TIDAL

Since its founding in 2006, Spotify has been one of, if not the, dominant music streaming service. Other major services include Pandora, Deezer, Apple Music, and Amazon Prime. All of these services notoriously prioritize profits over artists, the worst offender arguably being Spotify, which has come under fire recently due its controversy surrounding one of its top podcasts, the Joe Rogan Podcast. This has brought attention to the privilege that major artists such as Neil Young and Joni Mitchell have to simply pull their music from Spotify, not having to worry about the financial consequences of loss of streaming revenue. The majority of artists on the platform do not have that luxury and are torn between standing up for what they believe in and receiving the measly royalty payments they’re dependent on for income. Moreover, this controversy has reignited a debate over fair payments to artists for streams. Publishers and managers, as well as musicians, are hoping that this conversation continues and leads to Spotify and other platforms considering how much they really value the artists that utilize their services.

Tidal was founded in 2014 and offers a different perspective on what a music streaming service should look like. Its values are to deliver the highest sound quality as intended by the artists, create deeper connections for fans through unique collaborations with the artist community, and a commitment to empower artists to create and deliver their art exactly as they intended. Tidal pays its artists nearly three times the amount per stream that Spotify does, $9.27 per 1,000 streams compared to $3.30 per 1,000 streams. Just by this increase in payment, Tidal has conveyed how much more value it places on artists than the typical streaming service. They also allow their artists to upload their music directly rather than having to go through a third party distributor, putting more power in the hands of artists.

Artists should consider, however, that in a popularity contest between Spotify and Tidal, Spotify still reigns supreme. Tidal is right on Spotify’s heels when it comes to artist discovery by curating a “daily discovery” for users based on their listening habits. But the number of playlists available on Spotify based on genre, mood, activity, and decade is vast. And many have found mainstream success by having a song of theirs featured on a Spotify playlist, which led them to get picked up by a major label. One such story of success is that of indie artist Wallice, whose song “Punching Bag” was featured on the Lorem playlist of over 900,000 followers. From there, the song was streamed 4 million times and caught the attention of London-based indie record label Dirty Hit.

But could Tidal eventually replace Spotify as the preferred streaming service by the masses? It’s quite possible. More listeners are seeing what a listening experience could be like when the needs of artists and fans come first. As one article from the Weekly Ringer puts it:

As the debates over fair compensation for streaming evolve, it’s fair to say that Tidal is taking a step in the right direction. Music fans are slowly ditching Spotify, causing Tidal to gain even more traction. By prioritizing the needs of artists by providing higher pay, delivering their music in high quality audio, and empowering them to upload their music directly onto their service, it will be interesting to see if other streaming services follow suite.

SoundExchange

In light of the issue of paying artists fairly for their recordings being streamed, SoundExchange offers another solution. By administering statutory licenses, artists are able to stream their music on digital radio services while being paid a fixed rate for each play. This differs from agencies such as ASCAP or BMI as they only pay out to songwriters, composers and publishers, versus owners of recordings and the artists on them. SoundExchange collects playlists from internet, satellite, and cable radio services and combs through the data. They then pay out 50% to the rights owner (often time the record label), 45% to the featured artist, and the remaining 5% to non-featured artists. To date, they have paid out over $7 billion in royalties. Registration on SoundExchange is free and easy for artists. Often times, there are royalty payments waiting to be paid out to them until they sign up.

In the for-profit sector, streaming royalties are a complicated mess, to say the least. Payouts depend on several factors and are not even among all platforms. Spotify, for example, pays out somewhere between 60 and 70% of its total revenue to artists. SoundExchange is unique in that it is a non-profit that was designated by the Library of Congress as the sole U.S. organization authorized to collect royalties from services that transmit sound recordings under statutory licenses.

With this tie to the federal government, SoundExchange uses its position to advocate for issues related to the rights of creators of sound recordings. Their website provides valuable information for artists, fans, and industry professionals about the Music Modernization Act, American Music Fairness Act, CLASSICS Act, and several other relevant issues. Thanks to their litigation work in 2021, the Copyright Royalty Board increased royalty rates at +17% for music played on commercial non-subscription digital streaming services and +8% for music played on commercial subscriptions services. Their focus on policy and advocacy makes them stand out as a true leader for the recording industry.

TikTok

Today, a core component of measuring an artist’s success is the amount of engagement they receive from fans. This is especially true for major record labels who have taken to social media and streaming platforms to find their next big artist. TikTok has revolutionized the way artists and fans can connect, even collaborate.

A prime example of this mutually beneficial relationship is between singer Doja Cat and a teenaged TikTok user named Haley Sharpe. Sharpe recorded a video of herself performing a dance to the Doja Cat song “Say So,” it went viral, Doja Cat noticed, put it in her music video and then performed it at the Grammy Awards. Artists can retweet something from a fan or read a DM from them, but TikTok offers something more unique. The platform almost feels like it was designed to be communal, as opposed to Instagram or Facebook where the focus is very much on the artist’s branding of themselves and less so on engagement with the fans.

