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@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/