Robot Ipsa Loquitur
Written By: Bryan Casey
Accidents are becoming automated. From self-driving cars to self- flying drones, robots are increasingly colliding with the world. One of the most pressing questions raised by these technologies—indeed, one of the great regulatory challenges of the coming era—is how the law should account for crashes involving these complex, automated systems. By now, many have weighed in. And, though responses vary, a tentative consensus has begun to emerge around the idea that tomorrow’s robots will pose formidable challenges to today’s negligence and design defect doc-trines.
This Article challenges that view. In sharp contrast to the prevailing wisdom, it argues that widespread debates over the so-called vexing tort problems raised by robots have overlooked a crucial issue: inference. Fault, after all, need not be shown by pointing directly to a faulty line of code. Like all facts, it can be proven indirectly through circumstantial evidence. Indeed, as the ancient negligence rule of res ipsa loquitur makes plain, sometimes an accident can “speak for itself.” Using the first robot accused of negligence as a case study, this Article shows how advanced data-logging technologies in modern machines provide richly- detailed records of accidents that, themselves, speak to the fault of the parties involved. In doing so, it offers the first wide-ranging account of how inference-based analysis can—and, in fact, already does—elegantly resolve liability determinations for otherwise confoundingly complex accidents. Having done so, it outlines steps that courts, practitioners, and policymakers can take to streamline fault determinations using an approach it calls robot ipsa loquitur. With trillion-dollar markets and millions of lives on the line, it argues that drastic calls by leading experts to upend conventional liability are ahistorical, contrary to tort law’s fundamental goals, and unnecessary to protect the interests of accident victims. A simpler, more productive approach would let the robot speak for itself.
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