From Algorithmic Transparency to Algorithmic Choice: European Perspectives on Recommender Systems and Platform Regulation

Author:

Busch Christoph

Abstract

AbstractAlgorithmic recommendations and rankings have become a key feature of the user experience offered by digital platforms. Recommender systems determine which information and options are prominently presented to users. While there is abundant technical literature on recommender systems, the topic has only recently attracted the attention of the European legislator. This chapter scrutinizes the emerging European regulatory framework for algorithmic rankings and recommendations in the platform economy with a specific focus on online retail platforms. Surveying the new rules for rankings and recommender systems in consumer contract law, unfair commercial practices law, and platform regulation, it identifies shortcomings and inconsistencies and highlights the need for coherence between the different regulatory regimes. The Digital Services Act could change the regulatory trajectory by introducing (albeit hesitantly and incompletely) a new regulatory model that shifts the focus from algorithmic transparency to algorithmic choice. More importantly, a choice-based approach to recommender governance and a market for third-party recommender systems (“RecommenderTech”) could also be facilitated by the new interoperability requirements introduced by the Digital Markets Act.

Publisher

Springer International Publishing

Reference36 articles.

1. Aggarwal, C.C. 2016. Recommender Systems. Cham: Springer. https://doi.org/10.1007/978-3-319-29659-3.

2. Airbnb Ireland. 2022. How search results work. https://www.airbnb.ie/help/article/39/how-search-results-work. Accessed 13 Feb 2022.

3. Airbnb UK. 2022. How Airbnb search works. https://www.airbnb.co.uk/resources/hosting-homes/a/how-airbnb-search-works-44. Accessed 13 Feb 2022.

4. Alexander, C. 2019. Neue Transparenzanforderungen im Internet – Ergänzungen der UGP-RL durch den “New Deal for Consumers”. Wettbewerb in Recht und Praxis 10: 1235–1241.

5. Banker, S., and S. Khetani. 2019. Algorithm Overdependence: How the Use of Algorithmic Recommendation Systems Can Increase Risks to Consumer Well-Being. Journal of Public Policy & Marketing 38 (4): 500–515.

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