Merge pull request #328 from NGO-Algorithm-Audit/feature/structural_edits

Feature/structural edits

Author: jfparie

content/.DS_Store

@@ -52,25 +52,22 @@ overview_block:
 Activity_Feed:
   featured_title: Featured
   featured_activities:
+    - title: 'Algoprudence: Predicting irresponsible driving'
+      intro: >
+        Case study how irresponsible driving can be predicted based on data of a car sharing platform.
+      link: /algoprudence/cases/aa202501_predicting-irresponsible-driving-behavior/
+      image: /images/algoprudence/AA202501/Cover_EN.png
+      date: 30-09-2025
+      type: algoprudence
     - title: >-
         Inventory 14 Dutch Ministries Netherlands Algorithm Registry
       intro: >
-        Last summer, 14 Dutch ministries published their inventories of high-risk AI systems and high-impact algorithms. We compiled an overview.
+        Overview of 14 Dutch ministries' inventories of high-risk AI systems and high-impact algorithms.
       link: >-
         /knowledge-platform/knowledge-base/inventory_high_risk_ai_systems/
       image: /images/knowledge_base/Inventarisation Netherlands Algorithm Registry.png
       date: 26-08-2025
       type: review
-    - title: Local-only tools for AI validation
-      intro: >
-        Slides explaining the concept of 'local-only' tools. Highlighting
-        similarities and differences with cloud computing, including examples
-        how Algorithm Audit's open source software can be used for unsupervised
-        bias detection and synthetic data generation tool.
-      link: /technical-tools/bdt/#local-only
-      image: /images/BDT/20250605_carrousel_local-only.png
-      date: 05-06-2025
-      type: open source code
   featured_button_text: More items
   featured_button_link: /knowledge-platform/knowledge-base/
   items_title: Upcoming events
@@ -102,8 +99,15 @@ Activity_Feed:
       link: >-
         /knowledge-platform/knowledge-base/public_standard_meaningful_human_intervention/
       image: /images/partner logo-cropped/NPD.png
-      date: 30-10-2025
+      date: 31-10-2025
       type: presentation
+    - title: >-
+        Panel discussion 'Statistics: burden or solution for efficient supervision on algorithms and AI?', Dutch Journal for Supervision and network of competent authorities VIDE
+      link: >-
+        https://www.videnet.nl/activiteiten/detail/157/seminar-toezicht-op-emerging-technologies/schedule
+      image: /images/partner logo-cropped/Vide.png
+      date: 31-10-2025
+      type: panel discussion
   items_button_text: More events
   items_button_link: /events/activities/
 Areas_of_AI_expertise:

