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Found 11 papers, 6 papers with code
Towards Responsible AI Music: an Investigation of Trustworthy Features for Creative Systems
Whilst previous work has tackled isolated aspects of generative systems (e. g., transparency, evaluation, data), we take a comprehensive approach, grounding these efforts within the Ethics Guidelines for Trustworthy Artificial Intelligence produced by the High-Level Expert Group on AI appointed by the European Commission - a framework for designing responsible AI systems across seven macro requirements.
Behind Recommender Systems: the Geography of the ACM RecSys Community
Recommender Systems filter this information into manageable streams or feeds, adapted to our personal needs or preferences.
Fairness and Diversity in Information Access Systems
Among the seven key requirements to achieve trustworthy AI proposed by the High-Level Expert Group on Artificial Intelligence (AI-HLEG) established by the European Commission (EC), the fifth requirement ("Diversity, non-discrimination and fairness") declares: "In order to achieve Trustworthy AI, we must enable inclusion and diversity throughout the entire AI system's life cycle.
Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study
We present the results of a 12-week longitudinal user study wherein the participants, 110 subjects from Southern Europe, received on a daily basis Electronic Music (EM) diversified recommendations.
Monitoring Diversity of AI Conferences: Lessons Learnt and Future Challenges in the DivinAI Project
DivinAI is an open and collaborative initiative promoted by the European Commission's Joint Research Centre to measure and monitor diversity indicators related to AI conferences, with special focus on gender balance, geographical representation, and presence of academia vs companies.
Fair ranking: a critical review, challenges, and future directions
Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and hiring.
Diversity in the Music Listening Experience: Insights from Focus Group Interviews
In this study, we interview several listeners about the role that diversity plays in their listening experience, trying to get a better understanding of how they interact with music recommendations.
Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics
Shared practices to assess the diversity of retrieval system results are still debated in the Information Retrieval community, partly because of the challenges of determining what diversity means in specific scenarios, and of understanding how diversity is perceived by end-users.
Exploring Artist Gender Bias in Music Recommendation
Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i. e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users' specific music preferences.
Measuring Diversity of Artificial Intelligence Conferences
The lack of diversity of the Artificial Intelligence (AI) field is nowadays a concern, and several initiatives such as funding schemes and mentoring programs have been designed to overcome it.
Recognizing Musical Entities in User-generated Content
Recognizing Musical Entities is important for Music Information Retrieval (MIR) since it can improve the performance of several tasks such as music recommendation, genre classification or artist similarity.
