Case study: Disrupting Spotify — a plugin to change it all
By: Kas Moreno Madrigal
Tags: Spotify, Case Study, Prototyping, Discrimination, Algorithms

Within the context of this brief, we were asked to identify a ‘disrupted’ market — by the appearance of internet giants — and develop ‘an incumbent service’ to create non-zero-sum games, where all parties are ‘winners’ (instead of our current system, where inequality is a keen and painful reality). Our area of research was the music industry, and specifically the streaming platform Spotify. Nevertheless, within this brief, there are a series of assumptions that need to be unpacked. Firstly in regards to the meaning of disruption within business.

This project was a team effort. The amazing and talented members include Tania Carregha (Social Innovator), Leonardo Gentili (Service Designer), Ana María Ramírez Mourraille (Social Innovator).

What is in fact, ‘disruption’?

The term ‘disruption’ is introduced when discussing the marketplace in the 1995 Harvard Business review article by Bower and Christensen. These economists believed that disruptive technologies occurred due to a company’s short-sightedness (Christensen, 1995, disruptive-technologies). Established companies use inductive logic to manage their product development, investments and so forth. Inductive logic (The Design of Business, Martin, p.63) works from the specific to the general — if my Facebook ad with the bright red background has generated 15% more revenue when targeting 18–30 year olds, then I conclude that 18–30 year olds respond better to red Facebook ads.

Inductive logic inadvertently creates a philosophy — if it has worked in the past, it will work in the future.

It is because of this that small, ‘disrupting’ organisations can become serious challengers in the economy. They, on the other hand, follow the philosophy ‘if it has worked in the past, it won’t work in the future.’ Christersen had another stab at this conversation 20 years later in the same publication (Christensen et al, 2015, what-is-disruptive-innovation). They come to the conclusion that in order for an organisation to be considered disruptive, it must follow a series of rules:

Firstly, it must create a new market or provide for an underserved (also called low-end) audience.

Secondly, it will only be accepted with mainstream consumers once the quality has reached the same standards as the industry competitors.

This is a particularly interesting point, as it aligns perfectly with the way of working of the Lean Startup — in which quality is built slowly, through a constant sense of iteration or, as they call it, validated learning (Ries, 2011). This method is usually attributed to these ‘disrupting’ organisations. They seem to appear all of a sudden in the market, however, they are usually the result of long processes of iteration.

They argue that if one follows this framework, many organisations we consider to be disruptive are, in fact, simply sustaining. The main difference being, a company that is sustaining is providing a service that has been improved. A disrupting organisation provides a service that has been diversified.

To put an example — one could then argue that Uber is not in fact a disrupting organisation.

It has not created a service for the underserved or unlocked new markets, it has improved general demand by providing a cheaper, more convenient alternative to the existing one. It has improved, or rather, sustained, the service. Whether this example holds or not after Uber’s global expansion requires deeper research, however, it is important to make this distinction in order to approach the process of creating a disrupting service, not a sustaining one, within the task of this project. Nevertheless, it is my opinion that when an industry has been in constant disruption, constantly in a flux of creating consumers into non-consumers and vice versa, can it be truly called disruption?

If disruption is the norm, does it deserve the name?

According to Joseph Schumpeter, who in his book ‘Capitalism, socialism and democracy’ studied the nature of capitalism, this constant disruption is, in fact, the norm. He calls it Creative Destruction.

Schumpeter’s iconic waves of disruption turned into code and redesigned into a cut pattern. By Kas Moreno Madrigal

‘The same process of industrial mutation — if I may use that biological term — that incessantly revolutionises the economic structure from within, incessantly destroying the old one, incessantly creating a new one. This process of Creative Destruction is the essential fact about capitalism.’ (Schumpeter, 1994, p.83). This constant remodelling of a system is exactly the case in which the music industry has found itself in. As Schumpeter suggests, closely linked in the last decades with technological development and the boom of capitalism, but mainly through the simple interconnectivity of music and culture. From Pope Gregory codifying chants in Roman Catholic religious services after the spread of Christianity throughout Europe, to the arrival of West African rhythms in 1860 to the western world due to the slave trade of black communities. Disruption and specifically disruption within the music industry is deeply interconnected to the development — and destruction — of communities. A clear example — In London, Grime concerts are perpetually cancelled under the 696 risk assessment law, as they are strongly correlated to a discriminatory perception of black communities in the UK. The link between the community and the music genre is that strong.

‘I’ve never been in the club, ever and somebody has been shot. I’ve heard it in some west end clubs (…) but you never hear them say that chart artists’ music is causing it. But if there’s a show and there’s a Grime event and something happens, then it is Grime music that’s causing it’ (Adenuga, 2014, www.youtube.com).

