Computed compatibility

Examining user perceptions of AI and matchmaking algorithms

Bibliographic Data

ID21452624
AuthorsAditi Paul (0000-0001-9476-2134, Communication and Media Studies, Pace University, New York, NY, USA, corresponding author), Saifuddin Ahmed (0000-0001-6372-213X, Nanyang Technological University)
Year2024
Volume43
Issue5
Pages1002-1015
Publication date2024-04-03
Open AccessNo
TypeARTICLE
VenueBehaviour and Information Technology (JOURNAL)
Journal identifiersISSN: 0144-929X • E-ISSN: 1362-3001
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/0144929x.2023.2196579
OpenAlexW4365813599
LanguageEN
Citations received2
References cited43

Artificial intelligence (AI) driven matchmaking algorithms are at the core of modern-day dating. Millions of users rely on these algorithms used by online dating platforms for successful matchmaking. However, a scholarly understanding of user perceptions of AI-driven matchmaking algorithms is limited. We explore the factors affecting users’ perceived effectiveness of matchmaking algorithms and analyze how users’ perception of AI’s fairness, social presence, and the threat posed by AI are associated with their perceived effectiveness of matchmaking algorithms. We also investigate if their previous relationship initiation experience through online dating platforms further moderates the studied relationships. An analysis of survey data from Singapore suggests that those who perceive AI to be fair and have higher levels of social presence are more likely to showcase a higher degree of perceived effectiveness of matchmaking algorithms. Moreover, those who have previously been successful in online dating relationship initiation are also more likely to believe in the perceived effectiveness of these algorithms. Further, previous experience of relationship initiation conditionally impacts the relationship between users’ general AI perceptions and perceived effectiveness. We also find that males and younger respondents are more likely to believe in the efficacy of matchmaking algorithms. Practical implications are offered

Algorithm · Machine learning · Perception · Artificial Intelligence · Computer Science · Evolutionary Psychology and Human Behavior · Psychology · Sexual function and dysfunction studies · Sexuality, Behavior, and Technology

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