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7 PROOF A Case for Musical Privacy Richard Randall
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7
A Case for Musical Privacy
Richard Randall
Singin’ right to me I can hear the melody
The story is there for the takin’
Drivin’ over Kanan, singin’ to my soul
There’s people out there turnin’ music into gold
John Stewart, “Gold”
If I didn’t love you, I’d hate you
I’m playing your stereogram
Singles remind me of kisses
Albums remind me of plans
Squeeze, “If I didn’t love you”
“Every song has a story. What’s yours?” read the subject of an email sent to
me by the streaming-music service Spotify (2014a). The email continues:
Spotify
#thatsongwhen
Find a song, tell your story, share with the world.
Nothing triggers a memory quite like a song. You know, that song
when weekend mornings meant sugary cereal and cartoons. Or that
song when you did everything to win the heart of your playground
crush ... So we’re asking – what songs take you back to a special
moment?
Here are some of the songs you played most in 2014:
Give Out by Sharon Van Etten
Serpents by Sharon Van Etten
Leonard by Sharon Van Etten
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Does one of them spark a good story? We’d love to hear it.
Or explore other stories in the gallery.
When I saw this list of songs, I knew exactly when I was listening to them,
where I was, how I felt, what was going on in my life, and how these songs
made me feel. These experiences came back in vivid detail. I probably
listened to the song “Give Out” (Van Etten, 2012, track 2) over a hundred
times during this period on my MP3 player, on my computer, and apparently on Spotify. When I saw Spotify’s request that I share why with them
and “the world,” I was taken aback. The time in question was emotionally charged and challenging. I felt fragile and disoriented. The song was
an anchor for me. It was a point of reference and a constant companion.
The song made me feel I wasn’t alone in a way that was safe, private, and
confidential. To me, sharing the details of this experience would be on par
with sharing a private conversation with a therapist or a trusted friend.
While this story might seem melodramatic, I share it to highlight
the personal and intimate relationship we have with music. Listening
to music is an important part of our lives and our listening habits say a
lot about who we are, how we feel, and what we believe. Over the past
ten years we have seen an unprecedented transformation in how we
are able to discover and listen to music. Online streaming music services such as Spotify and Pandora comprise a complex of technologic,
economic, and critical human issues. Some of these issues are common
to streaming media services in general (e.g. YouTube, Netflix, Hulu)
and the Internet, while others are unique to music services. This essay
examines streaming music services (SMuS) from the perspective of the
listener. Listening to music online is drastically different from offline
listening largely because the economics of online listening create a new
model of the “audience commodity” and raise critical privacy issues.
The economics of SMuS have been discussed largely as to whether or not
artists are fairly compensated for their music or how SMuS represent a
new business model for the industry. However, in the context of SMuS,
listening becomes a transaction whereby a user’s selection labor is converted into a commodity that has exchange-value. Moreover, this essay
explores how selection labor reveals personal information we make
freely available anytime we make a choice that is recorded by a second
party. This essay works to raise awareness of the kinds of transactions
we are engaging in and risks we are exposed to when we listen to music
online and frames musical identities as something worthy of protection.
In order to discuss streaming music services it is important to
understand some background and issues of online digital capitalization.
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Web 2.0 describes a set of online technologies and practices in which
users are encouraged and empowered to generate and share content
and make on-demand, selective choices about media consumption
(O’Reilly, 2007). A large part of the political economy of Web 2.0 can be
summarized by the free labor (Terranova, 2000) duality of prosumption
and surveillance (Fuchs, 2012). Prosumption is the combined activity of
content production and consumption (Ritzer and Jurgenson, 2010). As
a form of capitalism, prosumption describes how social networking sites
such as Facebook and Twitter work: a Twitter user, for example, produces
tweets for others to consume and this user consumes tweets produced
by others. The mitigating service, in this case Twitter, is free to the user.
