8–10% of algorithmic recommendations are ‘bad’, but… an exploratory risk-utility meta-analysis and its regulatory implications - Hilbert, Martin; Thakur, Arti; Flores, Pablo M.; Zhang, Xiaoya; Bhan, Jee Young; Bernhard, Patrick; Ji, Feng (International journal of information management, 2024)
Keywords: Algorithmic auditing; Digital harms; Machine behavior; Meta-analysis; Recommender algorithms; Machine behavior; Digital harms; Algorithmic auditing; Recommender algorithms; Meta-analysis