When automobiles first started to appear alongside horse-drawn buggies, horses were the initial victims of the technology. They would not be struck by the slow-moving vehicles so much as be afraid into runaways. Sometimes the horses themselves suffered injury; other times it was property havoc and rambler injury as the terrified steeds trampled everything in their paths.
As cars got faster and more numerous, pedestrians began to plummet direct victim to moving vehicles, and it wasn’t long before rules of the roadway, and product and tort liability laws, imposed order to evade carnage. Still, even today we have an ever-growing number of distracted and inept drivers turning our crowded highways into a dystopic model of real-life Frogger.
Enter the autonomous automobile. All the benefits of driving, without having to steer! Proponents of driverless cars believe that autonomous technology will make cars safer and guide to a 90% reduction in accident frequency by 2050, as more than 90% of automobile crashes are caused by motorist error.
There is certainly no shortage of news stories about injuries and fatalities resulting from drunk or distracted driving, and other accidents caused by drivers’ behavior. Text your friends or binge watch Black Mirror; with an autonomous automobile, it’s OK. Or is it? All goes well until your AV decides that the rambler in front of you isn’t really there, or mistakes the debris trailing a garbage truck for lane advice, and steers you into a concrete barrier.
Some companies are closer than others to completely driverless vehicles, but the edge case driving situations are still a contest, as we sadly found not long ago when an av knocked
a rambler walking her bicycle across a dark freeway in Arizona. Although there was a motorist present who could have taken the controls, she didn’t. One can hardly blame her inattention, for the whole point of autonomous driving technology is to allow for, if not encourage, drivers to disengage from the task.
The “autonomous automobile paradox” of inducing drivers to disconnect because they are not needed most of the moment is confounding. At least in the interim, until autonomous systems can reliably attain good than a 98% safety rate (roughly the rate of humankind drivers), autonomous systems will need to be supplemented by a humankind motorist for emergencies and other unexpected situations.
What happens and who is at fault when an accident occurs during or even after this transition period? Before the advent of autonomous automobile technology, automobile accidents would typically invoke one of two legal theories: motorist negligence and manufacturers’ products liability. The legal theory of negligence seeks to hold people accountable for their actions and leads to financial compensation from drivers, or more commonly their insurance companies, for the drivers’ conduct behind the wheel. Products liability legal theories, on the other hand, are directed at companies that make and vend the injury-causing products, such as defective breeze bags, ignition switches, tires, or the cars themselves. Applying current legal theories to autonomous automobile accident situations presents many challenges.
Suppose artificial intelligence (AI), or whatever makes an automobile autonomous, fails to detect or correct for a slippery curve. Perhaps a coolant leak from some automobile ahead covers the roadway with antifreeze, which can seen by the humankind behind the wheel, yet is all but invisible to the AI system. If the AV has manual override controls and an accident occurs, is the motorist at fault for not taking the controls to evade the crash? Is the automobile manufacturer at fault for not sensing the roadway condition or correcting for it? If both, how should fault be apportioned?
If a conventional automobile was involved, the case against the motorist may depend on proof that their behavior fell below an applicable grade of care. Not having one’s hands on the steering wheel would most likely be considered negligent behavior with such an automobile, and likely, so would being distracted by texting on a smartphone. But the self-driving feature of an autonomous automobile by its very nature encourages motorist inattention and lack of engagement with the controls. So would we be willing to find the motorist at fault in the above instance for not taking over?
automobile manufacturers have expressed non-identical views on liability.
As to the manufacturer of a conventional automobile, liability might depend on whether a system or part was defective. a conventional automobile in good condition, with no suspension, brake or steering defects, would likely allow the manufacturer to escape the brunt of liability in the above scenario. The manufacturer of an autonomous automobile with humankind override controls, however, might strive to shift at least some section of fault to the motorist, but would or should society allow that? The motorist might argue he or she reasonably relied upon the AV, but should the manufacturer instead be held responsible where the risk was visible and motorist intervention could have avoided the accident?
