Chapter 4 describes the field of cognitive science, which is the arena where all those who study “intelligent systems” (“minds“) get together to compare notes. A shared idea is that the mind can be understood as an information-processing computational system. We will see how during the 1960s renewed interest in the mind from different academic disciplines emerged as a reaction to the denial of the mind of an approach to psychology called behaviorism. We then discuss the various strands of thinking in a variety of different fields that led to this “cognitive revolution.” We learn that there are fundamental, opposing views in this field that are relevant to the nature–nurture debate. Despite differences, a general understanding within cognitive science is that the mind can be studied at different levels of abstractness and from different angles which to some extent compete but also complement each other.

This chapter describes exemplary curricula in the sciences and engineering that give expression to many of the ideals of decolonization. Despite claims about the value neutrality and universality of the sciences, broadly defined, these exemplars demonstrate how to decolonize curricula in engineering, computer science, occupational therapy, organic chemistry, and archaeology. These curriculum innovators are conscious of the social dimensions of the sciences and what this means for improving the human condition.

Though worries about the impact of new technology have accompanied many inventions, AI is unusual in that some of the starkest recent warnings have come from those most knowledgeable about the field. Many of these concerns are linked to ‘general’ or ‘strong’ AI, meaning the creation of a system that is capable of performing any intellectual task that a human could – and raising complex questions about the nature of consciousness and self-awareness in a non-biological entity. The possibility that such an entity might put its own priorities above those of humans is non-trivial, but this book focuses on the more immediate challenges raised by ‘narrow’ AI – meaning systems that can apply cognitive functions to specific tasks typically undertaken by a human. The book is organized around the following sets of problems: How should we understand the challenges to regulation posed by the technologies loosely described here as ‘AI systems’? What regulatory tools exist to deal with those challenges and what are their limitations? And what more is needed – rules, institutions, actors – to enable us to reap the benefits offered by AI while minimizing avoidable harm?

Since computers entered the mainstream in the 1960s, the efficiency with which data can be processed has raised regulatory questions. This is well understood with respect to privacy. Data that was notionally public – divorce proceedings, say – had long been protected through the ‘practical obscurity’ of paper records. When such material was available in a single hard copy in a government office, the chances of one’s acquaintances or employer finding it were remote. Yet when it was computerized and made searchable through what ultimately became the Internet, practical obscurity disappeared. Today, high-speed computing poses comparable threats to existing regulatory models in areas from securities regulation to competition law, merely by enabling lawful activities – trading in stocks, or comparing and adjusting prices, say – to be undertaken more quickly than previously conceived possible. Many of these questions are practical rather than conceptual and apply to technologies other than AI. Nevertheless, current approaches to slowing down decision-making – through circuit-breakers to stop trading, for example – will not address all of the problems raised by the speed of AI systems.

Privacy has traditionally been conceptualized in an individualistic framing, often as a private good that is traded off against other goods. This chapter views the process of privacy enforcement through the lens of governance and situated design of sociotechnical systems. It considers the challenges in formulating and designing privacy as commons (as per the Governing Knowledge Commons framework) when privacy ultimately gets enacted (or not) in complex sociotechnical systems. It identifies six distinct research directions pertinent to the governance and formulation of privacy norms, spanning an examination of how tools of design could be used to develop design strategies and approaches to formulate, design, and sustain a privacy commons, and how specific technical formulations and approaches to privacy can serve the governance of such a privacy commons.