Algorithmic Responsibility

Research in algorithmic responsibility at Spotify combines machine learning research with social science to ensure high quality data decisions and equitable algorithmic outcomes. We carry out in-depth research, perform product-focused case studies, as well as develop practical tools that teams can actually use. We do this by taking deep-dives into the characteristics of large-scale datasets, including understanding which creators are less accessible in both existing recommendations and new modalities such as voice, and developing machine learning approaches that combine multiple stakeholder objectives.

Latest Algorithmic Responsibility Publications

November 2024 | EPJ Data Science

Consumption-based approaches in proactive detection for content moderation

Shahar Elisha, John N. Pougué-Biyong, Mariano Beguerisse-Díaz

October 2024 | Journal of Online Trust & Safety

Algorithmic Impact Assessments at Scale: Practitioners’ Challenges and Needs

Amar Ashar, Karim Ginena, Maria Cipollone, Renata Barreto, Henriette Cramer

June 2024 | ICWSM

Socially-Motivated Music Recommendation

Ben Lacker, Samuel Way