The outcome might differ if the automobile was completely autonomous and no humankind possibly could have intervened, but that automobile may be years away.
When such an av comes to mart, would, or should it be considered “defective” if it fails to detect or correct for the unexpectedly slippery surface? And if so, would it be considered defective merely because the failures occurred, or would proof also demand some showing of errors in the AI app? Given that AI algorithms can evolve on their own and be dependent on millions of miles or hours of training data, how would one prove a “defect” in the app? Would it be fair to hold the programmer or app supplier accountable if the algorithm at the moment of the accident differed substantially from the genuine, and the changes were effected by the AI algorithm having “taught” itself?
Another issue is the “hive mind.” One route AI could learn is by processing the collective experiences of other connected AVs, a process at one moment used by Tesla. But if a significant proportion of other AVs upload erroneous data that is acted upon, what then?
In light of these issues, and as technology moves toward finish regulate of the automobile with increasingly less humankind intervention, we may see the law evolve to place more emphasis on products liability theories and perhaps strict liability rather than negligence. It is not far-fetched that the price tag of an av of the future not only include the R&D and element costs, but an “insurance” element to cover the costs of accidents. Such an evolution would be consistent with the decreasing role of the humankind motorist, though it is somewhat inconsistent with an automobile manufacturer’s ability to exert full regulate over an ai system’s learning process, not to mention the driving environment.
In the present interim period when at least some humankind intervention is required, automobile manufacturers have expressed non-identical views on liability. Some, like Volvo, have publicly stated that they will accept full responsibility whenever one of their vehicles is involved in an accident while in autonomous method. But others, like Tesla, are attempting to shift liability to drivers when accidents happen by requiring some modicum of motorist engagement, even in autonomous method.
For instance, to activate the capability to pass other cars in autonomous method, drivers of Teslas once had to trigger the turn signal (Tesla recently announced a brand-new model that would dispense with this requirement). Having drivers perform this seemingly insignificant but deliberate action could assist auto manufacturers shift legal liability to the motorist. Performing that easy action not only tells the automobile to pass, but suggests the motorist has made a decision that the maneuver is safe and therefore is willing to, or should, accept responsibility for the consequences if it is not.
What about the ethics of the AI programming or training?
The underlying technology itself presents further complications to ascertain who is at fault. As alluded to above, one aspect of AI, good characterized as “device learning,” is that its behavior is more or less a “black box” developed from millions of a variety of inputs and cannot be well-understood as might a strictly math-based algorithm.
Put another route, we might be incapable of knowing exactly how the device decided to act as it did. In such an instance, if the AI box was negligently trained, or “trained” on an imitator rather than based on real-world driving, could the author of the imitator instead be held accountable for the box’s failure to handle the edge case scenario that resulted in the accident?
What about the ethics of the AI programming or training? a recent study found that current AI systems are perhaps 20% less likely to identify pedestrians if they are people of color. Was that due to the AI training on an insufficiently distinct subject base, or is there some other explanation? a recent survey conducted by MIT concluded that people ascribe a hierarchy to whose lives might be spared in edge cases where a crash is unavoidable and the request is not whether, but which, lives will perish. According to survey participants, humankind lives should be spared over those of animals; the lives of many should be spared over those of a few; and the youthful should be spared at the expense of the aged.
Interestingly, people also thought there should be a decision for someone pushing a stroller and observing traffic laws, the bottom line being that if an av is programmed according to such ethics, your odds of being knocked
by an av might increase significantly if you are a lone person jay-walking across a busy freeway. In the moral hierarchy of the study, being a cat, dog or lawbreaker is at the lowest stage of protection, though one must wonder how an automobile will be able to distinguish a humankind lawbreaker from a non-lawbreaker — real-moment connection to prison records? And what happens if, say, beast activist hackers modify the programming to prefer saving animals over people?
If the MIT survey is to be believed, such a hierarchy and variability exist today, only tucked away in the subconsciousness of humankind drivers rather than in machines. Think about that the next moment you cross the roadway.