added terms and conditions to all forms

jfparie · jfparie · commit a4238a9c6e8f · 2024-03-17T14:14:12.000+01:00
diff --git a/content/english/about/vacancies.md b/content/english/about/vacancies.md
@@ -12,6 +12,7 @@ form1:
     - label: |
         Name
       id: name
+      required: true
       type: text
     - label: |
         Contact details
@@ -49,9 +50,11 @@ form1:
       file_type: '.docx, .pdf'
       type: file
     - label: >
-        <a href="/privacy_policy/" target="_blank">Terms and conditions
-        (link)</a>
-      id: terms
+        Terms and conditions <br> <span style="font-size:12px;
+        color=#777;">Submitted data will only be processed for the purpose
+        described above, kept for the minimum necessary duration, and is
+        securely stored in a protected environment</span>
+      id: terms-conditions
       values:
         - label: Agree
           value: agree
diff --git a/content/english/algoprudence/submit-a-case.md b/content/english/algoprudence/submit-a-case.md
@@ -1,47 +1,11 @@
 ---
-content: |
-  Helloi Test
-  {{< team >}}
 title: Submit a case
 subtitle: >
   Algorithm Audit conducts solicited and unsolicited audits. Fill in the below
   form to provide us with preliminary information required to review your
   algorithm. Or submit a carefully documented normative judgement for inlcusion
   in our case repository.
 image: /images/svg-illustrations/case_repository.svg
-form:
-  title: Submit a case
-  button_text: Send
-  backend_link: 'https://formspree.io/f/xzbnrlan'
-  id: submit-a-case
-  questions:
-    - label: Name of the algorithm
-      id: name
-      type: text
-    - label: >-
-        Short description – Define the specific task of the algorithm and its
-        the context in which it operates (max. 200 words)
-      id: description
-      type: textarea
-    - label: >-
-        Technical dimension – Description of data collection, used statistical
-        methodologies and used evaluation criteria 
-      id: dimensions
-      type: textarea
-    - label: >-
-        Legal framework – Applicable laws and open legal norms, e.g., GDPR, EU
-        non-discrimination law 
-      id: legal-framework
-      type: textarea
-    - label: >-
-        Ethical issues – Description of the identified ethical issue given it's
-        technical and legal framework
-      id: ethical-issues
-      type: textarea
-    - label: Contact details
-      id: contact-details
-      type: email
-      placeholder: Email address
 team:
   title: Team Algoprudence
   icon: fas fa-user-friends
@@ -96,38 +60,52 @@ form1:
   backend_link: 'https://formspree.io/f/xzbnrlan'
   id: submit-a-case
   questions:
-    - label: Name algorithm
+    - label: |
+        Name algorithm
       id: name
       required: true
       type: text
-    - label: >-
+    - label: >
         Short description – Define the specific task of the algorithm and its
         the context in which it operates (max. 200 words)
       id: description
       required: true
       type: textarea
-    - label: >-
+    - label: >
         Technical dimension – Description of data collection, used statistical
         methodologies and used evaluation criteria
       id: technical-dimension
       type: textarea
-    - label: >-
+    - label: >
         Legal framework – Applicable laws and open legal norms, e.g., GDPR, EU
         non-discrimination law
       id: legal-framework
       required: false
       type: textarea
-    - label: >-
+    - label: >
         Ethical issues – Description of the identified ethical issue given it’s
         technical and legal framework
       id: ethical-issue
       required: true
       type: textarea
-    - label: Contact details
+    - label: |
+        Contact details
       id: contact-details
       required: false
       type: email
       placeholder: Mail address
+    - label: >
+        Terms and conditions <br> <span style="font-size:12px;
+        color=#777;">Submitted data will only be processed for the purpose
+        described above, kept for the minimum necessary duration, and is
+        securely stored in a protected environment</span>
+      id: terms-conditions
+      values:
+        - label: Agree
+          value: agree
+          id: agree
+      required: true
+      type: checkbox
 form2:
   title: Case information
   content: ''
@@ -158,6 +136,18 @@ form2:
       file_type: .pdf
       type: file
       placeholder: ''
+    - label: >
+        Terms and conditions <br> <span style="font-size:12px;
+        color=#777;">Submitted data will only be processed for the purpose
+        described above, kept for the minimum necessary duration, and is
+        securely stored in a protected environment</span>
+      id: terms-conditions
+      values:
+        - label: Agree
+          value: agree
+          id: agree
+      required: true
+      type: checkbox
 ---
 
