Merge pull request #399 from NGO-Algorithm-Audit/feature/structural_edits

EN NL > updated new paper UBDT en updated some content on page

Author: jfparie

content/english/technical-tools/BDT.md

@@ -23,8 +23,8 @@ quick_navigation:
       url: "#local-only"
     - title: Supported by
       url: "#supported-by"
-    - title: Awards and acknowledgements
-      url: "#awards-acknowledgements"
+    - title: Awards and publications
+      url: "#awards-publications"
     - title: Summary
       url: "#summary"
     - title: Team
@@ -64,7 +64,11 @@ team:
       - image: /images/people/MJorgensen.jpeg
         name: Mackenzie Jorgensen PhD
         bio: |
-          Researcher Alan Turing Institute, London
+          Postdoctoral Research Fellow, Northumbria University
+      - image: /images/people/JParie.jpg
+        name: Jurriaan Parie
+        bio: |
+          Director, Algorithm Audit
 ---
 
 <!-- Promobar -->
@@ -204,7 +208,7 @@ The HBAC algorithm maximizes the difference in bias variable between clusters. T
 
 The unsupervised bias detection tool has been applied in practice to audit a Dutch public sector risk profiling algorithm. Our [team](/technical-tools/bdt/#team) documented this case in a scientific paper. The tool identified proxies for students with a non-European migration background in a risk profiling algorithm. Specifically the most deviating cluster contains above average students following vocational education and has higher-than-average students living far away from their parent(s)' address, which turned out to be correlate significantly with students with a non-European migration background. Deviations in the control process could therefore also have been found if aggregation statistics on the origin of students had not been available. The results are also described in Appendix A of the below report. This report was sent to <a href="https://www.tweedekamer.nl/kamerstukken/detail?id=2024D20431&did=2024D20431" target="_blank">Dutch parliament</a> on 22-05-2024.
 
-{{< embed_pdf url="/pdf-files/technical-tools/UBDT/20250205 Auditing a Dutch Public Sector Risk Profiling Algorithm.pdf" url2="/pdf-files/algoprudence/TA_AA202402/TA_AA202402_Addendum_Preventing_prejudice.pdf" width_mobile_pdf="12" width_desktop_pdf="6" >}}
+{{< embed_pdf url="/pdf-files/technical-tools/UBDT/20260215 Auditing a Dutch Public Sector Risk Profiling Algorithm.pdf" url2="/pdf-files/algoprudence/TA_AA202402/TA_AA202402_Addendum_Preventing_prejudice.pdf" width_mobile_pdf="12" width_desktop_pdf="6" >}}
 
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@@ -260,19 +264,19 @@ In 2024, the SIDN Fund <a href="https://www.sidnfonds.nl/projecten/open-source-a
 
 {{< container_close >}}
 
-<!-- Awards and acknowledgements -->
+<!-- Awards and publications -->
 
-{{< container_open title="Awards and acknowledgements" icon="fas fa-medal" id="awards-acknowledgements" >}}
+{{< container_open title="Awards and publications" icon="fas fa-medal" id="awards-publications" >}}
 
-This tool has received awards and is acknowledged by various <a href="https://github.com/NGO-Algorithm-Audit/unsupervised-bias-detection?tab=readme-ov-file#contributing-members" target="_blank">stakeholders</a>, including civil society organisations, industry representatives and academics.
+This tool has received awards and is acknowledged by various <a href="https://github.com/NGO-Algorithm-Audit/unsupervised-bias-detection?tab=readme-ov-file#contributing-members" target="_blank">stakeholders</a>, including civil society organisations, industry representatives and academic outlets.
 
 {{< accordions_area_open id="awards-accordion" >}}
 
-{{< accordion_item_open title="Finalist Stanford’s AI Audit Challenge 2023" image="/images/partner logo-cropped/StanfordHAI.png" tag1="06-2023" >}}
+{{< accordion_item_open title="IASEAI’26 presentation" image="/images/BDT/IASEAI_logo.png" tag1="2026" >}}
 
 ##### Description
 
-Under the name Joint Fairness Assessment Method (JFAM) the unsupervised bias detection tool has been selected as a finalist in <a href="https://hai.stanford.edu/ai-audit-challenge-2023-finalists" target="_blank">Stanford’s AI Audit Competition 2023</a>.
+The [scientific paper](/#scientific-paper) of the tool was presented during the the International Association for Safe and Ethical Artificial Intelligence (<a href="https://www.iaseai.org" target="_blank">IASEAI’26</a>).
 