One artist that found success through the app is Tyler Colon, otherwise known as Tai Verdes. When Verdes decided to become serious about pursuing music, he started putting his music on TikTok. He noticed that listeners were especially engaged, flowing from TikTok to his YouTube and Spotify pages for more. After Tai got picked up by a major label, his song that led to it all, “Stuck In The Middle,” was streamed over 100 million times on Spotify.

Major labels are realizing the value of the app from a cost-benefit analysis. Rather than paying for an extensive social media campaign to promote an artist, they have found more success with much less. All they have to do is pick an artist with an extensive following on TikTok, pitch one of their songs to Spotify to get it put on a playlist with millions of followers, and let the music make its own case to listeners.

There’s a formula for success that has been identified on TikTok. The three primary characteristics users look for are simplicity, vulnerability, and unique song/dance challenges. Lyrics play a big part in how big a song blows up, as users look for songs with relatable themes such as heartbreak, nostalgia, or partying with friends that they can lip sync to. A song must also be short and catchy enough to grab someone’s attention, maybe even have a good rhythm for a memorable dance to be invented alongside it.

If Instagram and Vine had a Gen Z baby, it would be TikTok. It reflects the fearless, eccentric, creative attitude of the youngest generation and through it, record labels are picking out the artists that best represent Gen Z personas. It’s a disruptive technology in an industry when, for the longest time, major labels were deciding what was going to be popular, what kind of music an artist should be releasing, what they should be wearing, saying, basically having total control over them as a branded entity. Now, fans are deciding what they like, how to dress, what to sing about, what to dance to, and the industry is listening.

The disruption of these three technologies comes in the form of a redistribution of power from major labels and publishers to the artists and fans. We are living in a unique time where independent artists have more tools and resources than ever before to make it on their own. Living in obscurity, having to support oneself through other means and adopting the “starving artist” trope should not come at the expense of the expression of oneself through music. More people are realizing that music is a viable career option, and now the technology is catching up to it.

Artificial intelligence in the arts is growing increasingly more complex. It’s 2022, and robots are co-creating art, NFTs are celebrating and confusing art consumers around the world, and at least 85% of Americans have smartphones that give immediate access to endless amounts of streaming content. This article specifically covers the recommendation algorithms built to support content delivery for this majority, their cultural implications, and bias control.

Pervasive Algorithms

New technologies in the crypto realm and metaverse have distracted many from the increasing prevalence of social media and content streaming applications like Spotify, Facebook, YouTube, TikTok, Twitter, etc. So, before delving into the unsolved problems with these services, it is worth reviewing how important they are in our lives through examining consumer data. According to the Pew Research Center, a majority of adults use social media applications at least once a day. The algorithms these applications use to present relevant content to their users vary technically across companies, but one element remains the same between them. The algorithms are constantly improving their modeling capabilities using a combination of content-based, collaboration-based, and knowledge-based methods. Content-based methods use metadata about items presented to recommend what a user would like to see, whereas collaborative and knowledge-based methods use user profiles and behavior and a combination of user and content data, respectively, to share content relevant to specific users. The high-level goal of the algorithm, regardless of its specific structure, is to add personalization and enjoyment to the application for its user.

The Center for Humane Technology calls the cause of our daily reliance on personalized social media and content streaming applications “pervasive technology.” Advanced recommendation algorithms disrupt our daily lives because they constantly reinvent what we see to appeal to our psychological wiring. For example, recommending dramatic or violent social media content appeals to human curiosity, and creating endless scrolling in an application gives users the power to continue engaging in that curiosity consumption without bound. When asked to describe positive experiences with recommendation algorithms, a group of peers responded that they enjoy receiving a mixture of similar and new content, events to attend based on past consumption, and niche accounts that make them feel “extra special and unique.” The common thread in these sentiments is the feeling of being known by the application. Recommendation algorithm tuning has progressed to the point that individuals expect algorithms to understand them, and they reward these systems with their undivided attention when they do.

TikTok currently has one of the best content recommendation algorithms on the market in terms of capturing users’ interests and recommending desired content. It is estimated that 90%-95% of what TikTok users watch on the application comes from the TikTok algorithms’ recommendations. For reference, only approximately 70% of YouTube views can be traced to its recommendation algorithms, and YouTube is widely considered one of the best content streaming platforms with more than 2 billion active users. TikTok is still changing its algorithm often to make even more improvements in content recommendation, but this video provides a general use case for exploring how it curates individualized content.

Cultural Implications – Spotify Case

A major unanswered question surrounding recommendation algorithms and their control over society’s content consumption is: how will these algorithms influence cultural traditions and preferences around art in the long-term? Spotify has the most transparent recommendation algorithm out of most massive content sharing applications, so it is easiest to explain the potential cultural disruptions facilitated by recommendation algorithms using it as an example.

