AI has the potential to be substantially safer than people. Self-driving cars will cause accidents, but they will cause fewer accidents than people. Because automation will result in substantial safety benefits, tort law should encourage its adoption as a means of accident prevention. Under current laws, suppliers of AI tortfeasors are strictly responsible for their harms. A better system would hold them liable for harms caused by AI tortfeasors in negligence. Not only would this encourage the use of AI after it exceeds human performance, but also the liability test would focus on activity rather than design, which would be simpler to administer. More importantly, just as AI activity should be discouraged when it is less safe than a person, human activity should be discouraged when it is less safe than an AI. Once AI is safer than a person and automation is practicable, human tortfeasors should be held to the standard of AI behavior.

The impact of artificial inventors is only starting to be felt, but AI’s rapid improvement means that it may soon outdo people at solving problems in certain areas. This should revolutionize not only research and development but also patent law. The most important requirement to being granted a patent is that an invention must be nonobvious to a hypothetical skilled person who represents an average researcher. As AI increasingly augments average researchers, this should make them more knowledgeable and sophisticated. In turn, this should raise the bar to patentability. Once inventive AI moves from augmenting to automating average researchers, it should directly represent the skilled person in obviousness determinations. As inventive AI continues to improve, this should continue to raise the bar to patentability, eventually rendering innovative activities obvious. To a superintelligent AI, everything will be obvious.

This chapter concludes by responding to some of the controversies about artificial intelligence and possible criticisms of AI legal neutrality. It argues that AI legal neutrality is important regardless of whether AI broadly achieves superhuman performance, and that the law would not want to constrain AI development for protectionist reasons. It further argues that AI legal neutrality is a coherent principle for policymakers to apply, even though it allows the law to treat AI and people differently and will sometimes be at odds with other regulatory goals. Finally, it discusses some of the risks and dangers of AI and argues these are susceptible to management with appropriate legal frameworks.

Criminal law falls short in cases where an AI functionally commits a crime and there are no individuals who are criminally liable. This chapter explores potential solutions to this problem, with a focus on holding AI directly criminally liable where it is acting autonomously and irreducibly. Conventional wisdom holds that punishing AI is incongruous with basic criminal law principles such as the capacity for culpability and the requirement for a voluntary act. Drawing on analogies to corporate and strict criminal liability, the chapter shows AI punishment cannot be categorically ruled out with quick theoretical arguments. AI punishment could result in general deterrence and expressive benefits, and it need not run afoul of negative limitations such as punishing in excess of culpability. Ultimately, however, punishing AI is not justified, because it might entail significant costs and would certainly require radical legal changes. Modest changes to existing criminal laws that target persons, together with potentially expanded civil liability, is a better solution to AI crime.

AI is generating patentable inventions without a person involved who qualifies as an inventor. Yet, there are no rules about whether such an invention could be patented, who or what could qualify as an inventor, and who could own the patents. There are laws that require inventors be natural persons, but they predate inventive AI and were never intended to prohibit patents. AI-generated inventions should be patentable because this will incentivize the development of inventive AI and result in more benefits for everyone. When an AI invents, it should be listed as an inventor because listing a person would be unfair to legitimate inventors. Finally, an AI’s owner should own any patents on its output in the same way that people own other types of machine output. The chapter proceeds to address a host of challenges that would result from AI inventorship, ranging from ownership of AI-generated inventions and displacement of human inventors to the need for consumer protection policies.

Some material in this book was adapted from the author’s prior works, including two coauthored works, reprinted with permission.

Where did you last encounter a piece of political information? Chances are, you clicked on a link a friend sent you on a messaging app, read the preview to a piece on the Facebook wall of a colleague, or followed a retweet posted by an acquaintance on Twitter. Depending on your predilections for the ways of the ancients, you might also have picked up a printed newspaper or watched the news on a television set.

Two episodes from 2011 and 2016 bookend public expectations regarding the role of digital media in politics. In the wake of the protests and demonstrations in North Africa and the Middle East that we discussed in Chapter 5, the dominant public narrative portrayed social media as the keystone that enabled the opposition to coordinate a challenge to otherwise seemingly unwavering autocracies. Only social media offered disgruntled citizens the possibility of taking their discontent to the streets. Decentralized networks on top of real-time communication systems enabled activists to level the playing field against authoritarian regimes that previously had taken full advantage of their control over the official media and showed an unfettered capacity to repress any sign of dissent. It does not matter whether we see digital media as a causal factor; no account of the events in Egypt would be complete without a reference to the #jan25 hashtag on Twitter or the “We are all Khaled Said” site on Facebook (see Chapter 5).

It is June 2015 and the famous American reality-TV personality Donald Trump announces his bid for the Republican nomination to the 2016 race for the US presidency. Journalists, Republican donors, and prospective voters now have to decide if they should take his bid seriously. The history of American presidential campaigns is littered with celebrities and third-party candidates who tried to capitalize on their fame or success by entering politics. While some like Ronald Reagan, Arnold Schwarzenegger, or Michael Bloomberg proved to be successful, most celebrity candidacies turned out to be mere blips in the history of American politics. How should observers decide on whether Donald Trump’s bid fell into the first or the second category? The Trump campaign portrayed their candidate as being in touch with the long-forgotten people lacking a voice in US politics (Green 2017), a group that the campaign of the Democratic frontrunner Hillary Clinton helpfully labeled “deplorables” (Chozick 2016). To assess the validity of Trump’s claims, journalists decided to take to social media as a source of how well his message resonated with the public.

Imagine for a moment that you are a member of a local group of activists. Your group found out that the recently proposed zoning regulation threatens to cut off vital channels of airflow into your city with unforeseen consequences. At the next meeting of the city council, you want to demonstrate the strength your concerns have with the general public. But how do you get the word out?

Organizations are a fixture in democratic politics. Parties, business groups, advocacy groups, unions, and lobbies represent groups of people who share a common interest and who join forces to exert influence in the political process (Abrahamsson 1993). Pluralists see organizations as a natural component of liberal politics and they welcome them in a political arena that they perceive should provide opportunities for decentralized and balanced competition among organized interests (Dahl 1956). Others are less positive in their assessment and are concerned with the fact that inequality lies at the very heart of organizations. First of all, they tend to represent those who already have a disproportionate presence in politics to begin with. Second, organizations, more often than not, engender internal differentials of power between the leaders and the rank-and-file members (Michels 1915). These two layers of external and internal inequality thus shift representation from ordinary citizens to political elites, thus inspiring distrust in organizations as vehicles for political participation on both the political left and right (Abrahamsson 1993).

Ever since the protest cycle of 2010 and 2011, which included events such as the Arab Spring in North Africa and the Middle East, Occupy Wall Street in the United States, and anti-austerity movements in Spain and Greece, the question of whether digital media are transformative tools that facilitate collective action has been a major theme in the public imagination (Freelon, McIlwain, and Clark 2016). Although the potential of computer-mediated communication for social and political activism has been one of the central topics in the digital politics literature almost from the very beginning (see Margolis and Resnick 2000), with early milestones in the Zapatista movement in 1994 or the Seattle protests against the World Trade Organization (WTO) in 1999 (Kahn and Kellner 2004; Van Laer and Van Aelst 2010), it was in the 2010s, after the popularization of social media, that the public started to think of the internet as another tool in the political repertoire of social movements and not as just a novelty item.