Technical challenges for lawyers impact on our ethics. Some predict that singularity technologies will progressively merge human and machine intelligences. These may become evident in knowledge processing and therefore affect litigation, with potential for such merged intelligences to minimize moral accountability. Perhaps machine intelligences will progressively calculate which actions are ‘better’ in rigid consequentialist terms, ignoring competing moral frameworks described in this book, and government authority will be challenged. Speculation, of course, but there is unlikely to be a lessening in the need for moral leadership from law school deans, bar association presidents and leading practitioners, as Greater China strives for overall sustainability. Arguably, with our lawyers’ sensitivity to virtue and Confucian teaching we might conclude that narrow role morality is no longer enough. Our final social utility (and the reason for any social and economic privileges we retain as lawyers) may lie in our willingness to help whole communities access practical justice: that is, genuine equality of access to health, food, housing and education.

There is an overwhelming case against the current regulation of AI for existential risks. The regulation would compromise the progress in AI because regulators could not tell which lines of research make existential threats. Part of the reason is that these risks are not imminent and are not probable, thus making identification even harder. Finally, regulating at the national level might empower rogue nations to threaten the national security of well-functioning democracies. But international regulation is not possible, because it is difficult, if not impossible, to verify that prohibited lines of research are not occurring within another nation’s territory. Encouraging with subsidies the development of AI that is not an existential threat is the best way forward, because it will build up knowledge of potential dangers.

Chapter 15 concludes by describe two radically different future visions of the intelligent society. On one hand, instrumentarian intelligence assumes that algorithms tracking human behavior can predict human behavior more accurately than ever before. In western countries, this intelligence manifests itself in a new surveillance capitalism with companies like Google and Facebook constantly searching for behavioral surplus in both online and offline settings. In the political domain, instrumentarian intelligence seeks a reputation state built on a neobehavioristic governing model. The most prominent example is the nationwide social credit system in China that makes it possible to grade citizens on different behavioral indicators. In stark contrast, civic intelligence highlights a use of technology still controlled by the community and citizens in contrast to the dehumanizing aspects of instrumentarian intelligence. While machine intelligence also craves for informational diversity in its hunt for behavioral surplus, civic intelligence seeks a broader diversity that includes not only information, but also multicultural, cognitive, biological, and participatory diversity. The “fuel” of CI is people who are different from each other, with different interests and unique perspectives. Civic intelligence also builds on a strong knowledge commons and an open shared collective memory. It does not hide information to produce the best predictions, but it promotes complete transparency and individual empowerment. In contrast to instrumentarian intelligence, CI still lets human-to-human intelligence, and not the algorithms, be at the core of the human collective problem solving.