Before platforms like Spotify existed, music was a means of cultural expression. Communities shared songs live from generation to generation to tell stories about their struggles and triumphs, and even in the early 2000s when CDs, mp3 players, and iPods were prevalent, music exposure was dictated largely by the community individuals lived in. However, biases in Spotify’s algorithms are now changing the narrative of how music integrates with society because they expose listeners only to whatever culture(s) they understand.

The first bias that influences what the Spotify algorithm shares is the popularity bias. This bias occurs when a user enters Spotify’s user matrix upon creating an account. The system has little context with which to build successful recommendations upon at this point, so the application is programmed to begin presenting a selection of popular music to the user to kick off their content personalization. The more the user listens, the more Spotify can narrow from a broad list of hits to their most resonant, curated content, creating what has been referred to as an “echo chamber” of listening behavior. Because algorithms are influenced by user behavior, this programming also results in a feedback loop between users and the app that can only be broken if the user searches content outside of the types Spotify presents. There is no way to know exactly how this tunneled call and response of the Spotify user and recommendation algorithm will change our society’s relationship to music, but there have already been urgent calls to redesign music streaming algorithms due to their gender biases. This issue hints at a turbulent relationship between the streaming music industry and equitable music access that has surely changed society’s music affinities already.

Making Algorithms Less Bias

First and foremost, recommendation algorithms are largely built to serve their application’s parent company. The more time users spend on the applications, the more the companies that develop them make. For these platforms to be sustainable and fair to the artists, musicians, and content creators that use them, however, their developers would need to change their goal and code with extreme consciousness toward the biases they feed into them. Because we know that these systems have impactful and unchecked influences on our culture, we must think of their development as a venture in computational social science. An algorithm’s development team can do a lot by bringing interdisciplinary perspectives into the room when building recommendation models to positively or negatively affect the bias in their performance. To mitigate instances of bias in algorithmic design, the developers must first consider diversifying their team.

1. Diversify Development Teams

As Kriti states in “How to keep human bias out of AI”, many women in the computer science field are not respected, trusted, or valued for their capabilities and intelligence as highly as their male colleagues. This issue in the technology field may relate to the fact that gender biases have been discovered in music streaming services. Based on statistics from big technology companies like Google and Facebook, it is likely that pervasive algorithms behind such services are primarily developed by men. Although big tech companies are making great strides to change this, it is also likely that these algorithms are being developed by individuals who identify as White or Asian. 

It takes diverse minds to nurture an algorithm to make diverse decisions. In other words, the more perspectives a development team can engage about how algorithms could potentially leave cultures behind in their data, behaviors, and reach, the less likely it is that the algorithm will be culturally disruptive in a homogenizing manner. Potentially, the diverse team can even craft an algorithm that uplifts diverse and marginalized voices in ways the physical world does not to change our world for the better. Training diverse teams to understand and manage their personal biases could help them grow toward making socially informed and equitable algorithmic decisions as well.

2. Talk about the Data

The complexity of recommendation algorithms is beyond comprehension for most individuals, so it can be difficult to fathom how to improve them from the outside and ensure they fairly represent diverse creators. However, it is widely known that humans create algorithms by feeding them massive amounts of data and fine-tuning their responses to improve the accuracy of their suggestions. This training data that developers choose to give the algorithms matters because the algorithm can only respond within the confines of the information it is given. To provide an oversimplified example for why this is important to artists, imagine that a new music streaming service is building a simple recommendation algorithm. They feed the algorithm a training dataset full of the song metadata and user listening behavior for 200,000 pop music lovers. Then, they recommend a song to a new user who rarely listens to music by collaboratively matching the user with the profiles in this data set. The algorithm will likely recommend a pop song to the new user because it has learned that these songs are popular.

The issue in the above example scenario is that having only pop fans’ data in the algorithm’s training set makes it less likely that a user will get exposure to other types of music that the service provides. For an artist outside the pop genre, this means that they may not gain exposure simply because of this data oversight. It is therefore crucial that developers think about diversity and popularity rankings not just in their physical team, but in the data they feed their algorithms when training them. Over the past few years, lawmakers have proposed over 100 bills in over 20 states aiming to make data sharing more transparent within the AI and recommendation system space and pressure companies to think about data implementation biases.

3. The Power of the User

One of the most exciting elements of recommendation algorithms is that they are always evolving. While it is the onus of the companies that create them to protect artists and content creators by making sure all content has a fair chance of getting presented to users on their platforms, individuals can make up for their blind spots through their consumption behavior. For example, using application search features to find new content outside of the genres typically presented teaches the algorithm to nurture a diverse individual content ecosystem. 

It will ultimately take collaborative efforts beginning at the highest level of technology team recruitment and stepping down to the individual’s consumption behavior to mitigate algorithmic biases. While comprehensive, this work will affect the long-term cultural and artist equity implications of popular recommendation algorithms, thus affecting how our society engages with art in the future.

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