[Air-L] CfP: Clouds, Streams, and Ground (Truths): Developing Methods for Studying Algorithmic Music Ecosystems

[Allison Jerzak]

We are pleased to announce a Call For Papers for the conference, "Clouds,
Streams, and Ground (Truths): Developing Methods for Studying Algorithmic
Music Ecosystems <https://www.algorithmicmusicmethods.com/>," to be held at
the University of California, Berkeley, March 7-8, 2026.

"Fail fast, fail forward" echoes throughout Silicon Valley. The phrase
validates (and often financially rewards) companies who pursue rapid
technological development over more considered approaches. But this
future-oriented vision has also made these systems difficult to study: like
the metaphorical "stream," they are constantly in flux. One consequence:
digital music, streaming platforms, and cloud infrastructures have been
around for decades, yet scholars lack a consensus on how to study these
objects. Access to the past is often foreclosed by relentless pursuits of
digital futures.

Our aim is to bring together an interdisciplinary group of scholars,
researchers in the music industry, and legal practitioners to discuss the
challenges of studying these digital systems and develop ways to make them
more knowable. We are soliciting proposals for presentations (20 minutes +
10 minutes discussion) from scholars in musicology, critical data studies,
media studies, and related disciplines to contribute their perspective to
the conversation. Possible topics of interest include:

   -

   How does metaphorical language like clouds and streams shape how we
   perceive the affordances of different music technologies?
   -

   What kind of knowledge can we generate about these systems taking a
   historical approach? An ethnographic one?
   -

   Quantitative vs. qualitative: What can we learn about these systems by
   studying them at scale, and what can we learn from case studies?


   -

   What are the implications of a rapidly changing political economy of
   music? Have we seen comparable economic shifts in the past?
   -

   What can recent (or not so recent) litigation reveal about these
   companies or their technologies?
   -

   What kind of musical data is publicly available, and what can we do with
   it?
   -

   Most commercial music recommendation companies were developed in North
   American and European contexts. These systems were largely trained on
   popular music, but with an eye to universal applications. How might we go
   about mitigating bias from this training data? Is taking a universal
   approach to music recommendation and generation even possible?


The conference will feature keynotes by Bob Sturm (KTH Royal Technical
University), Anna Huang (MIT), and Chris White (UMass, Amherst), along with
roundtables with researchers in the music industry and the legal sphere. We
anticipate having financial support available to help defray the costs of
travel/lodging for accepted participants, particularly graduate students or
independent scholars.

If interested, please send proposals of 250 words to
conference at algorithmicmusicmethods.com by August 22, 11:59PM.

Program decisions will be announced no later than September 19.

Organizing committee: Allison Jerzak (UC Berkeley), Ravi Krishnaswami
(Brown University)

https://www.algorithmicmusicmethods.com/