In order to make money, however, the service must sell something to
someone. The content each user creates is surveilled, aggregated, analyzed, and sold to third parties often for the purposes of advertising in a
practice called “behavioral targeting ” (Anderson, 2014). In other words,
the product Twitter sells is both the labor of the user (in the form of
content created to attract and retain other users) and the user (who
receives targeted advertisements). Andrejevic writes that “[t]he value
accruing to the privatization of network resources is, at least in part,
dependent upon the ability to extract productive data from its users –
data that can serve as a resource for advertisers, employers, political
campaigns, and policing” (2012, p. 160).
Sharing personal information is common on online social networks
such as Twitter and Facebook. A social network is “an Internet community where individuals interact, often through profiles that (re)present
their public persona (and their networks of connections) to others”
(Acquisti and Gross, 2006, p. 37). When we participate in these networks,
we have a reasonable understanding of how what we share can be and
will be used.1 For example, sharing information about a recent vacation will both keep friends and family appraised of your activities and
generate targeted advertisements for future travel opportunities. Social
networks, search engines, and free email services collect and analyze
data ostensibly in order to connect goods and services with consumers
who are most likely to purchase them.2 The information generated by
our online activity in terms of both content and behavior falls into the
category of “Big Data.” Big Data describes data sets that are so large and
complex that they resist traditional methods of analysis. It is a $50 billion
industry characterized by algorithmic “mining” techniques that search
for otherwise obscured patterns in human-generated information (Kelly,
2014). The goal is often to establish correlations between various factors that allow the assertion of probable behaviors of individuals and
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groups. Depending on the analytic goals, Big Data can be used to identify a person as a potential product buyer (behavioral targeting), medical
risk, or terrorist threat. The main ethical issue with Big Data is that digital prosumers never know how their data will be used. In his critique of
Big Data analytics, Acquisti asks us to:
Imagine a world in which the collection and analysis of individual
health data allow researchers to discover the causes of rare diseases
and the cures for common ones. Now, consider the same world, but
imagine that employers are able to predict job candidates’ future
health conditions from a few data points extracted from the candidates’ social network profiles – and then, imagine those employers
making hiring decisions based on those predictions, without any
candidate’s consent or even awareness (2014, p. 76).
Prosumers often acquiesce to data collection by believing that potential
benefits outweigh risks. We will get better user experiences, access to
goods and services we want, and be shielded from things we don’t want.
But Acquisti writes that “[t]he metaphor of a ‘blank check’ has been
used to describe the uncertainty associated with privacy costs: disclosing personal information is like signing a blank check, which may never
be cashed in – or perhaps cashed in at some unpredictable moment in
time with an indeterminably low, or high, amount to pay ” (2014, p. 84).
A 2014 New York Times article highlights the issue succinctly. A suicide
prevention group released an app that allowed Twitter users to monitor
the feeds of anyone they follow for key terms that may indicate that a
user is a suicide risk.
A week after the app was introduced on its website, more than 4,000
people had activated it, the Samaritans said, and those users were following nearly 1.9 million Twitter accounts, with no notification to
those being monitored. But just about as quickly, the group faced an
outcry from people who said the app, called Samaritans Radar, could
identify and prey on the emotionally vulnerable – the very people
the app was created to protect. (Singer 2014a)
The risks of such a surveillance technology were many. For example,
stalkers could use the app to identify a victim’s vulnerable moments
and employers could make hiring decisions based on amateur psychiatric diagnoses. As one health-care professional pointed out, “you can
have sophisticated employment consultants who will do the vetting on
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people’s psychiatric states, derived from some cockamamie algorithm,
on your Twitter account” (Singer 2014a). The well-meaning app was
withdrawn once it was clear that its possible nefarious implementation
was beyond the control of both the creators and the users being monitored. This example highlights the fact that digital users rarely know
when or how they are at risk. The Samaritan Radar case is important and
unique because the analytic results and means for obtaining them were
explicit and designed to be collected and used by the public. It was a
transparent transgression that met with immediate condemnation. For
proprietary services such as Twitter, Facebook, or Google, however, user
agreements are vague, temporary, and voluminous. We are never fully
aware of what information is being extracted or how it is or will be used.
