Provenance record

Click, Branch, Audit: A Decision-Tree Toolkit for Assessing Fundamental Rights Impacts under the Digital Services Act of Very Large Platforms & Search Engines

arXiv cs.CY (Computers and Society) (tier 1, academic) 2026-09-23T04:00:00.000Z Original ↗

Source note: Preprints on societal impact.


Discourse valence
-15
Mixed or uncertain
confidence 72% · 4 items · range -100 to +20
Adverse readingFavourable reading
Consensus of 4 models from different labs. Spread 120 points, agreement low.
Evaluation and measurementGovernance and regulationConcentration of power
Excerpt as ingested

arXiv:2609.26353v1 Announce Type: new Abstract: The EU Digital Services Act (DSA) requires Very Large Online Platforms and Search Engines (VLOPs/VLOSEs) to assess and mitigate systemic risks, including actual or foreseeable negative effects on fundamental rights, and subjects these assessments to independent audit. Yet how such rights-relevant risks should be identified, assessed, evidenced, and documented remains methodologically under-specified. We address this gap by introducing a decision-tree-based toolkit that operationalizes Article 34(1)(b) DSA and related audit requirements into a structured assessment process. The toolkit decomposes platforms as systems-of-systems and guides assessors through DSA applicability, context definition, subsystem and stakeholder identification, risk identification, risk assessment, reporting, and visualization. A central design contribution is its treatment of vulnerability as situational and relational: rather than assigning vulnerability to fixed groups, the toolkit asks how particular platform subsystems and dependencies may place stakeholders in vulnerable positions. It further makes fundamental rights-relevant judgments traceable by recording the affected right, interference and justification analysis within the structure of a proportionality test, while also covering a perception and impact dimension including severity, scope, duration, reversibility, as well as practical consequences. We developed the toolkit iteratively through a design-science process combining literature-derived design requirements, three interdisciplinary exp

Every model that read this

ModelProviderStageScoreConf.LatencyPromptWhen
Llama 3.3 70BMetaanalysis 0 70%3553ms v1.0.0 / m1.0.1 2026-09-23 05:41
GPT-4.1 miniOpenAIconsensus +20 85%4143ms v1.0.0 / m1.0.1 2026-09-23 05:42
Claude Sonnet 5Anthropicconsensus +18 55%6917ms v1.0.0 / m1.0.1 2026-09-23 05:42
Gemini 2.5 FlashGoogleconsensus -100 80%2227ms v1.0.0 / m1.0.1 2026-09-23 05:42
Llama 3.3 70B · reading

The toolkit aims to support assessments of fundamental rights impacts under the DSA.

evidence: anecdotal horizon: n/a
GPT-4.1 mini · reading

The toolkit aims to enhance transparency and systematic assessment of fundamental rights risks in large digital platforms, which could improve oversight and reduce harm. However, it is a methodological tool and does not directly guarantee improved outcomes. Its impact depends on adoption and enforcement effectiveness.

evidence: reported horizon: near capability 35 societal 25 existential 0 economic 10
Claude Sonnet 5 · reading

This is a methodological governance tool, not evidence of actual societal impact from AI systems. It could modestly support better platform accountability if adopted, but is early-stage and validated only through small workshops, so implications remain speculative and mild.

evidence: reported horizon: n/a
Gemini 2.5 Flash · reading

This toolkit aims to help Very Large Online Platforms and Search Engines comply with the EU Digital Services Act by providing a structured method for assessing fundamental rights impacts. Its purpose is to streamline and increase the transparency and auditability of these assessments, which is a mildly positive development for human rights protection.

evidence: reported horizon: n/a

Evidence extracted

The chain
SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   9aa55f81-77cc-4f64-b72a-355e1356735b
           https://arxiv.org/abs/2609.26353
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EVIDENCE   1 extracted excerpt
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      -15  (Mixed or uncertain)
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CONFIDENCE 72%