The systemic moral imperative seeks the distribution of power over its concentration, and thus the reduction of inequities of power. Money is usually a good proxy for power, so it's tempting to regard any redirection of money to the preexistingly unwealthy as moral. But this is both a dangerous conflation of cause and effect, and an attractive nuisance of potentially misleading measurement.  

In fact, the most common nominal redistributions of money in a functionally self-defending power-structure are likely to be ones that specifically do not meaningfully distribute power. Capitalism's idea of charity is billionaires bestowing heroically magnanimous gifts. The recipients of this benevolence do benefit from it, but they do not generally become independently powerful themselves as result. And one of capitalism's favorites forms of structural redistributions of money is the lottery. Lotteries, by which I mean all general systems that assign selective benefits to a minority of the disempowered via processes that are either literally random or effectively random because they are out of the recipients' control, transfer money without conferring agency. Government lotteries usually compound this flaw by appealing to the disempowered and thus acting as a regressive tax, as well.  

Jackpot-weighted lotteries, like Mega Millions and Powerball, have one more trick, which is that their biggest prizes can only be portrayed as redirecting money to the unwealthy by disingenuously selective definitions. Any individual jackpot winner is almost certain to have been among the unwealthy before their windfall, so any economic metrics that attribute the win to the collective unwealthy will look superficially progressive. But of course the actual effect is that the winner is moved from the category of the unwealthy to the ranks of the wealthy, at least nominally. The collective state of the unwealthy is unchanged. The power of billionaires is not threatened by the annointment of one more, particularly if the new one gets money without any of the other entitlements that usually help the rich stay rich, and is thus likely to either fall back out of the category of the wealthy by their own mismanagement, or at least spend their money on predictable signifiers of wealth and thus offer no systemic disruption.  

A lottery is an algorithm, and of course the same moral calculus applies to all algorithms, particularly ones that operate directly as social or cultural systems. A music-recommendation algorithm is systemically moral if it reduces inequities of power among listeners and artists. Disproportionately concentrating streams among the most popular artists is straightforwardly regressive, but distributing streams to less popular artists is not itself necessarily progressive. A morally progressive algorithm distributes agency: it gives listeners more control, or it encourages and facilitates their curiosity; it helps artists find and build community and thus career sustainability. Holistically, it rewards cultural validation, and thus shifts systemic effects from privilege and lotteries towards accessibility and meritocracies.  

The algorithms I wrote to generate playlists for the genre system I used to run at Spotify were not explicitly conceived as moral machines, but they inevitably expressed things I believed by virtue of my involvement, and thus were sometimes part of how I came to understand aspects of my own beliefs. They were proximally motivated by curiosity, but curiosity encodes an underlying faith in the distribution of value, so systems designed to reflect and magnify curiosity will tend towards decentralization, towards resistance against the gravity of power even if they aren't consciously counterposed, ideologically, against the power itself. The premise of the genre system was that genres are communities, and so most of its algorithms tried to use fairly simple math to capture the collective tastes of particular communities of music fans.  

The algorithm for generating 2023 in Maskandi, for example, compared the listening of Maskandi fans to global totals in order to find the new 2023 songs that were most disproportionately played by those people.  

 

Or, to phrase this from the world into streaming data, rather than vice versa, there is a thing in the world called Maskandi, a fabulously fluttery and buoyant Zulu folk-pop style, and there is an audience of people for whom that is what they mean when they say "music", and their collective listening contains culturally unique collective knowledge. Using math to collate that collective knowledge can allow us to discover the self-organization of music that it represents. If we do this right, we do not need to rely on individual experts approximating collective love with subjective opinions. If we do this right, we support a real human community's self-awareness and power of identity in a way that it cannot easily support itself. There's no magic source of truth about what "right" consists of, which is the challenge of the exercise but also exactly why it's worthwhile to attempt. For 12 years I spent most of my work life devising algorithms like this, running them, learning how to cross-check the cultural implications of the results, and then iterating in search of more and better revealed wisdom.  