We are signing a blank check.
For streaming media services (SMeS), how users interact with technology and consume and produce content is somewhat different. While
some SMeS, such as YouTube, SoundCloud, MySpace, or Vimeo, allow
users to prosume, other services, such as Spotify and Netflix, do not and
focus on consumption. Netflix users, for example, do not upload their
own content. Revenue is generated by subscriptions to the service
(Netflix) or general advertising (Hulu). The service, therefore, functions
more like traditional cable or broadcast media. For a SMuS like Spotify or
Pandora, users can upload media so long as they can provide evidence
of ownership and agree to the service’s terms of use. Still, this is similar
to traditional broadcast radio where an individual can send their own
recording to a radio station DJ or program manager for them to consider including in their rotation. Radio station playlists and programs
are intrinsically connected to advertising revenue. The type of music
played at a particular time correlates with likely audience demographics
determined by surveys. These correlations are used to set advertising
rates and sales strategies. This is a classic model of the “audience commodity” described by Dallas Smythe (McGuigan and Manzerolle, 2013).
For Smythe, the raison d’être of radio and TV stations was to create and
tailor programming in order to develop and retain an audience. The
audience becomes a commodity that is sold to advertisers.
There is a crucial difference between broadcast radio and SMuS, however.3 In the latter, the traditional “push” design of broadcast radio is
replaced with a “pull’’ design where users are able to initiate the delivery
of specific songs and playlists (Trecordi and Verticale, 2000; Kendall and
Kendall, 1999). A detailed explanation of push vs pull is beyond the
scope of this essay, but it is important to point out that the bidirectional
information flow of pull not only facilitated the “on demand” media
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revolution, but also of Web 2.0, itself. With the ability for users to make
requests and initiate delivery, content providers such as Pandora do not
have to create programming for users in the hopes that they will be
able to sell their attention to an advertiser. Instead, users create their
own programming from a library. The catch is that in all pull technologies, the gateway application, for example Pandora, is also a surveillance
device that directly monitors and records each user’s behavior.
Online streaming media services have realized that such choices
represent a set of collectable and analyzable behaviors that not only
allow providers to refine their own recommendation algorithms and
marketing strategies, but also to package and resell these behaviors to
third parties. Numerous scholars have critiqued the labor implications
of user-generated content and prosumption (Scholz 2012). But the political economic issues associated with making choices about listening and
watching are more subtle. Consuming media has usually been framed as
a leisure activity or unproductive labor, that is, labor that does not produce a good with exchange value. However, in the case of SMuS, where
listening requires input from a user, behavior resembles something like
the subjective immaterial labor that underpins cognitive capitalism
(Fuchs, 2011). Cognitive capitalism holds that ideas and thoughts can
be commodities with use and exchange value. “There is currently extensive global competition to attract the best brains,” writes Larsen, and
“[k]nowledge becomes a strategic force of production and an important
commodity” (2014, p. 161).
Related to this is selection power and selection labor. In his book
Human Information Retrieval, Julian Warner posits that selection power
is “the human ability to make informed choices between objects or
representations of objects” (2010, p. 17). Warner is referring to how recommendation algorithms model human behavior. In SMuS, algorithmic
recommendation is a crucial part of the listening experience. Given
a user’s choice of two songs, for example, an algorithm will choose a
third song that it thinks the user will like. It is important for the algorithm to be correct because that will improve the quality of the user’s
experience and keep them using the service. The user can affirm or
deny the selection (e.g. thumbs up or thumbs down), which provides the
algorithm with additional information so as to make better decisions in
the future. In the case of recommendation, the results of an information retrieval algorithm, at best, will represent the selection power of an
individual or group of individuals. It is a property of human consciousness and represents a variety of human experiences and desires. Selection
power is produced by selection labor, which can be understood as the
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mental work of memorization and recall (Warner, 2010, pp. 27 and
31). Psychologically speaking, selection labor would necessarily represent both tacit and explicit knowledge and is therefore only partially
explicable. Selection labor can be construed as a code for a wide variety
of human experiences. When transformed into selection power, these
experiences produce outcomes that are desirable for a person, but often
not easily predicted by machine. In order for these “selection machines”
to do what people do, they observe, record, and analyze the behaviors
of the users themselves. It is an interesting twist on the free labor issue.
