(read Part 1 here)
I have seen it many times while watching chess commentators (typically, Grandmasters of the highest level) performing game analysis in real time. These GMs will be considering different possibilities for both sides and, infrequently, when the situation becomes too complex and unclear, say something like, “Hey, let’s check with the chess engine now…. Oh, it gives a strong advantage to White, but I don’t see why…. It says to do… WHAT?! And then… WHAT?! No…. these are not “human-like moves”, the players will not do that. This is too deep and machine-like…”.
The truth is that even the strongest Grandmasters often feel like little children when comparing their own analysis to that of a machine. But this is exactly why they are using machine analysis!
Lucky for chess, nobody suspects that “Stockfish” or “AlfaZero” have some ulterior motives, biases, don’t like some of the players, or wants to take advantage of somebody. Chess engines are considered to be fast, powerful, accurate, and objective analysis and decision-making tools capable of finding the best solution for any situation and being useful to us by simply being better than us. And nothing else.
And this is exactly how the future AI governments should look like: fast, powerful, accurate, and objective analysis and decision-making TOOLS capable of finding the best solution for any situation and being useful to us by being better than us. And nothing else.
Machine learning (ML) might already offer a possible approach needed to build and test such an “AI governance engine” and create the entire democratic election process using ML’s normal training and testing approach and steps:
- Provide the “governance engine” with a training dataset of historical or other examples that are of high value to us and explain how to classify them (for example, “bad” or “good”). Cover important social, economic, judicial, cultural, and educational fields. For example, imagine thousands upon thousands of statements or questions along with their classifiers/answers presented like this:
- “Rosa Parks rejected bus driver James F. Blake’s order to relinquish her seat in the “colored section” to a white passenger. Was she right or should she have stayed in the colored section?”. The answer: Rosa Parks was right. The driver was wrong.
- Or, “greater investments in children education” are good. Cutting these investments is bad.
- Cutting forest in Amazon delta is bad. Reducing industrial water and air pollution is good.
We have tons of examples like this from our past and present.
- Keep another dataset of examples with answers for testing. We will use it later to verify that the engine works well.
(Comment: The general population should take part in creating the above list of Q&A. Millions of people can contribute to it. This will allow the people to have a very direct impact on the training and selection of their own government instead of choosing the best available but imperfect candidate) Continue reading