Jacob Pleasants is an Assistant Professor of Science Education at the University of Oklahoma. Through his teaching and research, he works to bring issues that lie at the intersection of science, engineering, technology, and society into STEM education.
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In Computing Taste, Nick Seaver conducts an anthropological
study of the technologists who design algorithmic music recommendation systems. He
explores their ways of thinking and talking about music, taste, and computation to
better understand their technological design approaches. By highlighting the humans
behind the machines, Computing Taste shows how to think about
computer algorithms as sociotechnical systems.
Jacob Pleasants publishes book review of The Humans and Algorithms of Music Recommendation: A Review of Computing Taste (2022)
Streaming services such as Spotify have changed the way that many (if not most)
people listen to music. Those services do much more than just provide access to
massive searchable libraries; they actively shape what users listen to via their
recommendation systems. Music recommendation comes in many forms, from basic
suggestions to playlists to personalized radio stations,
all of which now rely on
algorithmic computing technologies. Much has been written about how those
technologies have transformed the media landscape and have created significant
issues for users [e.g., Computing Taste
Computing Taste is an anthropological study, based on field
work that Seaver conducted over several years in the early 2010s. He spent time
within a company he calls Whisper
and conducted interviews with a wide variety of
technologists across the industry. Seaver’s aim is not primarily technology
criticism (though he does offer critiques) but rather to illuminate the cultural
practices of those who design recommendation technologies. What do the designers aim
to accomplish through their work? How do they understand music? And perhaps most
centrally, how do they think about taste? Computing Taste
reveals that the answers to those questions are anything but simple.
This book is about those humans — people who occupy a paradoxical position within algorithmic systems and in discourse about them. Although they are often figured as unfeeling agents of rationality, they
describe themselves as motivated by a deep care for music. Like many of their
critics, the makers of music recommendation recognize that there is something
strange about their aspirations — a tension between the closed, formal rationality
of technology and the open, contingent subjectivity of taste.
Given the abundance of scholarship that examines and critiques algorithmic
recommendation systems, what does Seaver’s anthropological approach offer? When
analyzing these technologies, there is a tendency to either overlook the designers
entirely or to flatten them into simple caricatures. Computing
Taste challenges such perspectives by providing a far more complex portrait
– one that Seaver did not necessarily anticipate from the outset.
I came to Whisper looking to understand how people like Ed
and his colleagues thought about taste… I assumed that theories of taste would
map neatly on to techniques and data sources: if you thought that people liked
music because of how it sounded, then you’d make a system that analyzed audio
data; if you thought they liked music to fit in with their friends, then you
might try to acquire social network data… But what I found at Whisper, and
across the industry, was not so straightforward.
Each of the six chapters of the book examines a facet of how the designers of music
recommendation systems think about music, taste, and the computational systems they
develop. Chapter topics include the need to assist users in navigating too much
music,
the capture of users’ attention, the mathematical nature of music, and the
space
of musical genres. Each chapter begins with an illustration of each theme
that draws from Seaver’s field work, followed by extended analysis and
interpretation that leverage constructs and insights from a broad body of humanities
scholarship. For example, in Chapter 1 he connects the origins of music
recommendation in the mid-20th century to
contemporaneous developments in computing, particularly the emergence of
Cybernetics. In Chapter 5 he compares the ways that technologists conceptualize the
space
of music genres to anthropological and sociological theories of cultural
spaces (such as those of Bourdieu). A theoretical thread that runs throughout the
book is the insight from Technology Studies that we ought to think of technologies
as sociotechnical systems: assemblages of human and technical components computer algorithms
too often obscures the human actors that
not only design the technical components but actively and continuously manage and
maintain them.
All six chapters bring insights, but several are particularly worth highlighting. The
first two chapters provide a careful and novel analysis on the tensions that exist
in how designers think about the captivating algorithms
that they create. In
Chapter 1, Seaver describes how recommendation systems are often positioned as
assisting audiophiles navigate the overwhelming catalog of music that they can
access. The concern that humans are practically drowning in a sea of information
(musical or otherwise) is, as Seaver points out, a perennial one, and he traces the
development of music recommendation services, since the early 1990s, that have
sought to help users navigate the problem of too much music.
