Somehow, back in 2017, I found myself at a small workshop on the ethics autonomous robotic systems at the University of Zurich in Switzerland. (If you are interested, that series of workshops culminated in this report on how to evaluate the risk of robots in the security context). At that early workshop, though, I was an AI novice. I had been invited because I had an operational background in remotely piloted aircraft, not because I knew anything about AI.

So, there I was, sitting across the table from Ron Arkin. He talked about programming an “ethical governor” into lethal autonomous weapons systems. “But Ron,” I asked in the impetuousness of youth, “how could you possibly program an ethical governor given that AI is a “black box,” the inner workings of which are opaque to its designers?” (Ok, I probably didn’t phrase it exactly like that, but that was the gist).

In machine learning—the kind of AI that has become popular since about 2012—the neural network can be so complex that even the developers who design and train it are unable to trace how the system moves from input to output.

The Georgia Tech professor looked at me and blinked for what felt like several minutes, but what was probably a much more socially acceptable period of time. “You know, Joe,” he said, “not all AI is a black box.”

Back then, I just had no idea.

You see, there is a history of AI that comes before the machine learning revolution that began in 2012 and certainly before large language models like ChatGPT, Claude, and Gemini.

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