Responsible Artificial Intelligence for Music Recommendation

The widespread adoption of music streaming platforms has necessitated the development of accurate music genre classification systems. These systems, which are often based on techniques for machine learning (ML) and artificial intelligence (AI), have raised ethical and social concerns. As such, implementing responsible AI practices has become essential in developing music recommendation systems. This chapter presents a responsible AI-based music recommendation approach that employs classical ML models, neural networks, and deep neural networks to accurately classify music genres while adhering to ethical principles and ensuring accountability. Feature retrieval techniques were utilized to extract relevant information from music data, and model performance was evaluated using a range of metrics. The deep neural network model demonstrated superior performance, achieving 93% accuracy on both the training and test datasets, a micro-average Receiver Operating Characteristic (ROC) curve area of 99, and 91.4% confidence interval accuracy. Using Explain Like I’m 5 (eli5), permutation importance was utilized to pinpoint the dataset’s most significant features, and these features were subsequently used to retrain the models. Finally, the SHapley Additive exPlanations (SHAP) technique was employed to provide interpretability of the model predictions. The chapter concludes that the developed responsible AI-based music recommendation system can offer personalized recommendations to users while minimizing potential risks and ensuring accountability through transparency and explainability.