On Robots Killing People

The robot revolution began long ago, and so did the killing. One day in 1979, a robot at a Ford Motor Company casting plant malfunctioned—human workers determined that it was not going fast enough. And so twenty-five-year-old Robert Williams was asked to climb into a storage rack to help move things along. The one-ton robot continued to work silently, smashing into Williams’s head and instantly killing him. This was reportedly the first incident in which a robot killed a human; many more would follow.

At Kawasaki Heavy Industries in 1981, Kenji Urada died in similar circumstances. A malfunctioning robot he went to inspect killed him when he obstructed its path, according to Gabriel Hallevy in his 2013 book, When Robots Kill: Artificial Intelligence Under Criminal Law. As Hallevy puts it, the robot simply determined that “the most efficient way to eliminate the threat was to push the worker into an adjacent machine.” From 1992 to 2017, workplace robots were responsible for 41 recorded deaths in the United States—and that’s likely an underestimate, especially when you consider knock-on effects from automation, such as job loss. A robotic anti-aircraft cannon killed nine South African soldiers in 2007 when a possible software failure led the machine to swing itself wildly and fire dozens of lethal rounds in less than a second. In a 2018 trial, a medical robot was implicated in killing Stephen Pettitt during a routine operation that had occurred a few years earlier.

You get the picture. Robots—”intelligent” and not—have been killing people for decades. And the development of more advanced artificial intelligence has only increased the potential for machines to cause harm. Self-driving cars are already on American streets, and robotic "dogs" are being used by law enforcement. Computerized systems are being given the capabilities to use tools, allowing them to directly affect the physical world. Why worry about the theoretical emergence of an all-powerful, superintelligent program when more immediate problems are at our doorstep? Regulation must push companies toward safe innovation and innovation in safety. We are not there yet.

Historically, major disasters have needed to occur to spur regulation—the types of disasters we would ideally foresee and avoid in today’s AI paradigm. The 1905 Grover Shoe Factory disaster led to regulations governing the safe operation of steam boilers. At the time, companies claimed that large steam-automation machines were too complex to rush safety regulations. This, of course, led to overlooked safety flaws and escalating disasters. It wasn’t until the American Society of Mechanical Engineers demanded risk analysis and transparency that dangers from these huge tanks of boiling water, once considered mystifying, were made easily understandable. The 1911 Triangle Shirtwaist Factory fire led to regulations on sprinkler systems and emergency exits. And the preventable 1912 sinking of the Titanic resulted in new regulations on lifeboats, safety audits, and on-ship radios.

Perhaps the best analogy is the evolution of the Federal Aviation Administration. Fatalities in the first decades of aviation forced regulation, which required new developments in both law and technology. Starting with the Air Commerce Act of 1926, Congress recognized that the integration of aerospace tech into people’s lives and our economy demanded the highest scrutiny. Today, every airline crash is closely examined, motivating new technologies and procedures.

Any regulation of industrial robots stems from existing industrial regulation, which has been evolving for many decades. The Occupational Safety and Health Act of 1970 established safety standards for machinery, and the Robotic Industries Association, now merged into the Association for Advancing Automation, has been instrumental in developing and updating specific robot-safety standards since its founding in 1974. Those standards, with obscure names such as R15.06 and ISO 10218, emphasize inherent safe design, protective measures, and rigorous risk assessments for industrial robots.

But as technology continues to change, the government needs to more clearly regulate how and when robots can be used in society. Laws need to clarify who is responsible, and what the legal consequences are, when a robot’s actions result in harm. Yes, accidents happen. But the lessons of aviation and workplace safety demonstrate that accidents are preventable when they are openly discussed and subjected to proper expert scrutiny.

AI and robotics companies don’t want this to happen. OpenAI, for example, has reportedly fought to “water down” safety regulations and reduce AI-quality requirements. According to an article in Time, it lobbied European Union officials against classifying models like ChatGPT as “high risk” which would have brought “stringent legal requirements including transparency, traceability, and human oversight.” The reasoning was supposedly that OpenAI did not intend to put its products to high-risk use—a logical twist akin to the Titanic owners lobbying that the ship should not be inspected for lifeboats on the principle that it was a “general purpose” vessel that also could sail in warm waters where there were no icebergs and people could float for days. (OpenAI did not comment when asked about its stance on regulation; previously, it has said that “achieving our mission requires that we work to mitigate both current and longer-term risks,” and that it is working toward that goal by “collaborating with policymakers, researchers and users.”)

