Artificial intelligence AI can bring substantial benefits to society by helping to reduce costs, increase efficiency and enable new solutions to complex problems. Using Floridi's notion of how to design the 'infosphere' as a starting point, in this chapter I consider the question: what are the limits of design, i.e. what are the conceptual constraints on designing AI for social good? The main argument of this chapter is that while design is a useful conceptual tool to shape technologies and societies, collective efforts towards designing future societies are constrained by both internal and external factors. Internal constraints on design are discussed by evoking Hardin's thought experiment regarding 'the Tragedy of the Commons'. Further, Hayek's classical distinction between 'cosmos' and 'taxis' is used to demarcate external constraints on design. Finally, five design principles are presented which are aimed at helping policymakers manage the internal and external constraints on design. A successful approach to designing future societies needs to account for the emergent properties of complex systems by allowing space for serendipity and socio-technological coevolution.

With there being many technical and ethical issues with artificial intelligence (AI) that involve marginalized communities, there is a growing interest for design methods used with marginalized people that may be transferable to the design of AI technologies. Participatory design (PD) is a design method that is often used with marginalized communities for the design of social development, policy, IT and other matters and solutions. However, there are issues with the current PD, raising concerns when it is applied to the design of technologies, including AI technologies. This paper argues for the use of PD for the design of AI technologies, and introduces and proposes a new PD, which we call agile participatory design, that not only can could be used for the design of AI and data-driven technologies, but also overcomes issues surrounding current PD and its use in the design of such technologies.

Much contemporary rhetoric regards the prospects and pitfalls of using artificial intelligence techniques to automate an increasing range of tasks, especially those once considered the purview of people alone. These accounts are often wildly optimistic, understating outstanding challenges while turning a blind eye to the human labor that undergirds and sustains ostensibly "automated" services. This long-standing focus on purely automated methods unnecessarily cedes a promising design space: one in which computational assistance augments and enriches, rather than replaces, people's intellectual work. This tension between agency and automation poses vital challenges for design, engineering, and society at large. In this talk we will consider the design of interactive systems that enable adaptive collaboration among people and computational agents.

Over the course of the last few decades, the gaming industry has seen great strides. Beginning with simple games like Pong* and Pac-Man* which offered players a short escape from reality and growing into such involved games like World of Warcraft* and Call of Duty 4* which are serious hobbies to those that play them. Today's gamers, who according to the Entertainment Software Association (ESA) have an average of 13 years of gaming under their belt, have grown accustomed to seeing each new game become increasingly complex, engaging, and intelligent. For developers, the challenge becomes pushing the envelope to create games that are increasingly compelling. Computer-controlled Artificial Intelligence (AI) has evolved in many forms to meet the test.

Companies are exploring ways to automate manual business operations but few have the institutional knowledge to do so. Donaldson, who spent 16 years building IT systems for video game retailer GameStop, is building a methodology that combines robotic process automation (RPA), artificial intelligence (AI) and design thinking to help companies automate manual and routine tasks such as data processing and workforce optimization. But here's the kicker: If it works as Donaldson hopes, it will provide enterprises a way to automate work to augment rather than replace humans. Such a method could help stave off job elimination. "Processes drive all of our organizations and almost anything can be automated so long as you can identify and use appropriate tools to define what that process looks like end to end," says Donaldson, founder and CEO of Intriosity, a new consultancy whose goal is to create "force multipliers" in business productivity.