Translation, Tracks & Data: an Algorithmic Bias Effort in Practice


Potential negative outcomes of machine learning and algorithmic bias have gained deserved attention. However, there are still relatively few standard processes to assess and address algorithmic biases in industry practice. Practical tools that integrate into engineers’ workflows are needed. As a case study, we present two tooling efforts to create tools for teams in practice to address algorithmic bias. Both intend to increase understanding of data, models, and outcome measurement decisions. We describe the development of 1) a prototype checklist based on existing literature frameworks; and 2) dashboarding for quantitatively assessing outcomes at scale. We share both technical and organizational lessons learned on checklist perceptions, data challenges and interpretation pitfalls.


April 2022 | The Web Conference (WWW)

Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencing

Emanuele Bugliarello, Rishabh Mehrotra, James Kirk, Mounia Lalmas

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