How exactly do Spotify’s algorithms make it discriminatory?
By: Kas Moreno Madrigal
Tags: Racism, Spotify, Discrimination, Algorithms, Music

How exactly do Spotify’s algorithms make it discriminatory?

The map shows two crucial pieces of information. Firstly, the types of algorithms Spotify uses. Secondly, how these create, pass on or filter data based within their wider system, record labels and the internet. Kas Moreno Madrigal Nov 13, 2020·4 min read

Firstly, the filtering algorithms in Spotify are 3 — natural language processing, collaborative filtering and audio analysis.

Two of them, natural language processing and audio analysis are mechanical. Natural language processing tracks news articles, blogs, and other texts around the internet, which we already know, is a filtered environment. It simply analyses your language or location, and so it assumes that because you live in a certain country you would like a specific kind of music. It makes assumptions based on your culture, and the culture that the internet creates. Raw audio model analysis looks at things like key, mode, tempo or loudness, since when Spotify creates playlists they all have to sound a similar way.

However, collaborative filtering is human-based. When a person inputs a new song into Spotify, there is a form where you have to label your music by genre and the ‘feeling it gives off’ via a description. This is where, for example, you need to input patterns of where you fit in with other singers. For example, if you’re a LGBTQ artist it’s suggested that put that in the description, which already leads to group mentality. Then, Spotify creates an implicit data feedback, it tracks whether a user saves an artist to their own playlist, or visits the artist page after listening to a song. Then, the algorithm suggests music for users that have liked similar songs to yours, or from users that have similar listening patterns to yours.

Secondly, these algorithms correlate to the internet in the following way. (More information on the next page)

The map (presented in the following page) shows two crucial pieces of information. Firstly, the types of algorithms Spotify uses. Secondly, how these create, pass on or filter data based within their wider system, record labels and the internet.

Firstly, the filtering algorithms in Spotify are 3 — natural language processing, collaborative filtering and audio analysis.

Two of them, natural language processing and audio analysis are mechanical. Natural language processing tracks news articles, blogs, and other texts around the internet, which we already know, is a filtered environment. It simply analyzes your language or location, and so it assumes that because you live in a certain country you would like a specific kind of music. It makes assumptions based on your culture, and the culture that the internet creates. Raw audio model analysis looks at things like key, mode, tempo or loudness, since when Spotify creates playlists they all have to sound a similar way.

However, collaborative filtering is human-based. When a person inputs a new song into Spotify, there is a form where you have to label your music by genre and the ‘feeling it gives off’ via a description. This is where, for example, you need to input patterns of where you fit in with other singers. For example, if you’re a LGBTQ artist it’s suggested that put that in the description, which already leads to group mentality. Then, Spotify creates an implicit data feedback, it tracks whether a user saves an artist to their own playlist, or visits the artist page after listening to a song. Then, the algorithm suggests music for users that have liked similar songs to yours, or from users that have similar listening patterns to yours.

Secondly, these algorithms correlate to the internet in the following way.

You begin with the ‘listeners and artists’ section, where they exchange music in all its forms, they have communities and relationships purely based on music. As you can see, the first step is highlighted through accessibility and how from raw data in multiple forms, only some reaches the internet. When it is there, some information is highlighted over others due to Google’s algorithms (hence some shapes in the map being bigger than others). This is important for Spotify, as with natural language processing algorithms (step 2) if someone is writing about an artist in a blog or newspaper article, this will be picked up by Spotify and raise the awareness of that artist within its platform. This means that what you say in Google has also consequences in Spotify, everything is interconnected. Record Labels are always inputting further information into Spotify as investors in the platform, and they place their own agenda within its algorithms. Artists always have the option of adding more data into the platform, however because of the rules of Collaborative Filtering, the data inputted must follow the forms and structures already in place. In the final step 4, Spotify gives listeners back the musical options it has based on this process and the audio analysis algorithm, which looks for music that is similar to each other.

All in all, this map shows what information is inputted and what information is taken out of the equation when it comes to the music streaming options within Spotify.