content/english/_index.md

@@ -1,11 +1,14 @@
 ---
+layout: repository
 title: Algoprudence repository
-subtitle: "Stakeholders learn from our\_techno-ethical jurisprudence, can help to improve it and can use it as to resolve ethical issues in a harmonized manner.\n\nWe are open to new cases. Please <span style=\"color:#005aa7\">[submit</span>](/algoprudence/submit-a-case/) a case for review.\n\nOr read our <span style=\"color:#005aa7\">[white paper</span>](/knowledge-platform/knowledge-base/white_paper_algoprudence/) on algoprudence.\n"
+subtitle: "Stakeholders learn from our case law for algorithms (_algoprudence_), can help to improve it and can use it to resolve ethical issues in a harmonized manner when deploying algorithmic systems"
 image: /images/svg-illustrations/case_repository.svg
 facet_groups:
   - value: year
     title: Year
     facets:
+      - value: "2025"
+        label: "2025"
       - value: "2024"
         label: "2024"
       - value: "2023"
@@ -69,6 +72,51 @@ facet_groups:
         label: Algorithm Audit
 title_content: Case repository
 algoprudences:
+  - title: Predicting irresponsible driving
+    intro: >-
+      Case study how irresponsible driving can be identified and predicted in the database of a car sharing platform. An independent commission issues advice on among others model validity, balancing false positives and false negatives and meaningful transparency.
+    image: /images/algoprudence/AA202501/AA202501P_EN.png
+    link: /algoprudence/cases/aa202501_predicting-irresponsible-driving-behavior/
+    facets:
+      - value: AA202501
+        label: "TA:AA:2025:01"
+      - value: year_2025
+        label: "2025"
+        hide: true
+      - value: type_of_audit_normative
+        label: normative review
+      - value: type_of_algorithm_ml
+        label: machine learning
+      - value: type_of_algorithm_profiling
+        label: profiling
+      - value: ethical_issue_fp_fn_balancing
+        label: FP-FN balancing
+      - value: owner_private
+        label: car sharing platform
+      - value: standard_risk_management
+        label: risk management
+        hide: true
+      - value: standard_governance_data_quality
+        label: governance & data quality
+        hide: true
+      - value: standard_transparency_provisions
+        label: transparancy provisions
+        hide: true
+      - value: standard_human_oversight
+        label: human oversight
+        hide: true
+      - value: standard_quality_management_system
+        label: quality management
+        hide: true
+      - value: standard_accuracy_specifications
+        label: Accuracy specifications
+        hide: true
+      - value: standard_robustness_specifications
+        label: Robustness specifications
+        hide: true
+      - value: standard_quality_management_system
+        label: Quality management system
+        hide: true
   - title: Addendum Preventing prejudice
     intro: >-
       Further research into CUB process of Education Executive Agency of The
@@ -92,9 +140,9 @@ algoprudences:
       - value: ethical_issue_proxy
         label: proxy discrimination
       - value: owner_public
-        label: public organisation
+        label: DUO
       - value: standard_risk_management
-        label: risk mmanagement
+        label: risk management
         hide: true
       - value: standard_governance_data_quality
         label: governance & data quality
@@ -132,7 +180,7 @@ algoprudences:
       - value: ethical_issue_proxy
         label: proxy discrimination
       - value: owner_public
-        label: public organisation
+        label: DUO
       - value: standard_risk_management
         label: risk management
         hide: true
@@ -150,8 +198,8 @@ algoprudences:
         hide: true
   - title: Risk Profiling for Social Welfare Reexamination
     intro: >-
-      The commission judges that algorithmic risk profiling can be used under
-      strict conditions for sampling residents receiving social welfare for
+      Case study to ML-driven risk predictions on unduly granted social welfare. An independent commission judges that algorithmic risk profiling can be used under
+      strict conditions for sampling residents for
       re-examination. The aim of re-examination is a leading factor in judging
       profiling criteria.
     image: /images/algoprudence/AA202302/AA202302A_cover_EN.png
@@ -170,11 +218,11 @@ algoprudences:
       - value: type_of_algorithm_ml
         label: ML
       - value: type_of_algorithm_high_risk_AI
-        label: high-risk AI
+        label: high-risk AI system
       - value: ethical_issue_proxy
         label: proxy discrimination
       - value: owner_public
-        label: public organisation
+        label: Municipality of Rotterdam
       - value: standard_risk_management
         label: risk management
         hide: true
@@ -198,10 +246,7 @@ algoprudences:
         hide: true
   - title: BERT-based disinformation classifier
     intro: >-
-      The audit commission believes there is a low risk of (higher-dimensional)
-      proxy discrimination by the BERT-based disinformation classifier and that
-      the particular difference in treatment identified by the quantitative bias
-      scan can be justified, if certain conditions apply.
+      Case study to algorithmic detection on fake news on Twitter. An independent advice commission believes there is a low risk of proxy discrimination by the BERT-based disinformation classifier and that the particular difference in treatment identified by the quantitative bias scan can be justified, if certain conditions apply.
     image: /images/algoprudence/AA202301/Cover.png
     link: /algoprudence/cases/aa202301_bert-based-disinformation-classifier
     facets:
@@ -222,6 +267,7 @@ algoprudences:
         label: FP-FN balancing
       - value: owner_self
         label: Algorithm Audit
+        hide: true
       - value: disinformation
         label: disinformation
       - value: standard_risk_management
@@ -235,12 +281,8 @@ algoprudences:
         hide: true
   - title: Type of SIM card as a predictor variable to detect payment fraud
     intro: >-
-      The audit commission advises against using type of SIM card as an input
-      variable in algorithmic models that predict payment defaults and block
-      afterpay services for specific customers. As it is likely that type of SIM
-      card acts as a proxy-variable for sensitive demographic categories, the
-      model would run an intolerable risk of disproportionally excluding
-      vulnerable demographic groups from the payment service.
+      Case study to ML-driven risk profiling to detect after-pay fraud at an e-commerce platform. An independent commission advises against using type of SIM card as an input variable in the algorithmic risk model. As it is likely that type of SIM
+      card acts as a proxy-variable for sensitive demographic categories, the model would run an intolerable risk of disproportionally excluding vulnerable demographic groups from the payment service.
     image: /images/algoprudence/AA202201/Cover.png
     link: /algoprudence/cases/aa202201_type-of-sim
     facets:
@@ -268,5 +310,4 @@ algoprudences:
       - value: standard_transparency_provisions
         label: transparency provisions
         hide: true
-layout: repository
 ---