User input is utilized to build algorithms that enhance the service’s user
experience by creating a better product. These algorithms are shadowy
versions of our experiences and knowledge expressed as selections we
make actively, but often intuitively. The question is: how important is
this musical experience and knowledge?
Music is often considered entertainment or, as neuroscientist Steven
Pinker (1997) has said, “auditory cheesecake,” but we know that it is
much more. As a species we have always exhibited distinctly musical
behaviors (Mithen, 2005). We sing and dance, and we do these activities
alone and in groups. We have an innate desire to be musical. As a human
universal, music is arguably central to the development and survival of
our species. Archeologist Steven Mithen (2005) writes that before there
was a spoken language, there was an advanced communication system
involving complex and holistic vocalizations that enabled our ancestors to hunt, reproduce, and socialize. It is from this system that both
language and music were borne. Given an opportunity to fade away in
the shadow of language’s formidable ability to communicate thoughts
and ideas, music held its ground. The question is: why? One answer is
that music allowed us to do things that were important to us, and for
which language was not particularly well suited. Language, while great
for organizing a hunt, perhaps falls short in expressing the exuberance
that comes with its successful conclusion. The importance of music in
our lives has not changed over the millennia, even if the way we engage
with it has.
Erik Clarke writes that “music affords dancing, worship, coordinated
working, persuasion, emotional catharsis, marching, foot-tapping, and
a myriad other activities of a perfectly tangible kind” (2005, p. 38).
Challenging ideas that listening and musical experiences are passive,
Joel Krueger argues that music is something we are always seeking.
Music, Krueger writes, “is a crucial tool for cultivating and regulating
our social life. Without music, our life – including our ability to sensitively relate to and communicate with others – would indeed change
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dramatically” (2011, p.3). These are claims that online social networks
would love to make. The music industry never has to create a demand
for what it sells, as we will never stop wanting and needing to be musical.
They only need to convince us that the product they’re selling and the
way we access it is what we want.
The materialization of music by means of notation and recording has
had a profound influence and effect over musical practice, especially in
capitalist economies. Jacques Attali writes that “music, an immaterial
pleasure turned commodity, now heralds a society of the sign, of the
immaterial up for sale, of social relation unified in money” (1985, p. 4).
He argues that material physical formats such as LPs, CDs, musical scores,
and piano rolls, allow us to exercise political and financial control over
what music is and how it can be used. “Wherever there is music,” he
says, “there is money” (Attali 1985, p. 3).
Streaming music services eschew the notion of materiality altogether.
In its place is the notion of “service.” These services mediate our access
to music and in doing so are situated in a position to observe how listeners behave. By moving to a service model, companies like Pandora,
Spotify, and Rdio provide access to a limited catalog when you want it,
where you want it. No need to manage an MP3 collection or purchase
and download music. It is pitched as a radio where a user gets to choose
the songs. These services have been widely criticized in recent years for
the small amount of royalties musicians actually make compared to
how frequently their songs get played (Krukowski, Chapter 6 in this
volume). The fact is, these services do not seem to make money. They
have relied on ads and subscriptions to generate revenue and not one
SMuS operating in 2013 made a profit.
When we listen to music on a SMuS, we make choices about what
we want to hear. These choices reflect who we are, how we feel, what
we believe. Our musical tastes have developed over years of personal
reflection and social interactions. We have learned how to use music to
make ourselves feel better and to create social bonds. Christopher Small
coins the term “musicking” as a verb that describes a diverse collection
of activities that comprise musical engagement (Small 1998). Small proposes that being musical involves not just performing and creating, but
also listening and sharing. Listening is not a capricious activity. In fact,
listening preferences develop over time and reflect important individual
characteristics and social choices that represent who we are.
