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 2021 | CIKM

Leveraging Semantic Information to Facilitate the Discovery of Underserved Podcasts

Maryam Aziz, Alice Wang, Aasish Pappu, Hugues Bouchard,Yu Zhao, Benjamin Carterette and Mounia Lalmas

May 2021 | ICWSM

Representation of Music Creators on Wikipedia, Differences in Gender and Genre

Alice Wang, Aasish Pappu, Henriette Cramer

May 2021 | CHI

Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits

Brianna Richardson, Jean Garcia-Gathright, Samuel F. Way, Jennifer Thom, Henriette Cramer