Contemporary
algorithmic methods are largely continuous with their predecessors, though their
technical methods differ. Their designers employ similar rhetoric about the goal of
helping users navigate a bewildering array of options. The enduring promise is that
recommendation systems will help users not only find the music they already like,
but discover new music that they never would have otherwise encountered.
Yet for all the rhetoric about helping users navigate and make new discoveries, there
is also a discourse about using algorithms to get users hooked
on the music
service. This is not the algorithm of musical discovery, but the captivating
algorithm
of the attention economy. Much, of course, has been written on the
subject of attention-capturing algorithms [e.g., musical landscape
(Chapter 5 specifically addresses spatial metaphors of
music).
In Chapter 2, Seaver uses anthropological studies of traps as a lens to interpret how
designers approach the task of captivation (building on work that he previously
published in 2019). The key insight is that the designer needs a model of
that-which-is-to-be-trapped, because the trap needs to be able to lure its intended,
and only its intended, prey. This is why designers of recommendation algorithms need
to model their users, including the different kinds of users that might exist. The
trap metaphor is also instructive because it draws attention to the array of traps
that can be made. A trap need not kill or even injure or even extensively confine
its prey. In fact, recommendation algorithms might be best conceptualized as the
pastoral
form of trap:
Pastoral enclosure is a kind of nonlethal, ongoing
relationship aimed at growing the number of creatures enclosed through the
careful social organization of animal and environment… Like reindeer
pastoralists, the makers of recommender systems do not want to annihilate their
prey. They want to keep them around and grow their numbers, through the artful
production of captivating environments that at once limit and facilitate life.
From this perspective, the algorithmic trapper/designer is not a jailer working to
constrain human autonomy, but a persuader trying to coax music listeners to hang
around
This brief overview illustrates the approach that Seaver takes throughout Computing Taste. He draws connections between how the
technologies are designed, the designers’ beliefs and intentions, and humanities
perspectives that provide insight into how the pieces fit together. He does not
hesitate to critique the technologists’ perspectives and beliefs when critique is
warranted, drawing attention to inconsistencies, blind spots, and misguided
assumptions. At the same time, he does not set the designers up as antagonists
(like, say, Computing Taste shows how those technologists use their
beliefs to navigate the challenges and complications of their technical projects.
And while they may often be naïve about the social effects of the technologies they
create, the way that they think about their work is anything but simplistic or
cynical.
The domain of music brings its own peculiarities, but Computing
Taste offers many tools and insights that are useful for digital humanities
scholarship more broadly, especially for inquiries into now-ubiquitous algorithmic
systems. Perhaps the most wide-reaching contribution is its call to take seriously
the beliefs, ways of thinking, and ways of talking (including the metaphors) of
technology designers. Those ways of thinking and speaking matter a great deal
because they wind up being knitted into the technical systems they create. And yes,
they are worthy of scrutiny and critique, but they are also worthy of being
understood. As Seaver shows, they are often far more complex than they at first
appear.
Exemplifying this point is Seaver’s examination (in Chapter 4) of the use of machine
learning to create systems that can detect musical genre. Machine learning systems
are often described as black boxes
because it is extremely difficult to comprehend
why they produce the classifications (e.g., genre) that they do. And yet, Seaver’s
analysis reveals how machine learning remains a sociotechnical system. Human
designers decide not only what to feed into the machine learning algorithm, but
tune
its outputs so that they conform to their expectations. The ways that the
designers think about genre and hear music become intertwined with the algorithm.
They use their ways of thinking and listening to detect, interpret, and correct
unexpected results. The designers are also tuned
by their technologies as they
strain to listen for and comprehend what the algorithm is hearing.
Careful considerations of the complex and ongoing interactions between technology and designer need to be central to our analyses and critiques of algorithmic systems (machine learning or otherwise). Those systems are shaped and continuously maintained by the beliefs, approaches, and discourses of the technologists who develop them. A deeper understanding of those designers’ cultures can not only enrich our analyses and critiques, but also point the way toward alternative futures. What kinds of music streaming services might we have if they were built up from a different set of beliefs and metaphors about music, taste, and computing?