Natasha Singer’s article “Listen to Pandora, and It Listens Back”
describes a new solution to an old problem: how can SMuS make money
from our desire to be musical (2014b). One solution is to commodify
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our musical identity as it is defined by the choices we make when we
listen to music online. This is important because most of us don’t think
about our musical identity, how important it is, or how much personal
information it potentially represents. Singer relays Pandora’s stance that
such data collection and analysis will be used for behaviorally targeted
advertising similar to practices of Twitter and Facebook. She quotes a
Pandora scientist who says, “we have [analysis] down to the individual
level, to the specific person who is using Pandora ... [w]e take all of these
signals and look at correlations that lead us to come up with magical
insights about somebody” (2014b). Singer writes:
People’s music, movie or book choices may reveal much more than
commercial likes and dislikes. Certain product or cultural preferences
can give glimpses into consumers’ political beliefs, religious faith,
sexual orientation or other intimate issues. That means many organizations now are not merely collecting details about where we go and
what we buy, but are also making inferences about who we are (2014b).
There is considerable evidence to support Singer’s claim. Music psychologists have long found clear evidence that what we listen to can
accurately predict specific personal demographic details and emotional
states. We listen to music for a variety of reasons and how, when, and
what we listen to can reveal a lot about who we are, how we feel, our values, and our beliefs. MacDonald et al. (2002) contend that music “plays a
fundamental role in the development, negotiation, and maintenance of
our personal lives” (2002, p. 462). Research also indicates that for young
people music is an important “badge of identity” that promotes development and maintenance of social groups (Hargraves et al., 2002). The
“sense of self” is a complex psychological construct that develops over
time and is subject to constant revision and modulation. Music plays a
significant role in this development.
A study by North and Hargreaves (2007) found numerous correlations
between subjects’ musical preference and lifestyle details including
moral and political beliefs, and attitudes about relationships and criminal
behavior. Rentfrow and Gosling (2011) found that musical preference
is the most common topic of conversation when two people are trying
to get to know each other and that people are able to form very accurate assessments of the personality of others based only on knowing
their musical preferences. Rawlings and Ciancarelli (1997) were able to
show clear and distinct associations between gender and personality
types (scales measuring extraversion and openness) and musical styles.
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Numerous studies explore and find strong connections between listeners’ emotional states and musical preferences (Juslin and Sloboda, 2010).
Moreover, Greasley and Lamont (2006) show that the more important
music is to a listener, the stronger these associations are. While results
like these are somewhat intuitive, it is unclear whether or not the average SMuS listener is aware that such associations are possible. The case
for musical privacy hinges on listeners’ appreciation and valuation of
their musical identities and how they can prevent personal information
from either being collected against their wishes or being used in ways
they do not want. It is reasonable to expect that loss of a loved one, for
example, may influence the music you listen to. It is also reasonable to
expect that you should be allowed to mourn in private, if you so wish.
Pandora’s Privacy Policy is vague about how it uses “Listening Activity”
information. The relevant section reads:
When you use the Service, we keep track of your listening activity,
which may include the number and title of songs you have listened
to, the songs that you like (thumb up) or dislike (thumb down), the
stations you create or listen to, the number of songs you skip, and
how long you listen to a station (Pandora, 2013).
It does not say that your listening history will be subject to algorithms
and classifiers in an attempt to create personality profiles that can be
sold and used for reasons you never intended. Nor does Pandora say
what they will do with this data, or if personal identities are protected.
Spotify is more detailed and explains what they collect and what they
do with it.
When you use the Service, we automatically collect certain information, including: (i) information about your type of subscription and
your interactions with the Service, including with songs, playlists, other
Spotify users, Third Party Applications and advertising, products and
services which are offered, linked to or made available on the Service.
