AI-Powered Recommender Systems: Personalization and Bias - Taneja, Ankit Kumar; Tripathi, Chandra (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2020)
AI-powered recommender systems changed how users discovered products and services online. These systems use sophisticated algorithms to analyse user preferences, behaviour’s, and product characteristics, with the goal of providing personalized recommendations. Personalization enhances the user experience by suggesting relevant content, thereby increasing user engagement and satisfaction. However, the effectiveness of these programs raises concerns about inherent bias. Recommendation systems often rely on historical user data, which can be biased by the data, and lead to potential gaps and lack of recommendations for example, if historical information reflects a preference or it excludes particular groups, the system may inadvertently reinforce this bias. Preventing bias in AI-driven recommendation systems is essential to ensure fairness and inclusion. Strategies such as algorithmic transparency, collection of diverse data types, and algorithmic adjustment can reduce biases. Striking a balance between individualism and diversity is challenging, requiring constant flexibility and ethical considerations. Efforts are being made to increase transparency and accountability in these processes, with the aim of generating more relevant recommendations. Ethical guidelines, industry standards and regulatory frameworks will play a key role in shaping the development and implementation of these AI systems, including the design and implementation of responsible AI. In conclusion, as AI-powered recommendation systems create personalized experiences, minimizing bias is essential to ensure fairness and encourage inclusion. Striving for a transparent, accountable, and ethical desi.