This is a fine draft, clever and engaging, somewhat irritating, as it should be. The lesson in the power of metaphors is also powerful. There's no particular relationship preexisting between the two forms of disclosure you chose to analogize, but having made the metaphorical connection, it then began to control the direction of your argument. If you had started from an environmental rather than a financial comparison, treating the disclosure of "algorithms" like the disclosures of hazardous chemicals in the workplace, or effluent disclosures from industrial discharge, you would have found a closer functional basis for comparison between two forms of regulation, and have reached different, though similar, rhetorical postures in your argument. You might try that, for the exercise.

But the primary route to improvement, I think, is to remove the factual misunderstanding that is the root of the argument. With respect to many "machine learning" applications, including the forms of recommendation and behavioral-cueing technologies you are discussing, there "algorithm" to be disclosed is basically trivial. Most ML, or even less descriptively "AI," structures depend for their effectiveness on their "training data," not on the executable computer programs, which are rather primitive routines, whose interconnections in "neural networks" depend not on the simple "algorithms" but on the sequence of data fed as raw material into those programs. What gets "disclosed" in the arrangements you have in mind is of no use in explaining the emergent properties of the system that contains this code.

About ten years ago I began receiving a few communications a year, first in the single digits then in the dozen range, form people (almost always trained at MIT) raising the same question:

Have you and Richard [Stallman] considered how to make free software principles work for machine learning? The source code of the programs doesn't do you any good in understanding or modifying the system: you need to have some copyleft that applies to the training data.

I always responded by saying that this was indeed a problem, and that it created an intractable subset of the general data licensing problem, which we hoped we could eventually solve on its own terms. In the latter part of the decade I would end each annual SFLC conference at CLS promising that "next year" we would discuss the issue. But by the time people knew it was there, a whole bunch of non-technical law and policy types, like Marc Rotenberg of EPIC, had invented "algorithmic transparency" as a policy prescription, and the bullshit-to-signal ratio climbed towards infinity.

Drop the assumption that when you know "the algorithm" you know anything. Assume instead that such disclosure is non-informative. Now what is your prescription?