3 principles for solving AI Dilemma: Optimization vs Explanation

Imagine your Aunt Ida is in an autonomous vehicle (AV) -- a self-driving car -- on a city street closed to human-driven vehicles. Imagine a swarm of puppies drops from an overpass, a sinkhole opens up beneath a bus full of mathematical geniuses, or Beethoven (or Tupac) jumps into the street from the left as Mozart (or Biggie) jumps in from the right. Whatever the dilemma, imagine that the least worst option for the network of AVs (Ed: Autonomous Vehicles) is to drive the car containing your Aunt Ida into a concrete abutment. Even if the system made the right choice -- all other options would have resulted in more deaths -- you'd probably want an explanation. Or consider the cases where machine-learning-based AI has gone wrong. It was bad when Google Photos identified black men as gorillas. It can be devastating when AI recommends that black men be kept in jail longer than white men for no reason other than their race. Not to mention autonomous military weapon systems that could deliver racism in airborne explosives.