The widespread use of social media has highlighted potential negative impacts on society and individuals, largely driven by recommendation algorithms that shape user behavior and social dynamics. Understanding these algorithms is essential but challenging due to the complex, distributed nature of social media networks as well as limited access to real-world data. This study proposes to use academic social networks as a proxy for investigating recommendation systems in social media. By employing Graph Neural Networks (GNNs), we develop a model that separates the prediction of academic infosphere from behavior prediction, allowing us to simulate recommender-generated infospheres and assess the model's performance in predicting future co-authorships. Our approach aims to improve our understanding of recommendation systems' roles and social networks modeling. To support the reproducibility of our work we publicly make available our implementations: https://github.com/DimNeuroLab/academic_network_project

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.

Suppose you enter a dark room in an unknown building. You may panic about some potential monsters lurking in the dark. Or just turn on the light, to avoid painfully bumping into the furniture. The dark room is the future of artificial intelligence (AI). Unfortunately, there are people who believe that, as we step into the room, we may run into some evil, ultra-intelligent machines. Fear of some kind of ogre, such as a Golem or a Frankenstein's monster, is as old as human memory.

OXFORD – Galileo viewed nature as a book written in the language of mathematics and decipherable through physics. His metaphor may have been a stretch for his milieu, but not for ours. Ours is a world of digits that must be read through computer science. It is a world in which artificial-intelligence (AI) applications perform many tasks better than we can. Like fish in water, digital technologies are our infosphere's true natives, while we analog organisms try to adapt to a new habitat, one that has come to include a mix of analog and digital components.

Galileo viewed nature as a book written in the language of mathematics and decipherable through physics. His metaphor may have been a stretch for his milieu, but not for ours. Ours is a world of digits that must be read through computer science. It is a world in which artificial-intelligence (AI) applications perform many tasks better than we can. Like fish in water, digital technologies are our infosphere's true natives, while we analogue organisms try to adapt to a new habitat, one that has come to include a mix of analogue and digital components.

Galileo viewed nature as a book written in the language of mathematics and decipherable through physics. His metaphor may have been a stretch for his milieu, but not for ours. Ours is a world of digits that must be read through computer science. It is a world in which artificial-intelligence (AI) applications perform many tasks better than we can. Like fish in water, digital technologies are our infosphere's true natives, while we analog organisms try to adapt to a new habitat, one that has come to include a mix of analog and digital components.