When I taught my older daughter to drive, my (overly) nervous tendency to constantly kibitz caused her to blurt out: "You have to let me make my own mistakes."
As she drove my sportscar on a winding backroad with narrow lanes and no shoulder, and as she occasionally drifted toward the middle even though oncoming traffic could appear suddenly over a rise, I assured her that she was, in fact, not allowed to make any mistakes.
Yes, I would try to take a hands-off approach about school, friendships, soccer and so on. But driving? Nope. That was off-limits.
While I'm happy to report that, at age 32, she has never had an accident — not even a moving violation — the tension we worked through springs to mind as I think about the deployment of AI. There is a fundamental tension between having AI improve as fast as possible — to get as many benefits out to as many people as soon as can be done —and a legal system that will hold the AI accountable for each and every harm it causes, no matter if that loss is in the service of a long-term gain for society writ large.
The underlying tension between statistics and accountability goes even beyond the usual issues that accompany the rise of a revolutionary technology. So AI faces a hard question: When is good enough good enough?
Let's have a look.
The clearest example of the tension is probably in autonomous vehicles, where Tesla took the speed route while Google's Waymo took the careful one.
Tesla relies just on cameras and radar as the sensors in its AVs, largely because they are much less expensive than the Lidar that Waymo and others use, meaning that Tesla could deploy what it calls Full Self Driving (Supervised) quickly. (The "supervised" label refers to the fact that, while drivers are told they can trust their cars fully, they are legally required to stay alert and be able to take control of the car instantly.) CEO Elon Musk also encouraged aggressive use of the technology so he could gather as much data as possible on problems that needed to be ironed out before the cars could be fully autonomous.
The result is that Tesla's cars have logged nearly 13 billion miles in FSD mode — but also that there have been all sorts of complaints about problems with the software, as well as numerous accidents and even fatalities. Tesla has mostly avoided legal liability because of the fine print about drivers retaining responsibility for their vehicles even in FSD mode. Musk has said that problems are inevitable but that, in the long run, his rapid deployment of AI will reduce the total number of car accidents and fatalities.
While Musk has been promising that full autonomy was just around the corner for a decade, his vaunted fleet of robotaxis has logged just 380,000 paid miles without a safety driver in a car. He and his supporters still argue that his advantage in generating real-world data on driving, based on all the cameras and radar systems he has in the nearly 10 million Teslas on the road, will eventually make him the winner. But recent declines in the stock price for Tesla are being attributed to growing concern among his investors about his ability to deliver, after so many promises went nowhere. We'll see.
Meanwhile, Waymo took the go-slow approach, using a full array of sensors on the assumption that they would ride the exponential Moore's law cost curve and become inexpensive enough soon enough. Prices have, in fact, cut the cost of a Lidar from about $75,000 per vehicle to $7,500, and technologies are out there that could take the price down into the hundreds of dollars per car. Waymo has rolled out the cars cautiously enough that it only has about 4,000 robotaxis on the road, but they have logged 200 million paid, unsupervised miles — so more than 500 times Tesla's total. There have been glitches with Waymo, such as with misunderstanding signs warning of construction zones, but nothing like what Tesla has faced, so Waymo is the clear leader on AVs at this point.
Software developers take an approach like my daughter's: focusing on learning quickly even if the speed leads to some mistakes along the way. The legal system, however, takes my approach: Even if fast learning reduces the total number of lives lost and total damage that occurs over the lifetime of an AI, those who deploy it are liable for each and every mistake along the way.
Basically, the legal system says: Don't tell me that you constructed a model and optimized it for gains to society writ large. If an AI hurt my parent/child/friend, I demand accountability.