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Algorithm overdependence : how the use of algorithmic recommendation systems can increase risks to consumer well-being

Banker, Sachin; Khetani, Salil - In: Journal of public policy & marketing 38 (2019) 4, pp. 500-515

Persistent link: https://www.econbiz.de/10012534263

Predicting customer demand for remanufactured products : a data-mining approach

Truong Van Nguyen; Zhou, Li; Chong, Alain Yee Loong; … - In: European journal of operational research : EJOR 281 (2020) 3, pp. 543-558

Persistent link: https://www.econbiz.de/10012157390

Applying Machine Learning to Predict Online Customers Behaviour

Adz Zikri, Arif Furqon Nugraha; Widianto, Sunu - 2023

, the authors used Decision Tree, Random Forest, XG Boosting, KNN, and SVM algorithms through a different set of conditions …

Persistent link: https://www.econbiz.de/10014360732

Risks of observable and unobservable biases in artificial intelligence predicting consumer choice

Teleaba, Florian; Popescu, Sorin; Olaru, Marieta; … - In: Amfiteatru economic : an economic and business research … 23 (2021) 56, pp. 102-119

Companies are increasingly adopting Artificial Intelligence (AI) today. Recently however debates started over the risk of human cognitive biases being replicated (and scaled) by AI. Research on biases in AI predicting consumer choice is incipient and focuses on observable biases. We provide a...

Persistent link: https://www.econbiz.de/10012821258