Epistemology

Risk analysis (engineering)

Machine learning

Mathematical analysis

Philosophy

Business

Archaeology

Quality (philosophy)

Surrender

Mathematics

Domain (mathematical analysis)

Computer science

Recommender system

History

Journal of Public Policy & Marketing

Consumers increasingly encounter recommender systems when making consumption decisions of all kinds. While numerous efforts have aimed to improve the quality of algorithm-generated recommendations, evidence has indicated that people often remain averse to superior algorithmic sources of information in favor of their own personal intuitions (a type II problem). The current work highlights an additional (type I) problem associated with the use of recommender systems: algorithm overdependence. Five experiments illustrate that, stemming from a belief that algorithms hold greater domain expertise, consumers surrender to algorithm-generated recommendations even when the recommendations are inferior. Counter to prior findings, this research indicates that consumers frequently depend too much on algorithm-generated recommendations, posing potential harms to their own well-being and leading them to play a role in propagating systemic biases that can influence other users. Given the rapidly expanding application of recommender systems across consumer domains, the authors believe that an appreciation and understanding of these risks is crucial to the effective guidance and development of recommendation systems that support consumer interests.

Algorithm Overdependence: How the Use of Algorithmic Recommendation Systems Can Increase Risks to Consumer Well-Being