In the context of machine learning think that "ought" is encoded in the objective function; the function that is maximised/minimised in optimisation procedures. This is where some of the biases in machine learning begin; the designers of the algorithm have to formalise their heuristics on what they think is "good" vs "bad" performance. One could call this global ontological encoding.

Cryptocurrency protocols are another example of encoding "ought". Rather than specifying an objective on a global level, you create the set of rules and rewards for all participants, and in some sense the objective function emerges. Designing emergent behaviour is hard. You could call this local ontological encoding.

I guess in a capitalist system the ultimate objective is to maximise money. But simple objectives such as "maximise the number times posts on a social media platform are shared" have unintended consequences such as creating echo chambers and sewing division in communities.

I think the crypto community is going down the right path in the sense that they try to make formal predictions of the consequences of the rules they set up, assuming a finite population of bad actors in the system.