Can AI be used to reverse engineer a black box? A while back I posted on the Reverse Engineering site about an audio DSP system whose designer had passed away and whose manufacturer no longer had source code (but the question was deleted). Basically, the audio filter settings are passed from a Windows program to the DSP device presumably as coefficients and then generic descriptions of those filters (boost/cut, frequency and bandwidth) are passed back from the box to the software - but only if it somehow recognizes the filter setting. 

I want to be able to generate the filter settings separately from the manufacturer software, so I need to know how they are calculated. I've not been able to deduce how this is structured from observing the USB communication that I've gathered. So, I wonder if AI could do this.

How would I go about creating an AI to send commands to the box (I know how to communicate with the box and have a framework for how these types of commands are phrased) and then look at the responses to either further decode the system and/or create an algorithm for creating filters?

The communication with the DSP mixer box is basically via "Serial" commands and although it uses a USB port, there is a significant bottleneck inside the command control system in the mixer box. Any attempts to reverse engineer may encounter problems based on the sheer amount of time that it would take to compile enough data. Or not.
Yes this is entirely possible. As was previously mentioned, complex connectionist systems are often thought of as black boxes(despite us being able to "look in" the box given enough computation and analysis) because of the difficulty in understanding learning and the networks ultimate decision making.

Here, we can model the problem as such: given an input of filter settings(and presumably some information about the audio), predict the target descriptors as an output. All you really need to do is generate a dataset from the program and then train it in a multi-label classification context to predict the output descriptors.