From Relevance to Reward: A Pipeline View of Addictive Design in Recommender Systems under the DSA

Ludovico Boratto, Silvia De Conca, Matteo Fabbri, Alessandro Fabris
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:40-44, 2026.

Abstract

Recent EU Commission proceedings against TikTok, Meta, Shein, and Temu have framed addictive design as a systemic risk under the Digital Services Act, yet the connection to recommender systems remains underexplored. This contribution proposes a four-stage analytical framework centered on relevance estimation, session sequencing, objective configuration, and interface and notifications, mapping where addictive effects emerge in the recsys pipeline and how they can be audited. Drawing on the 2025 DSA audit and systemic risk assessment reports of six VLOPs, we identify gaps in current transparency practices, including no disclosure of optimisation objectives and impact-relevant metrics, and derive recommendations for DSA enforcement.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-boratto26a, title = {From Relevance to Reward: A Pipeline View of Addictive Design in Recommender Systems under the DSA}, author = {Boratto, Ludovico and De Conca, Silvia and Fabbri, Matteo and Fabris, Alessandro}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {40--44}, year = {2026}, editor = {De Bie, Tijl and Defrance, MaryBeth and Calders, Toon and Nguyen, Dennis}, volume = {350}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v350/main/assets/boratto26a/boratto26a.pdf}, url = {https://proceedings.mlr.press/v350/boratto26a.html}, abstract = {Recent EU Commission proceedings against TikTok, Meta, Shein, and Temu have framed addictive design as a systemic risk under the Digital Services Act, yet the connection to recommender systems remains underexplored. This contribution proposes a four-stage analytical framework centered on relevance estimation, session sequencing, objective configuration, and interface and notifications, mapping where addictive effects emerge in the recsys pipeline and how they can be audited. Drawing on the 2025 DSA audit and systemic risk assessment reports of six VLOPs, we identify gaps in current transparency practices, including no disclosure of optimisation objectives and impact-relevant metrics, and derive recommendations for DSA enforcement.} }
Endnote
%0 Conference Paper %T From Relevance to Reward: A Pipeline View of Addictive Design in Recommender Systems under the DSA %A Ludovico Boratto %A Silvia De Conca %A Matteo Fabbri %A Alessandro Fabris %B Proceedings of Fifth European Conference on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2026 %E Tijl De Bie %E MaryBeth Defrance %E Toon Calders %E Dennis Nguyen %F pmlr-v350-boratto26a %I PMLR %P 40--44 %U https://proceedings.mlr.press/v350/boratto26a.html %V 350 %X Recent EU Commission proceedings against TikTok, Meta, Shein, and Temu have framed addictive design as a systemic risk under the Digital Services Act, yet the connection to recommender systems remains underexplored. This contribution proposes a four-stage analytical framework centered on relevance estimation, session sequencing, objective configuration, and interface and notifications, mapping where addictive effects emerge in the recsys pipeline and how they can be audited. Drawing on the 2025 DSA audit and systemic risk assessment reports of six VLOPs, we identify gaps in current transparency practices, including no disclosure of optimisation objectives and impact-relevant metrics, and derive recommendations for DSA enforcement.
APA
Boratto, L., De Conca, S., Fabbri, M. & Fabris, A.. (2026). From Relevance to Reward: A Pipeline View of Addictive Design in Recommender Systems under the DSA. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:40-44 Available from https://proceedings.mlr.press/v350/boratto26a.html.

Related Material