data that underpins such technologies. The STANDING Together recommendations aim to encourage transparency regarding limitations of health datasets and proactive evaluation of their effect across population groups. Draft recommendation items were informed by a systematic review and stakeholder survey. The recommendations were developed using a Delphi approach, supplemented by a public consultation and international interview study. Overall, more than 350 representatives from 58 countries provided input into this initiative. 194 Delphi participants from 25 countries voted and provided comments on 32 candidate items across three electronic survey rounds and one in-person consensus meeting. The 29 STANDING Together consensus recommendations are presented here in two parts. Recommendations for Documentation of Health Datasets provide guidance for dataset curators to enable transparency around data composition and limitations. Recommendations for Use of Health Datasets aim to enable identification and mitigation of algorithmic biases that might exacerbate health inequalities. These recommendations are intended to prompt proactive inquiry rather than acting as a checklist. We hope to raise awareness that no dataset is free of limitations, so transparent communication of data limitations should be perceived as valuable, and absence of this information as a limitation. We hope that adoption of the STANDING Together recommendations by stakeholders across the AI health technology lifecycle will enable everyone in society to benefit from technologies which are safe and effective. Journal Article The Lancet Digital Health 7 1 e64 e88 Elsevier BV 2589-7500 2589-7500 1 1 2025 2025-01-01 10.1016/s2589-7500(24)00224-3 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University Another institution paid the OA fee STANDING Together was funded by The NHS AI Lab and The Health Foundation and was supported by the National Institute for Health and Care Research (NIHR; AI_HI200014). 2025-01-23T16:13:45.1271298 2024-12-27T19:28:56.2676141 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Joseph E Alderman 1 Joanne Palmer 2 Elinor Laws 3 Melissa D McCradden 4 Johan Ordish 5 Marzyeh Ghassemi 6 Stephen R Pfohl 7 Negar Rostamzadeh 8 Heather Cole-Lewis 9 Ben Glocker 10 Melanie Calvert 11 Tom J Pollard 12 Jaspret Gill 13 Jacqui Gath 14 Adewale Adebajo 15 Jude Beng 16 Cassandra H Leung 17 Stephanie Kuku 18 Lesley-Anne Farmer 19 Rubeta N Matin 20 Bilal A Mateen 21 Francis McKay 22 Katherine Heller 23 Alan Karthikesalingam 24 Darren Treanor 25 Maxine Mackintosh 26 Lauren Oakden-Rayner 27 Russell Pearson 28 Arjun K Manrai 29 Puja Myles 30 Judit Kumuthini 31 Zoher Kapacee 32 Neil J Sebire 33 Lama H Nazer 34 Jarrel Seah 35 Ashley Akbari 0000-0003-0814-0801 36 Lew Berman 37 Judy W Gichoya 38 Lorenzo Righetto 39 Diana Samuel 40 William Wasswa 41 Maria Charalambides 42 Anmol Arora 43 Sameer Pujari 44 Charlotte Summers 45 Elizabeth Sapey 46 Sharon Wilkinson 47 Vishal Thakker 48 Alastair Denniston 49 Xiaoxuan Liu 50 68621__33229__2d70771f35904f9c979cdb14da62d05a.pdf 68621.VOR.pdf 2025-01-02T13:28:58.8509050 Output 1243134 application/pdf Version of Record true © 2024 World Health Organization. Published by Elsevier Ltd. This is an Open Access article published under the CC BY 3.0 IGO license. true eng http://creativecommons.org/licenses/by/3.0/igo/
title Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
spellingShingle Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
Ashley Akbari
title_short Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
title_full Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
title_fullStr Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
title_full_unstemmed Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
title_sort Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
author_id_str_mv aa1b025ec0243f708bb5eb0a93d6fb52
author_id_fullname_str_mv aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari
author Ashley Akbari
author2 Joseph E Alderman
Joanne Palmer
Elinor Laws
Melissa D McCradden
Johan Ordish
Marzyeh Ghassemi
Stephen R Pfohl
Negar Rostamzadeh
Heather Cole-Lewis
Ben Glocker
Melanie Calvert
Tom J Pollard
Jaspret Gill
Jacqui Gath
Adewale Adebajo
Jude Beng
Cassandra H Leung
Stephanie Kuku
Lesley-Anne Farmer
Rubeta N Matin
Bilal A Mateen
Francis McKay
Katherine Heller
Alan Karthikesalingam
Darren Treanor
Maxine Mackintosh
Lauren Oakden-Rayner
Russell Pearson
Arjun K Manrai
Puja Myles
Judit Kumuthini
Zoher Kapacee
Neil J Sebire
Lama H Nazer
Jarrel Seah
Ashley Akbari
Lew Berman
Judy W Gichoya
Lorenzo Righetto
Diana Samuel
William Wasswa
Maria Charalambides
Anmol Arora
Sameer Pujari
Charlotte Summers
Elizabeth Sapey
Sharon Wilkinson
Vishal Thakker
Alastair Denniston
Xiaoxuan Liu
format Journal article
container_title The Lancet Digital Health
container_volume 7
container_issue 1
container_start_page e64
publishDate 2025
institution Swansea University
issn 2589-7500
2589-7500
doi_str_mv 10.1016/s2589-7500(24)00224-3
publisher Elsevier BV
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str 1
active_str 0
description Without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. One major source of bias is the data that underpins such technologies. The STANDING Together recommendations aim to encourage transparency regarding limitations of health datasets and proactive evaluation of their effect across population groups. Draft recommendation items were informed by a systematic review and stakeholder survey. The recommendations were developed using a Delphi approach, supplemented by a public consultation and international interview study. Overall, more than 350 representatives from 58 countries provided input into this initiative. 194 Delphi participants from 25 countries voted and provided comments on 32 candidate items across three electronic survey rounds and one in-person consensus meeting. The 29 STANDING Together consensus recommendations are presented here in two parts. Recommendations for Documentation of Health Datasets provide guidance for dataset curators to enable transparency around data composition and limitations. Recommendations for Use of Health Datasets aim to enable identification and mitigation of algorithmic biases that might exacerbate health inequalities. These recommendations are intended to prompt proactive inquiry rather than acting as a checklist. We hope to raise awareness that no dataset is free of limitations, so transparent communication of data limitations should be perceived as valuable, and absence of this information as a limitation. We hope that adoption of the STANDING Together recommendations by stakeholders across the AI health technology lifecycle will enable everyone in society to benefit from technologies which are safe and effective.
published_date 2025-01-01T12:17:23Z
_version_ 1850851819825135616
score 11.08895

Similar Items