Large corporations have a tendency to develop computer technologies to self-servingly shift the burdens of their own shortcomings onto society at large, or to claim that safety regulations protecting society impose an unjust cost on corporations themselves, or that security baselines stifle innovation. We’ve heard it all before, and we should be extremely skeptical of such claims. Today’s AI-related robot deaths are no different from the robot accidents of the past. Those industrial robots malfunctioned, and human operators trying to assist were killed in unexpected ways. Since the first-known death resulting from the feature in January 2016, Tesla’s Autopilot has been implicated in more than 40 deaths according to official report estimates. Malfunctioning Teslas on Autopilot have deviated from their advertised capabilities by misreading road markings, suddenly veering into other cars or trees, crashing into well-marked service vehicles, or ignoring red lights, stop signs, and crosswalks. We’re concerned that AI-controlled robots already are moving beyond accidental killing in the name of efficiency and “deciding” to kill someone in order to achieve opaque and remotely controlled objectives.

As we move into a future where robots are becoming integral to our lives, we can’t forget that safety is a crucial part of innovation. True technological progress comes from applying comprehensive safety standards across technologies, even in the realm of the most futuristic and captivating robotic visions. By learning lessons from past fatalities, we can enhance safety protocols, rectify design flaws, and prevent further unnecessary loss of life.

For example, the UK government already sets out statements that safety matters. Lawmakers must reach further back in history to become more future-focused on what we must demand right now: modeling threats, calculating potential scenarios, enabling technical blueprints, and ensuring responsible engineering for building within parameters that protect society at large. Decades of experience have given us the empirical evidence to guide our actions toward a safer future with robots. Now we need the political will to regulate.

This essay was written with Bruce Schneier, and previously appeared on Atlantic.com.

3 thoughts on “On Robots Killing People”

  1. As a CTO in the robotics industry, I find the discussion around the potential psychological impact of job losses due to automation both relevant and poignant. While our technological advancements undoubtedly bring about increased efficiency and innovation, it’s crucial to acknowledge the human aspect of these transitions.

    The idea that individuals might be driven to take their own lives following job losses emphasizes the need for a comprehensive approach to the integration of robotics in the workforce. It’s not just about developing cutting-edge technology but also about understanding and addressing the human implications of these advancements.

    In our pursuit of automation excellence, we must prioritize not only the technical aspects of our innovations but also consider the impact on individuals’ identities, financial stability, and social connections. As leaders in the robotics industry, we bear a responsibility to actively contribute to the creation of support systems, retraining programs, and open dialogues that address the holistic challenges posed by these changes.

    Let’s steer the conversation towards solutions that not only optimize efficiency but also prioritize the well-being of the workforce. By fostering a culture of adaptability, providing resources for reskilling, and acknowledging the emotional toll of job transitions, we can pave the way for a future where technology enhances lives without sacrificing the human touch.

  2. This warning for me perfectly aligns with the precautionary principle—if an action or policy has the potential to cause harm to the public or the environment, in the absence of scientific consensus, the burden of proof falls on those advocating the action. In situations where a small number of deaths suggests the presence of robots capable of killing more, it becomes crucial to take the precautionary approach and investigate further. It’s a matter of recognizing the potential severity of the issue based on the available evidence and taking preventive measures to safeguard public health.

  3. I appreciate the perspectives shared by Bruce Schneier and Davi Ottenheimer, as they offer a unique and thought-provoking take on the impact of workplace robots. Their argument, although based on a set of specific data points, encourages us to consider cognitive biases and informal fallacies, fostering a deeper understanding of the topic.

    Examining the techniques employed in their argument, the mention of 41 recorded deaths from workplace robots between 1992 and 2017 raises awareness of potential safety concerns. While tragic, their emphasis on these incidents serves as a catalyst for a broader discussion on workplace safety and regulatory frameworks. This is especially true when they highlight the more than 40 deaths caused by Tesla in just the last few years, a tragic acceleration.

    The authors’ assertion that industrial deaths by robots may be underreported and under-regulated prompts us to reevaluate existing safety measures. While acknowledging the reported cases, they invite us to consider the effectiveness of current regulations, encouraging a constructive dialogue on how to enhance workplace safety.

    The article’s subtle suggestion that job losses from automation could have profound impacts, including potential mental health challenges, adds a human touch to the narrative. While acknowledging the complexities of economic systems, it prompts us to consider compassionate approaches and robust safety nets in the face of technological advancements.

    Given the extensive knowledge of the authors and The Atlantic’s commitment to journalistic integrity, their decision to publish this article speaks to their dedication to presenting diverse perspectives. Embracing such viewpoints fosters a healthy exchange of ideas, encouraging readers to engage in a constructive dialogue about the implications of robotics in the workplace.

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