Prediction Accuracy and Autonomy

2022/11/15 by Anton Angwald, Angwald, Anton, Kalle Areskoug +3
Computer Science · #Data Stream Mining Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning and Data Classification #Recommender Systems and Techniques

paper · pdf · doi:10.48550/arxiv.2211.08134

openalex publication_date 2022/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The tech industry has been criticised for designing applications that undermine individuals' autonomy. Recommender systems, in particular, have been identified as a suspected culprit that might exercise unwanted control over peoples' lives. In this article we try to assess the objectives of recommender system research and offer a nuanced discussion of how these objectives can align with users' goals. This discussion employs a qualitative literature survey connecting the dots between relevant research within the fields of psychology, design ethics, interaction design and recommender systems. Finally, we focus on the specific use-case of YouTube's recommender system and propose design changes that will better align with individuals' autonomy. Based on our analysis we offer directions for future research that will help secure rights to digital autonomy in the attention economy.

Citations

• Evaluating collaborative filtering recommender systems

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