fairness

Invited talk at the MediaFutures Seminar at the University of Bergen.

I had a busy week with talks. Great that we have not the opportunity for online talks to connect across continents.

Invited talk at the Music and Audio Research lab—a research school within New York University.

I’m happy to announce that I’m co-editing with Bruce Ferwerda a so-called research topic in Frontiers in Big Data (Recommender Systems) on Human Issues in Recommender Systems. For more information, see https://www.

What an honor that my recent work with Andrés Ferraro is featured as an example for research on Equality, Diversity & Inclusion at Utrecht University’s website. There is also a sneak peek to an upcoming work with Johanna Devaney.

I am very happy that our CHIIR paper Break the Loop: Gender Imbalance in Music Recommenders with Andrés Ferraro and Xavier Serra received a lot of media attention. It will take a while until I can integrate all the media articles (that I am aware of) in my website.

This article is republished from The Conversation under a Creative Commons license. Read the original article. Music recommendation algorithms are unfair to female artists, but we can change that. Algorithms help lots of people discover new music.

Andrés Ferraro will present our joint paper at CHIIR 2021. In the paper with the title Break the Loop: Gender Imbalance in Music Recommenders, we investigate the imbalance in music recommendations with respect to the artists gender.

The recording of the panel on gender bias and discrimination in AI (in German) at the VUT Indie Days 2020 (as part of c/o pop xoxo) last week is now also available on YouTube.

I am happy to announce that I will be part of this year’s online edition of VUT Indie Days 2020, co-located with c/o pop xoxo. I will join the panel on Gender biases and discrimination in AI.

The purpose of this project is to understand how current systems used to recommend music affect artists and to propose alternative solutions that could benefit the artists. The goal is to identify potential problems that affect artists, and to draw on the artists’ perspective about how future recommender systems could work.