 {{< tab_header width="6" tab1_id="case-for-review" default_tab="case-for-review" tab1_title="Submit a case for normative review" tab2_id="case-repository" tab2_title="Add a case to algoprudence repository" >}}
diff --git a/content/english/technical-tools/BDT.md b/content/english/technical-tools/BDT.md
@@ -52,22 +52,24 @@ text_field5:
   id: faq
   content: "##### Why this bias detection tool?\n\n* No data needed on protected attributes of users (unsupervised bias detection);\n* Model-agnostic (AI binary classifiers only);\n* Connecting quantitative tools with qualitative methods to assess fair AI;\n* Developed open-source and not-for-profit.\n\n##### By whom can the bias detection tool be used?\_\n\nThe bias detection tool allows the entire ecosystem involved in auditing AI, e.g., data scientists, journalists, policy makers, public- and private auditors, to use quantitative methods to detect bias in AI systems.\n\n##### What does the tool compute?\_\n\nA statistical method is used to compute which clusters are relatively often misclassified by an AI system. A cluster is a group of data points sharing similar features. The tool returns a report in which identified differences (between feature means) are visualized and statistical significant feature differences are tested (Welch’s two-samples t-test for unequal variances).\n\n##### The tool detects prohibited discrimination in AI?\_\n\nNo. The bias detection tool serves as a starting point to assess potentially unfair AI classifiers with the help of subject-matter expertise. The features of identified clusters are examined on critical links with protected grounds, and whether the measured disparities are legitimate. This is a qualitative assessment for which the context-sensitive legal doctrine provides guidelines, i.e., to assess the legitimacy of the aim pursued and whether the means of achieving that aim are\_appropriate\_and\_necessary.\n\n##### For what type of AI does the tool work?\_\n\nCurrently, only\_binary classification\_algorithms can be reviewed. For instance, prediction of loan approval (yes/no), disinformation detection (true/false) or disease detection (positive/negative).\n\n##### What happens with my data?\n\nYour .csv file is uploaded to a AWS bucket, where it is processed. Once the clustering algorithm is finised the data is immediately deleted.\n\n##### &#xA;In sum\_\n\nQuantitative methods, such as unsupervised bias detection, are helpful to discover potentially unfair treated groups of similar users in AI systems in a scalable manner. Automated identification of cluster disparities in AI models allows human experts to assess observed disparities in a qualitative manner, subject to political, social and environmental traits. This two-pronged approach bridges the gap between the qualitative requirements of law and ethics, and the quantitative nature of AI (see figure). In making normative advice, on identified ethical issues publicly available, over time a repository of 'techno-ethical jurisprudence' emerges; from which data scientists and public authorities can distill best practices to build fairer AI (see our\_case reviews).\_\n"
 reports_preview:
-  title: Example reports
+  title: Example output bias detection tool
   icon: fas fa-file
-  button_text: ''
-  button_link: ''
+  button_text: Case repository
+  button_link: /algoprudence
   id: example-reports
   feature_item:
+    - name: Normative judgement commission
+      image: /images/algoprudence/AA202301/Cover.png
+      link: /algoprudence/cases/aa202301_bert-based-disinformation-classifier/
+      content: >
+        An advice commission believes there is a low risk of
+        (higher-dimensional) proxy discrimination by the BERT-based
+        disinformation classifier
     - name: FPR clustering results
       image: /images/BDT/Example_report.png
       link: >-
         https://static-files-pdf.s3.amazonaws.com/bias_scan_FPR_test_pred_BERT.pdf
       content: "An example report for the\_[BERT-based disinformation detection (FPR) case study](https://static-files-pdf.s3.amazonaws.com/bias_scan_FPR_test_pred_BERT.pdf)\n"
-    - name: FNR clustering results
-      image: /images/BDT/Example_report.png
-      link: >-
-        https://static-files-pdf.s3.amazonaws.com/bias_scan_FPR_test_pred_BERT.pdf
-      content: "An example report for the\_[BERT-based disinformation detection (FNR) case study](https://static-files-pdf.s3.amazonaws.com/bias_scan_FNR_test_pred_BERT.pdf)\n"
 team:
   title: Bias Detection Tool Team
   icon: fas fa-user-friends
@@ -117,6 +119,8 @@ Try the tool below ⬇️
 