Grime is a UK-based genre which draws in elements of reggae, dance and rap music, developed around 2002 in London, evolving from the club music of the time, predominantly Jungle and UK Garage. (Google arts and culture, 2020, artsandculture. google.com) ‘The Promotion Event Risk Assessment Form 696 is designed to allow the management of licence premises, promoters of music events, security and the police to work in partnership to identify and minimise any risk of most serious violent crime happening at the proposed event’ (Met Police, 2020).

The history of music is the history of people and equally as full of its struggles, shifts and disruptions.

It is nevertheless important to unpack this term and by extension, the requirements for an organisation to be considered disruptive within a market and the music industry in particular. This term will be unpacked further, and the brief reframed, throughout the process of this essay.

Redefining this theoretical framework of disruption guided our aim within the project swiftly into Social Innovation, putting communities and activism for social justice at the heart of our decision-making. We were guided by the philosophy of Social Innovation as a way of ‘recombining existing resources and capabilities to create new functions and new meaning’ (Manzini, 2015, p.13). And tried to make sure that as we went through our prototype we asked ourselves — is it truly needed to make something new, instead of simply connecting what is already there?. Within our team dynamics, we had a vision that we agreed upon and the development of the project was decided based on how well it fit within the vision.

The origin of the internet

Much like music, the internet also came about from a deeply human experience. In his book ‘The Internet is not the answer’ (2015), Silicon Valley entrepreneur Andrew Keen explains how, very similarly to music, its origins were community-based, free and looking for a social revolution. In its early days, Nicholas Negroponte, an influential futurist and MIT professor of technology predicted that the digital age would be ‘decentralizing, globalizing, harmonizing and empowering.’ (Negroponte, 1995, p.229). Such high hopes were raised, in which communities would thrive within this new structure of the world. Even in the Declaration of Cyberspace, Barlow writes.

‘We will create a civilization of the Mind in Cyberspace. May it be more humane and fair than the world your governments have made before.’ (Barlow, 1996, www.eff.org).

The internet had started as a way of national security, figuring out how to enable anti-aircraft guns to track and destroy German bombers, and in the Cold War, looking for new ways of safe communication. But what we understand as ‘the internet’ did not appear until Berners-Lee turned these new technologies of connectivity into a new sense of communal ‘consciousness’ — the World Wide Web. He believed the Web should be strictly not for profit, but his version was incredibly difficult to navigate without advanced programming skills. It wasn’t until the 1980s where Marc Andreessen, a computer science student, created an easy to use, graphics-based web browser called Mosaic. But with limited pay and no recognition for his work, Andreessen quit and was soon approached by Jim Clarke, who invested 3 million dollars into turning Mosaic into Mozilla. However, it was also then that Berners-Lee’s vision of a free internet crumbled —

‘“So the hell with the commune,” Clark wrote in his autobiography as he turned his back on the nonprofit World Wide Web created by Tim Berners- Lee. “This was business.”’ (Keen, 2015, p.99)

And so, the internet as we know it was born.

Not simply by monetizing the World Wide Web, but by forgetting that it was meant as a pool of communal consciousness, and hence assuming that the internet would remain a neutral environment.

Throughout our research, we found it is in fact a pool of biases — ‘search results (…) reflect our lowest and most demeaning beliefs’ (Noble, 2018, p.99).

A crucial question is raised at this point — how is it possible that long lines of codes hold beliefs? To answer this question we must surface the concept of algorithms.

‘An algorithm is the software you use to analyze a big data set. Data sets have millions of pieces of information, and an algorithms job is to organize it in a way that it makes sense. Essentially it’s a question written in code’ (Think: Digital Futures, 2017, podtail.com)

Like every question, it holds tremendous power. It is deeply dependent on who is asking it, to what purpose, and how.

A good example of this is the STMP algorithm used in New South Wales, Australia. Police were asking the database — which of the people in this group is likely to commit a crime?. This preventive method of policing created filtering of information based on any and all interactions with police from a group of aboriginals — from having a criminal record, to knowing someone that has one, to simply being suspended from school. From this list of people, the police had now legal reason to begin stop and searches. This created a spike of ineffective stop and search actions to aboriginal youths with no actual record. By creating biased algorithms, we create a biased filtering of information and hence, a biased digital reality —

‘Algorithmic oppression is not just a glitch in the system but, rather, is fundamental to the operating system of the web.’ (Noble, 2015.p. 43.)