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@@ -284,6 +288,14 @@ The unsupervised bias detection tool is part of OECD's <a href="https://oecd.ai/
 
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+{{< accordion_item_open title="Finalist Stanford’s AI Audit Challenge 2023" image="/images/partner logo-cropped/StanfordHAI.png" tag1="2023" >}}
+
+##### Description
+
+Under the name Joint Fairness Assessment Method (JFAM) the unsupervised bias detection tool has been selected as a finalist in <a href="https://hai.stanford.edu/ai-audit-challenge-2023-finalists" target="_blank">Stanford’s AI Audit Competition 2023</a>.
+
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+
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content/nederlands/technical-tools/BDT.md

@@ -22,8 +22,8 @@ quick_navigation:
       url: "#local-only"
     - title: Ondersteund door
       url: "#supported-by"
-    - title: Prijzen en ondersteuning
-      url: "#awards-acknowledgements"
+    - title: Prijzen en publicaties
+      url: "#awards-publications"
     - title: Samenvatting
       url: "#summary"
     - title: Team
@@ -64,7 +64,11 @@ team:
       - image: /images/people/MJorgensen.jpeg
         name: Mackenzie Jorgensen PhD
         bio: |
-          Onderzoeker Alan Turing Institute, Londen
+          Postdoctorale onderzoeker, Northumbria University
+      - image: /images/people/JParie.jpg
+        name: Jurriaan Parie
+        bio: |
+          Directeurr, Algorithm Audit
 ---
 
 <!-- Promobar -->
@@ -206,7 +210,7 @@ Het HBAC-algoritme maximaliseert het verschil in bias variabele tussen clusters.
 
 De unsupervised bias detectie tool is in de praktijk toegepast om een risicoprofileringsalgoritme van de Dienst Uitvoering Onderwijs (DUO) te auditen. Ons [team](/technical-tools/bdt/#team) heeft deze casus uitgewerkt in een wetenschappelijke paper. De tool identificeerde proxies voor studenten met een niet-Europese migratieachtergrond in het risicoprofileringsalgoritme, specifiek opleidingsniveau en de afstand tussen het adres van de student en dat van hun ouder(s). Afwijkingen in het controleproces hadden dus ook gevonden kunnen worden als CBS-data over de herkomst van studenten niet beschikbaar was geweest. De resultaten worden ook beschreven in Appendix A van het onderstaande rapport. Dit rapport is op 22-05-2024 naar de <a href="https://www.tweedekamer.nl/kamerstukken/detail?id=2024D20431&did=2024D20431" target="_blank">Tweede Kamer</a> gestuurd.
 
-{{< embed_pdf url="/pdf-files/technical-tools/UBDT/20250205 Auditing a Dutch Public Sector Risk Profiling Algorithm.pdf" url2="/pdf-files/algoprudence/TA_AA202402/TA_AA202402_Addendum_Vooringenomenheid_voorkomen.pdf" width_mobile_pdf="12" width_desktop_pdf="6" >}}
+{{< embed_pdf url="/pdf-files/technical-tools/UBDT/20260215 Auditing a Dutch Public Sector Risk Profiling Algorithm.pdf" url2="/pdf-files/algoprudence/TA_AA202402/TA_AA202402_Addendum_Vooringenomenheid_voorkomen.pdf" width_mobile_pdf="12" width_desktop_pdf="6" >}}
 
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@@ -262,19 +266,19 @@ In 2024 ondersteunde het SIDN Fonds <a href="https://www.sidnfonds.nl/projecten/
 
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-<!-- Prijzen en ondersteuning -->
+<!-- Prijzen en publicaties -->
 
-{{< container_open title="Prijzen en ondersteuning" icon="fas fa-medal" id="awards-acknowledgements">}}
+{{< container_open title="Prijzen en publicaties" icon="fas fa-medal" id="awards-publications">}}
 
-De tool heeft prijzen ontvangen en wordt ondersteund door verschillende <a href="https://github.com/NGO-Algorithm-Audit/unsupervised-bias-detection?tab=readme-ov-file#contributing-members" target="_blank">belanghebbenden</a>, waaronder maatschappelijke organisaties, vertegenwoordigers uit de industrie en academici.
+De tool heeft prijzen ontvangen en is erkend door verschillende <a href="https://github.com/NGO-Algorithm-Audit/unsupervised-bias-detection?tab=readme-ov-file#contributing-members" target="_blank">belanghebbenden</a>, waaronder maatschappelijke organisaties, vertegenwoordigers uit de industrie en academische outlets.
 
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-{{< accordion_item_open title="Finalist Stanford’s AI Audit Challenge 2023" image="/images/partner logo-cropped/StanfordHAI.png" tag1="06-2023" >}}
+{{< accordion_item_open title="IASEAI’26 presentatie" image="/images/BDT/IASEAI_logo.png" tag1="2026" >}}
 
-##### Beschrijving
+##### Description
 
-Onder de naam Joint Fairness Assessment Method (JFAM) is de unsupervised bias detectie tool geselecteerd als finalist voor <a href="https://hai.stanford.edu/ai-audit-challenge-2023-finalists" target="_blank">Stanford’s AI Audit Competition 2023</a>.
+De [wetenschappelijke paper](/#scientific-paper) over de tool werd gepresenteerd tijds het International Association for Safe and Ethical Artificial Intelligence (<a href="https://www.iaseai.org" target="_blank">IASEAI’26</a>).
 
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@@ -286,6 +290,14 @@ De unsupervised bias detectie tool maakt deel uit van de <a href="https://oecd.a
 
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+{{< accordion_item_open title="Finalist Stanford’s AI Audit Challenge 2023" image="/images/partner logo-cropped/StanfordHAI.png" tag1="2023" >}}
+
+##### Beschrijving
+
+Onder de naam Joint Fairness Assessment Method (JFAM) is de unsupervised bias detectie tool geselecteerd als finalist voor <a href="https://hai.stanford.edu/ai-audit-challenge-2023-finalists" target="_blank">Stanford’s AI Audit Competition 2023</a>.
+
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+
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