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javascript structural fix in main.js and page_header.js, and AI policy observatory update
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assets/scss/templates/_main.scss

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li {
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font-family: avenir, sans-serif;
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font-size: 15px;
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line-height: 1.7;
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}
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.rounded-blue-border {

content/.DS_Store

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content/english/algoprudence/cases/aa202302_risk-profiling-for-social-welfare-reexamination.md

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title: React to this normative judgement
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content: >-
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Your reaction will be sent to the team maintaining algoprudence. A team will
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review your response and, if it complies with the guidelines, it will be placed in the Discussion & debate section
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above.
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review your response and, if it complies with the guidelines, it will be
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placed in the Discussion & debate section above.
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button_text: Submit
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backend_link: 'https://formspree.io/f/xyyrjyzr'
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id: case-reaction
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questions:
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- label: Name
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- label: |
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Name
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id: name
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required: true
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type: text
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- label: Affiliated organization
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- label: |
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Affiliated organization
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id: affiliated-organization
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type: text
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- label: Reaction
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- label: |
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Reaction
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id: reaction
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required: true
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type: textarea
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- label: Contact details
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- label: |
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Contact details
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id: contact-details
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required: true
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type: text
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* explainability requirements for the used explainable boosting algorithm
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* implications of the AIAct for this particular form of risk profiling.
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{{< pdf_frame articleUrl1="https://drive.google.com/file/d/1oPiO_s9KuV7446BqC9a4P2qjW7MGUyxu/preview" >}}
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{{< accordion_item_close >}}
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{{< accordion_item_open title="Binnenlands Bestuur" image="/images/algoprudence/AA202302/Actions/logo-bb.svg" id="binnenlands-bestuur" date="01-12-2023" tag1="news" >}}

content/english/events/activities.md

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CEN-CENELEC Dublin
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image: /images/events/jtc21.jpg
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date: 13-02-2024
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pdf: /pdf-files/20240213_JTC21_plenary_FRIAs_stakeholder_panels.pdf
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facets:
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- value: type_presentation
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label: Presentation
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label: presentation
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- value: year_q1_2024
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label: Q1-2024
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hide: true
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label: Q1-2024
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- value: type_panel
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label: Panel discussion
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label: panel discussion
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- title: University of Groningen (RUG) AI Act event
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description: |
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Sharing buttom-up experience on auditing AI during panel discussion.
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label: Q1-2024
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hide: true
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- value: type_panel
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label: Panel discussion
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label: panel discussion
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- title: >-
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Presentation Bias Detection Tool study association Christiaan Huygens TU
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Delft
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Delft
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image: /images/events/mathematics and computer science tu delft.png
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date: 08-01-2024
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pdf: /pdf-files/20240108_BDT_TU_Delft.pdf
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facets:
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label: Presentation
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label: presentation
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- title: >-
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Presentation on inclusive, deliberative stakeholder panels – Working Group
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1 Inclusiveness JTC21 CEN-CENELEC
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- value: type_presentation
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label: Presentation
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label: presentation
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label: Q1-2024
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pdf: ''
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label: event
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hide: false
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- value: year_q3_2023
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content/english/knowledge-platform/knowledge-base/Markdown_template.md

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---
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title: White paper – Feedback on DSA Delegated Regulation (conducting independent audits)
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title: >-
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Algorithm Audit (white paper) – Feedback on DSA Delegated Regulation (conducting independent
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audits)
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author: Algorithm Audit
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image: /images/knowledge_base/white-paper-3.png
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type: featured
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summary: 'Plea to include the normative dimension of AI auditing in delegated regulation of the Digital Services Act (DSA). Current limitations are illustrated by focussing on a recommender systems example'
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type: regular
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summary: >-
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Plea to include the normative dimension of AI auditing in delegated regulation
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of the Digital Services Act (DSA). Current limitations are illustrated by
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focussing on a recommender systems example
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subtitle: ''
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---
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Feedback to the European Commission on DSA Delegated Regulation – conducting independent audits.
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Feedback to the European Commission on DSA Delegated Regulation – conducting independent audits.
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###### **Summary**
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In addition to Article 37 of the Digital Services Act (DSA), Delegated Regulation (DR) sets out procedures, methodologies and templates for third-party auditing of Very Large Open Platforms (VLOPs) and Very Large Online Search Engines (VLOSEs). The DR builds upon established sector-specific risk management frameworks to provide procedural guidance for AI audits. However, the regulation lacks provisions to disclose normative methodological choices that underlie AI systems (e.g., recommender systems), which is crucial for evaluating associated risks in a meaningful way (as mandated by DSA Article 34). To illustrate this limitation, we elaborate on methodological crossroads that determine the performance of recommender systems and its downstream risks. We make concrete suggestions how the definition of ‘inherent risk’ (Article 2), audit methodologies of risk assessments (Section IV) and the audit report template (Annex I) set out by the DR should be amended to incorporate normative dimension of AI auditing in a meaningful way. Only if both the technical and normative dimension of AI systems are thoroughly examined, risk assessed under the DSA will empower the European Union and its citizens to determine what public values should to be safeguarded in the digital world.
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{{< pdf_frame title="White-paper" name="white-paper" articleUrl="https://drive.google.com/file/d/1_Raxsm0wDO_0cSVfPLzS9AzCFNviBUaO/preview" >}}
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