content/english/algoprudence/case-repository.md

@@ -0,0 +1,122 @@
+---
+layout: case
+title: Predicting irresponsible driving
+subtitle: |
+  Problem statement (ALGO:AA:2025:01:P) and advice document (ALGO:AA:2025:01:A)
+image: /images/algoprudence/AA202501/Cover_EN.png
+dynamic_form_engine:
+  - title: React to this normative judgement
+    content: >-
+      Your reaction will be sent to the team maintaining algoprudence. A team will
+      review your response and, if it complies with the guidelines, it will be
+      placed in the Discussion & debate section above.
+    id: case-reaction
+    icon: fas fa-align-justify
+    section:
+      - questions:
+          - title: |
+              Name
+            identifier: name
+            required: true
+            type: text
+          - title: |
+              Affiliated organization
+            identifier: affiliated-organization
+            type: text
+          - title: |
+              Reaction
+            identifier: reaction
+            required: true
+            type: textarea
+          - title: |
+              Contact details
+            identifier: contact-details
+            required: true
+            type: text
+            placeholder: Mail address
+    complete_form_options:
+      type: submit
+      button_text: Submit
+      backend_link: "https://formspree.io/f/xyyrjyzr"
+---
+
+{{< tab_header width="4" tab1_id="description" tab1_title="Description of algoprudence" tab2_id="actions" tab2_title="Actions following algoprudence" tab3_id="discussion" tab3_title="Discussion & debate" default_tab="description" >}}
+
+{{< tab_content_open icon="fa-car" title="Predicting irresponsible driving" id="description" >}}
+
+#### Algoprudence identification code
+
+ALGO:AA:2025:01
+
+#### Key takeaways normative advice commission
+
+- <span style="color:#005aa7; font-weight:600;">Model validity is fundamental</span>\
+  The algorithm must be altered to specifically predict driving behavior that causes damage, not general platform misuse. As for any risk prediction model, getting alignment between training data and intended purpose is a critical prerequisite.
+- <span style="color:#005aa7; font-weight:600;">Balance monitoring with user autonomy</span>\
+  Monitoring irresponsible driving to reduce damage costs is a legitimate business interest but must not become excessive surveillance or veer into paternalistic advice about general driving habits.
+- <span style="color:#005aa7; font-weight:600;">Meaningful transparency required</span>\
+  Users need specific explanations about what driving behavior triggered the warning and clear guidance for improvement, not generic warnings or confusing technical jargon that means nothing to the average driver.
+- <span style="color:#005aa7; font-weight:600;">Careful variable selection</span>\
+  Speeding has obvious safety implications, but acceleration and similar variables are trickier. They depend on context and may just reflect personal driving preferences. Before including them, there must be solid evidence linking them to actual damage risk, not just different driving styles or environments.
+- <span style="color:#005aa7; font-weight:600;">Human oversight essential</span>\
+Human analysts currently override 50-60% of the model’s recommendations, demonstrating real discretion rather than rubber-stamping. This meaningful human oversight must continue.
+
+#### Summary advice
+
+The commission judges that algorithmic risk prediction for identifying irresponsible driving behavior should
+take place under strict conditions and should be weighed against alternative methods of reducing damage.
+The validity of the prediction model is a critical prerequisite, and hence the current mismatch between the
+stated objective (predicting irresponsible driving) and the target variable in training (user bans for a wide variety
+of misuse) must first be resolved. The commission emphasizes that while monitoring to reduce damage cost
+may be a legitimate business interest, it should not become excessive surveillance or be used for paternalistic
+feedback on users’ general driving style. Users should receive specific, meaningful explanations about which
+driving behaviors triggered warnings, not generic notifications or lists of technical variables that users cannot
+comprehend. Variable selection must be carefully justified, with speeding as the most legitimate variable,
+while contextual behaviors like fast acceleration or hard braking require attention to driving context and solid
+evidence in what sense they are related to damage risk. The commission recommends maintaining substantial
+human review of algorithmic recommendations, to mitigate the risk that warnings are unduly sent and to
+facilitate appeal and redress by users.
+
+#### Source of case
+
+Collaboration with car sharing platform. Both the commission and Algorithm Audit have conducted this
+study independently from the car sharing platform. Neither the investigation nor the advice have been commissioned or funded by the platform.
+
+#### Presentation
+
+This case study was published during UNESCO's Expert roundtable II: Capacity building for AI supervisory authorities in Paris on September 30, 2025. 
+
+<!-- {{< image id="presentation-minister" image1="/images/algoprudence/AA202302/Algorithm audit presentatie BZK FB-18.jpg" alt1="Presentation advice report to Dutch Minister of Digitalization" caption1="Presentation advice report to Dutch Minister of Digitalization" width_desktop="5" width_mobile="12" >}} -->
+
+#### Problem statement and advice document
+
+{{< embed_pdf url="/pdf-files/algoprudence/ALGO_AA202501/EN/Problem statement Predicting irresponsible driving.pdf" url2="/pdf-files/algoprudence/ALGO_AA202501/EN/Advice Predicting irresponsible driving.pdf" >}}
+
+#### Normative advice commission
+
+- Cynthia Liem, Associate Professor at the Multimedia Computing Group, TU Delft
+- Hilde Weerts, Assistant Professor Fair and Explainable Machine Learning, TU Eindhoven
+- Joris Krijger, AI & Ethics Officer, De Volksbank
+- Maaike Harbers, Professor of Applied Sciences (lector) Artificial Intelligence & Society, Rotterdam University of Applied Sciences
+- Monique Steijns, Founder The People’s AI agency
+- Anne Rijlaarsdam, user car sharing platform.
+
+{{< tab_content_close >}}
+
+{{< tab_content_open icon="" title="" id="actions" >}}
+
+{{< accordions_area_open id="actions" >}}
+
+{{< accordions_area_close >}}
+
+{{< tab_content_close >}}
+
+{{< tab_content_open id="discussion" >}}
+
+{{< accordions_area_open id="discussion" >}}
+
+{{< accordions_area_close >}}
+
+{{< tab_content_close >}}
+
+{{< dynamic_form_engine index="0" >}}

