While watching this video, I wondered: We’re using machine learning to earn money on the stock market and to make computers understand speech. Why not ethics?
Around @26:00 , Isaac talks about ways to develop AIs to control androids. He would like to use the safe approach of manual programming all the specific details to create an AI.
The “manual programming” path has been tried since 1960 and it’s now deemed a failure. The task is simply too complex. It’s like manually writing down all possible chess positions: Even if you tried, you’d run out of time. Machine learning is the way to go.
Which means we have to solve a “simple” problem: Encode the rules of ethics. That is, a machine learning algorithm must check itself (or be checked by another algorithm) against a basic set of ethical rules to determine whether “a solution” to “a problem” is “ethical” (quotes mean: “We still have to figure out exactly what that means and how to put it into code”).
Just like intelligence, ethics is somewhat of a soft and moving target. Fortunately, we have a huge body of texts (religious, laws, philosophy, polls) which a machine learning algorithm could be trained on. To test this machine, we could present it with artificial and real life incidents and see how it rules. Note: The output of this algorithm would be a number between 0 (very unethical) and 1 (very ethical). It would not spit out solutions on how to solve an ethical dilemma. It could just judge an existing solution.
It’s important that the output (judgement) is checked and the result (how good the output was) is fed back into the algorithm so it can tune itself. Both output and feedback needs to be checked for the usual problems (racism, prejudice, etc.).
Based on that, another machine learning algorithm (MLA) could then try many different solutions, present those to the ethics one, and pick the best ones. At the beginning, humans would supervise this process as well (feedback as above). Eventually, the MLA would figure out the hidden rules of good solutions.
That would eventually lead to ethical machines. Which would cause new problems: There will eventually be a machine, very impartial, that “is” more ethical than almost all humans. Whatever “is” might mean, then.