In general, I found that collective listening knowledge is not especially elusive or cryptic. Streaming is not inherently performative, so most people listen in ways that seem likely to be earnest expressions of their love. That love can be collated with very simple math. Simple math that produces specific results is good because it's easy to adjust and evaluate. You might argue, I suppose, that simple math, by virtue of its simplicity, does not establish competitive advantages. If music services all have the same music, and music players all have the same basic controls, then services are differentiated by their algorithms, and more complex algorithms are harder for competitors to replicate.  

I offer, conversely, the rueful observation that in the last 12 years no other major music service has developed a cultural taxonomy of even remotely the same scale as the genre system we built at the Echo Nest and Spotify, while all of them have implemented versions of opaque personalization based on machine learning. ML recommendations are an arms-race with only temporary advantages. The machines don't actually learn, they always start over from nothing. ML engineers, too, can be trained from nothing or bought from other industries, without needing special love. But machines that do not run on love will not produce it.  

In particular, ML algorithms tend to drift towards lottery effects. Vector embeddings, even if they are trained on human cultural input like playlist co-occurence, tend to introduce non-cultural computational artifacts by their nature. And thus we get things like this set of music my Spotify daylist recently gave me:  

 

You don't need to hear the music behind these images to guess that it's mostly aggressive metalcore, but if you happen to know a lot about metalcore you could also notice that you probably have not heard of most of these bands. I am not a big fan of this very specific niche of metal, personally, which is the first thing wrong with this set as a personalized result for me. Bad results aren't disturbing because they're bad. Algorithms don't always work, for many reasons.  

But as I scanned through these songs, I couldn't help noticing that they all sounded very similar. And as I poked through the artist links, trying to understand what this set of bands represents, I quickly realized that it doesn't. These bands are not all from any one place, they do not appear together on any particular playlists, their fans do not also like each other. They are not collectively part of a real-world community. Many of them have fewer than 100 monthly listeners, sometimes a lot fewer, and thus probably do not even individually represent real-world communities. They do appear to be real bands, rather than opportunistic constructs or AI interpolations, and in general they aren't bad examples of this kind of thing.  

But they didn't end up on my list by merit or effort. They ended up here because Spotify uses ML techniques to group songs by acoustic characteristics, and this is one of the inputs into the vector embeddings that produce recommendations for daylist, Discover Weekly and other ML-driven personalized playlists. Acoustic similarity isn't completely random on the level of Powerball, but it's not a cultural meritocracy, and it's not a model for giving artists or listeners agency. Picking unknown artists out of the vast unheard tiers of streaming music is not an act of cultural incubation or stewardship, it's a mechanism of control. There are thousands of bands who sound like this. If you are one of the almost-thousands who are not randomly on my list, there's no action you can take to change this. If any one band ever gets famous this way, and statistically this is bound to happen rarely but eventually, you can be pretty sure we'll hear about it in self-congratulatory press releases that do not feature everyone else left behind. One exception doesn't change the rules. Lottery exposure offers a fleeting illusion of access, but if you didn't build it, you can't sustain it, either. You might hope, if you are in one of these lucky bands that reached me, that millions of not quite metalcore fans also got sets like this on a Friday afternoon, but two Friday afternoons later these bands are still obscure, still isolated. Losing lottery tickets do not make you luckier, but worse, lucking into more listeners this way doesn't give you an audience with any unifying rationale or presence, or a community to join. You can't learn from randomness, you can only hold still and hope it somehow picks you again.  

This is exactly what the power-structure wants: listeners holding still to see what daylist tells them to listen to on Friday afternoon, artists holding still hoping to be chosen. Measure this control by money and it looks virtuous, taking a few streams from the most saturated songs and sprinkling them sparingly across the thirstiest. Measure it by alleviated thirst, though, and it evaporates. Or, rather, it condenses, but only into the reservoirs of the machine itself. Audit the beneficiaries and you might find that they aren't even random. ML's idea of the distribution of power is enough unpredictability to distract from its own motivations. My idea of the future of music is not a chaos engine printing rigged lottery tickets that mostly don't even pay for themselves. It's a future that we build. It's a future we could build faster with better tools, and algorithms can be those tools. But only if they are handed to us, with intelligible instructions, as we are in productive motion. Only if they are designed not to give us each little jolts of seemingly new power for which we can yearn, but to give all of us, together, currents of shared power with which our yearning can be expressed and redeemed.