 {{< promo_bar content="Do you appreciate the work of Algorithm Audit? ⭐️ us on" id="promo" >}}
 
+{{< reports_preview >}}
+
 {{< container_open title="Finalist Stanford’s AI Audit Challenge 2023" icon="fas fa-medal" id="finalist" >}}
 
 Under the name Joint Fairness Assessment Method (JFAM) our bias scan tool has been selected as a finalist in [Stanford’s AI Audit Competition 2023](https://hai.stanford.edu/ai-audit-challenge-2023-finalists).
@@ -154,8 +158,6 @@ What input does the bias scan tool need? A csv file of max. 5GB with feature col
 
 {{< container_close >}}
 
-{{< reports_preview >}}
-
 {{< container_open title="FAQ" icon="fas fa-question-circle" >}}
 
 ##### Why this bias detection tool?
diff --git a/content/nederlands/about/vacancies.md b/content/nederlands/about/vacancies.md
@@ -12,6 +12,7 @@ form1:
     - label: |
         Name
       id: name
+      required: true
       type: text
     - label: |
         Contact details
@@ -49,9 +50,11 @@ form1:
       file_type: '.docx, .pdf'
       type: file
     - label: >
-        <a href="/privacy_policy/" target="_blank">Terms and conditions
-        (link)</a>
-      id: terms
+        Terms and conditions <br> <span style="font-size:12px;
+        color=#777;">Submitted data will only be processed for the purpose
+        described above, kept for the minimum necessary duration, and is
+        securely stored in a protected environment</span>
+      id: terms-conditions
       values:
         - label: Agree
           value: agree
diff --git a/content/nederlands/algoprudence/submit-a-case.md b/content/nederlands/algoprudence/submit-a-case.md
@@ -30,37 +30,6 @@ team:
       name: Samaa Mohammad-Ulenberg
       bio: |
         Bestuurder
-form:
-  title: Dien een case in
-  button_text: Verstuur
-  backend_link: 'https://formspree.io/f/xzbnrlan'
-  questions:
-    - label: Naam algoritme
-      id: naam
-      type: text
-    - label: Korte beschrijving van algoritme (max. 100 woorden)
-      id: beschrijving
-      type: textarea
-    - label: >-
-        Technische aspecten – Beschrijving van o.a. verzamelde data, gehanteerde
-        statistische methode en gebruikte evaluatie criteria
-      id: technische-dimensie
-      type: textarea
-    - label: >-
-        Juridisch kader – Relevante wet- en regelgeving, beschrijving van open
-        juridische normen, bijvoorbeeld in de Algemene Verordening
-        Gegevensbescherming (AVG) of de Algemene Wet Gelijke Behandeling (AWGB)
-      id: juridische-dimensie
-      type: textarea
-    - label: >-
-        Ethische aspecten – Beschrijving van geïdentificeerd ethische kwesties
-        gegeven de technische en juridische achtergrond
-      id: ethische-dimensie
-      type: textarea
-    - label: Contactgegevens
-      id: contactgegevens
-      type: email
-      placeholder: Emailadres
 reports_preview:
   title: Recente audits
   icon: fas fa-file
@@ -118,6 +87,18 @@ form1:
       id: reaction
       type: email
       placeholder: Emailadres
+    - label: >
+        Voorwaarden <br> <span style="font-size:12px;
+        color=#777;">Verstrekte gegevens worden alleen verwerkt voor het hierboven beschreven doel,
+        de gegevens worden niet langer opgeslagen dan strikt noodzakelijk en worden opgeslagen in
+        een beschermde omgeving</span>
+      id: terms-conditions
+      values:
+        - label: Agree
+          value: agree
+          id: agree
+      required: true
+      type: checkbox
 form2:
   title: Informatie over het oordeel
   content: ''
@@ -143,6 +124,18 @@ form2:
       id: documents
       file_upload_text: Kies bestand
       type: file
+    - label: >
+        Voorwaarden <br> <span style="font-size:12px;
+        color=#777;">Verstrekte gegevens worden alleen verwerkt voor het hierboven beschreven doel,
+        de gegevens worden niet langer opgeslagen dan strikt noodzakelijk en worden opgeslagen in
+        een beschermde omgeving</span>
+      id: terms-conditions
+      values:
+        - label: Agree
+          value: agree
+          id: agree
+      required: true
+      type: checkbox
 ---
 