To personalise your experience, we may share some information we have collected about you with providers of Third Party
Applications, such as high-level geographic information, your
musical preferences, settings and technical data. However, we take
precautions to prohibit Third Party Application providers from
attempting to identify you by using the information we provide
to them or by collecting additional information without your
consent (Spotify, 2014b).
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While this is more reassuring, Spotify is later very clear that they reserve
the right to sell your information.
Consumers have the right to clearly understand how their musical
identities are being used. More importantly, we have the right to opt out
of data collection. While our musical identities may not seem as important as social security numbers, health records, or banking information,
they nevertheless deserve protection. As companies like Pandora and
Spotify work to extract, bundle, and sell our information, we need to be
aware of what’s at stake.
In her analysis of the Jamaican street dance, Mann invokes two key
concepts: cultural intimacy and the exilic space. Cultural intimacy, Mann
writes, “arises from practices that embody both self-knowledge and selfrepresentation, wherein the self is collectively defined. This intimacy
allows marginalized people to affirm as positive the shared traits, situations, and actions that are designated negative by broader society” (Mann,
forthcoming, p. 4). Cultural intimacy is a set of traits that simultaneously
creates closeness within a marginalized group and distance between this
group and powerful outsiders who pose a threat to the group (Mann,
forthcoming, p. 4). The exilic space allows cultural intimacy by protecting the group from being observed and allowing members to act openly
in a way that promotes intimacy. Mann examines how “increased visibility on globally networked media platforms can harm marginalized
communities and their ability to celebrate their identities through various performance practices” (Mann, forthcoming, p. 4). She goes on to
say, “marginalized people need the power to exclude as much as the
power to include” (Mann, forthcoming, p. 4).
I argue that opacity of privacy protections in SMuS creates significant
ambiguity as to what kind of space online listening really is. In the
most dangerous scenario, SMuS listeners might believe they are in an
exilic space and act openly and inclusively as members of a marginalized group. Greater care needs to be taken to ensure that listeners are
aware their behaviors are subject to hegemonic observation with possible damaging consequences. We need to reframe online listening as
prosumption, meaning that listeners are generating content as they
consume content. This content has exchange-value in that it can be sold,
but more importantly this content has the capacity to reveal highly
personal and identifying information.
Furthermore, making choices about what we listen to is a form of
commodifiable labor for which listeners are not compensated. It is
the conversion of “leisure time” into “work time” as our personal
experiences become products that have use-value (in that they refine
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algorithms) and exchange-value (in that they can be sold directly or
indirectly to third parties). Listeners become estranged laborers as they
are separated from the products they create. Listening to music has
become synonymous with consumption largely because we have let ourselves believe that music is a good produced by labor and has a value
associated with this labor. It becomes intrinsically connected to formats
that reinforce this quality of a private good. Much has been said about
how digital formats recast music as a public good by imbuing qualities
of non-excludability and non-rivalry. But to confuse music with its
medium of transmission (formats or services) is a fallacy of misplaced
concreteness and avoids critical humanistic issues. In the case of music
we have to resist treating listening as an exercise in material engagement, embrace Small’s musicking, and appreciate that music is not a
thing, but a fundamental and critical human activity.
Notes
1. Significant work has been done in the last ten years to raise public awareness
about the implications of sharing information on social networks. In addition, there are frequent stories of people experiencing negative repercussions
(e.g. losing a job, being suspended from school) due to comments they have
posted online. This highlights an important aspect of prosumptive privacy,
which is that users can opt not to produce content they feel would put them
at risk.
2. There are other reasons as well, such as optimizing a service to enhance user
experience and satisfaction.
3. It is important to recognize that broadcast radio can stream their content
online. In my argument, I am making a clear distinction between any form
of media delivery that is essentially push versus those that are pull. Streaming
music services as I am discussing them are therefore defined by a user’s ability
to initiate content delivery.
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