Which raises another issue — that of the type of information being given into the system. We believe that everyone has access to the internet, to wifi. Yet this is a service that cannot and is not accessed by everyone. From older generations that did not jump on the digital wave, to people with disabilities that need extra technology to surf the web or simply people from lower socioeconomic backgrounds that have less financial stability.

‘Over five million adults in the UK are not accessing vital services, help and information online. Many of these are among the most vulnerable in society, including some disabled people and older people. (…)The ONS data also revealed about 700,000 secondary school pupils do not have a tablet or computer to access the internet at home, with a further 60,000 having no internet access at all.’ (Wyatt, 2019, independent. co.uk).

This poses a problem within the world of data and algorithms. It’s not simply that the algorithm is biased, but so is the data that is originally fed into the system, usually leaving behind the most vulnerable communities.

These issues in our ‘communal consciousness’ and the human biases of in-human algorithms are a definite characteristic in the platform of Spotify. Spotify is the leader in a long list of the so called ‘streaming platforms’ that took over the music industry after the digitisation of music. When we apply these data and algorithmic issues to music, this means that streaming platforms such as Spotify ‘do not have an established presence within the communities that produce subgenre musical genres, the people who create the algorithms and define music categories often don’t even see what they are missing’ (Lucero, 2020, qz.com). Whilst some errors are inevitable, constant filtering of data creates systemic changes in the shape and type of data we find,(click here to learn more about Spotify’s specific algorithms). According to our ethnographic research, the effect that these algorithmic structures have had on the music industry are specifically felt by artists and local communities.

‘The amount you get paid per stream is, as I’m sure you know, pitiful’ (Anonymous singer/song writer, 2020, Zoom interview).

There is a problem of equal opportunity for artists, which was always relevant in the music industry, as record labels have ‘exclusive, all-embracing exploitation rights on the artist’s work, which are locally and temporally unlimited’ (Schöpe, 2009, p.14). There was hope that streaming platforms would tumble power dynamics within record labels. Nevertheless, these have soon entered the world of streaming through investing in Spotify —

‘Sony BMG owns 5.8 percent of Spotify, Universal owns 4.8 percent, Warner Music owns 3.8 percent, and EMI has 1.9 percent. Merlin, which represents many independent record labels, owns 1 percent.’ (Aguiar and Waldfogel, 2018, p.2).

It’s the same model of inequality in music, simply implemented differently. Another artist explains, when speaking about Spotify ‘The idea that it was going to be impartial and everyone got a chance, it’s never going to be true. Money always gets in the way of everything.’ (Anonymous musician, 2020, Zoom interview).

Yet it is not simply about artists rights, as explained previously, music is deeply connected to communities. A London DJ says — ‘there was a radio station called Radar Radio. It has basically disappeared. It’s a bit of a shame because you don’t really have that many underground radio stations, or high level underground.(…) Radio, that was a big place for Hip Hop and Grime. And a lot of people were connected to that, but that died down.’ (Anonymous DJ, 2020, Zoom interview).

The Remora plugin is the answer to that question.

Digital remora attached to a shark by Kas Moreno Madrigal

To clarify the metaphor — a Remora is a small aquatic animal that attaches itself to a shark in a symbiotic relationship. The remora eats the parasites off the shark’s body, gets scraps of food and protection from other predators, whilst the shark remains parasite free. Therefore, a non-zero-sum game appears. This was the philosophy for our prototype.

It is an extension for Spotify that deletes its algorithms and substitutes them with new, fairer ones.

Remora logo example

It would let users see and control the data and algorithms that are shaping their music browsing experience, hence affecting the type of music that listeners encounter within this platform and making data use transparent. We came to its features through a series of prototypes and testing exercises.

Its features would include

Please bare in mind these features are work in progress, some of them finished, some underway. These wireframes are the conclusion of the features, benefits and ethnographic research we gathered along the process. It is the culmination of all philosophies.

1. Imagine shuffling the WHOLE of Spotify

Toggle your experience by genre or number of listeners, features unavailable currently. Browse with affirmative action.

2. Learn about algorithms and data use.

With an accessible glossary and easy to use data visualisations.

3. Raise awareness.

Sign our petition and take a stand for the silenced voices in the game. Become empowered with your data.

In the Main Screen you have the option of putting your user in ‘activist mode’, therefore letting Remora gather data and then share it with you. The ‘I’m feeling lucky’ toggle, randomises the search to the point of shuffling the whole of Spotify, a feature that is not currently possible in this application due to the agenda of the algorithms and investors in the system.

In Action A, the icon of Remora is clicked and you can see what data the system has gathered from you, with the prompt of learning more in order to incite curiosity and data literacy.

In Action B, the toggle icon is clicked and you can personalise your search by number of listeners or location.