 {{< tab_header width="6" tab1_id="case-for-review" default_tab="case-for-review" tab1_title="Dien een case in voor beoordeling" tab2_id="case-repository" tab2_title="Voeg oordeel toe aan algoprudentie uitspraken" >}}
diff --git a/content/nederlands/technical-tools/BDT.md b/content/nederlands/technical-tools/BDT.md
@@ -16,6 +16,25 @@ web_app:
   icon: fas fa-cloud
   id: web-app
   content: ''
+reports_preview:
+  title: Voorbeeld output bias detectie tool
+  icon: fas fa-file
+  button_text: Overzicht casuïstiek
+  button_link: /nl/algoprudence
+  id: example-reports
+  feature_item:
+    - name: Normatief oordeel commissie
+      image: /images/algoprudence/AA202301/Cover.png
+      link: /algoprudence/cases/aa202301_bert-based-disinformation-classifier/
+      content: >
+        Adviescommissie oordeelt dat er een laag risico is op
+        (hoger-dimensionale) proxydiscriminatie bij gebruik van de
+        BERT-gebaseerde desinformatie detectie-algoritme
+    - name: FPR clustering resultaten
+      image: /images/BDT/Example_report.png
+      link: >-
+        https://static-files-pdf.s3.amazonaws.com/bias_scan_FPR_test_pred_BERT.pdf
+      content: "Voorbeeld van automatisch gegenereerde biasgegevens over \_[BERT-gebaseerde desinformatie detectie-algoritme (FPR) case study](https://static-files-pdf.s3.amazonaws.com/bias_scan_FPR_test_pred_BERT.pdf)\n"
 team:
   title: Bias Detectie Tool Team
   icon: fas fa-user-friends
@@ -59,6 +78,8 @@ Gebruik de tool hieronder ⬇️
 
 {{< promo_bar content="Waardeer je het werk van Algorithm Audit? ⭐️ ons op" id="promo" >}}
 
+{{< reports_preview >}}
+
 {{< container_open title="Finalist Stanford’s AI Audit Challenge 2023" icon="fas fa-medal" id="finalist" >}}
 
 Met de inzending Joint Fairness Assessment Method (JFAM) is Algorithm Audit's bias detectie tool geselecteerd als finalist voor [Stanford’s AI Audit Competition 2023](https://hai.stanford.edu/ai-audit-challenge-2023-finalists).
@@ -96,8 +117,6 @@ Welke input data kan de bias detectie tool verwerken? Een csv-bestand van maxima
 
 {{< container_close >}}
 
-{{< reports_preview >}}
-
 {{< container_open title="Veelgestelde vragen" icon="fas fa-question-circle" >}}
 
 ##### Waarom deze bias detectie tool?
