Beyond the Checkbox: Strengthening DSA Compliance Through Social Media Algorithmic Auditing

2026-01-26

AI Summary

This study addresses critical gaps in current practices for auditing algorithmic compliance under the Digital Services Act (DSA), which mandates independent audits yet lacks methodological consistency and technical rigor—particularly in verifying key requirements such as protections for minors, transparency of recommender systems, and restrictions on sensitive-data-based advertising. To this end, the paper introduces, for the first time, a behavioral algorithmic auditing framework systematically tailored to DSA compliance verification. By integrating user behavior simulation, algorithmic response tracking, and compliance metric modeling, the proposed approach maps legal provisions to technical evaluations within a scalable audit architecture. Moving beyond static or formalistic checks, this method enables dynamic, empirical scrutiny of AI-driven platform operations, substantially enhancing the depth, scalability, and independence of regulatory compliance assessments.

Technology Category

• Search and Optimization: Algorithm Configuration
• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: AI for Accessibility

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Security and Privacy: Data transparency and provenance

Abstract

Algorithms of online platforms are required under the Digital Services Act (DSA) to comply with specific obligations concerning algorithmic transparency, user protection and privacy. To verify compliance with these requirements, DSA mandates platforms to undergo independent audits. Little is known about current auditing practices and their effectiveness in ensuring such compliance. To this end, we bridge regulatory and technical perspectives by critically examining selected audit reports across three critical algorithmic-related provisions: restrictions on profiling minors, transparency in recommender systems, and limitations on targeted advertising using sensitive data. Our analysis shows significant inconsistencies in methodologies and lack of technical depth when evaluating AI-powered systems. To enhance the depth, scale, and independence of compliance assessments, we propose to employ algorithmic auditing -- a process of behavioural assessment of AI algorithms by means of simulating user behaviour, observing algorithm responses and analysing them for audited phenomena.

Problem

• Digital Services Act
• algorithmic auditing
• compliance
• recommender systems
• targeted advertising

Innovation

• algorithmic auditing
• Digital Services Act (DSA)
• compliance assessment
• recommender systems transparency
• AI accountability