content/english/algoprudence/cases/aa202501_predicting-irresponsible-driving-behavior.md

@@ -55,6 +55,13 @@ overview_block:
 Activity_Feed:
   featured_title: Uitgelicht
   featured_activities:
+    - title: 'Algoprudentie: Voorspellen van onverantwoord rijgedrag'
+      intro: >
+        Case study naar het voorspellen van onverantwoord rijgedrag op basis van de data van een autodeelplatform.
+      link: /nl/algoprudence/cases/aa202501_predicting-irresponsible-driving-behavior/
+      image: /images/algoprudence/AA202501/Cover_NL.png
+      date: 30-09-2025
+      type: algoprudence
     - title: Inventarisatie 14 ministries Algoritmeregister
       intro: >
         Afgelopen zomer hebben 14 Nederlandse ministeries hun inventarisaties
@@ -64,17 +71,6 @@ Activity_Feed:
       image: /images/knowledge_base/Inventarisatie Algoritmeregister.png
       date: 26-08-2025
       type: onderzoek
-    - title: Local-only tools voor AI validatie
-      intro: >
-        Slides die het concept van 'local-only' tools uitleggen. Verschillen en
-        overeenkomsten met cloud computing worden toegelicht, inclusief
-        voorbeelden hoe Algorithm Audit’s open source software gebruikt kan
-        worden gebruikt voor usnupervised bias detectie en synthetische
-        datageneratie.
-      link: /nl/technical-tools/bdt/#local-only
-      image: /images/BDT/20250605_carrousel_local-only.png
-      date: 05-06-2025
-      type: open source code
   featured_button_text: Meer items
   featured_button_link: /nl/knowledge-platform/knowledge-base/
   items_title: Aankomende events
@@ -104,8 +100,15 @@ Activity_Feed:
       link: >-
         /nl/knowledge-platform/knowledge-base/public_standard_meaningful_human_intervention/
       image: /images/partner logo-cropped/NPD.png
-      date: 30-10-2025
+      date: 31-10-2025
       type: presentatie
+    - title: >-
+        Paneldiscussie ‘Statistiek: kwaal of wondermiddel voor effectief toezicht op algoritmes en AI?’, Tijdschrift voor Toezicht en professional association VIDE
+      link: >-
+        https://www.videnet.nl/activiteiten/detail/157/seminar-toezicht-op-emerging-technologies/schedule
+      image: /images/partner logo-cropped/Vide.png
+      date: 31-10-2025
+      type: paneldiscussion
   items_button_text: Meer events
   items_button_link: /nl/events/activities/
 Areas_of_AI_expertise: