Katarzyna Szymielewicz

Breaking the Loop: Exploring User Agency in Escaping Algorithmic Rabbit Holes on Social Media

Early-stage findings from a Panoptykon/Piotr Sapieżyński (Northeastern University) study testing whether Facebook's 'Not interested' feedback tool lets users actually escape unwanted recommended content; results are mixed, with one pilot showing no lasting effect and a later 7-participant study showing a 38% average reduction with wide variance.

Safe by Default (People vs Big Tech briefing)

Argues engagement-based recommender systems amplifying borderline content is a feature not a bug; calls on regulators to require non-profiling feeds as the default.

Algorithms of Trauma 2: 'Anxious about your health? Facebook won't let you forget'

Case study showing Facebook's ad-delivery algorithms push distressing health/illness ads exploiting users' mental vulnerabilities; disabling 'sensitive interests' limits advertiser targeting but not Facebook's own profiling.

Fixing Recommender Systems: From Identification of Risk Factors to Meaningful Transparency and Mitigation

Sets out six hypotheses on how recommender-system design choices (engagement-driven ranking, addictive features, data-driven personalisation) generate DSA Article 34 'systemic risks,' plus a technical checklist of disclosures VLOPs/VLOSEs should make to regulators, auditors and researchers.