The authors of the article examine the reasons for the pessimistic perspective, that envisions the possibility of catastrophe for humanity caused by the “singularity.” We argue that the interaction between humans and machines is neither a matter of competition nor collaboration. Instead, a more realistic outcome of human-machine interactions is the erosion of the genuine foundations of human identity nature. This perspective is based on an analysis of the essential elements that constitute human identity, as well as the most significant ways in which robotics and artificial intelligence (AI) affect individual, social, and cultural life. Through this analysis, we aim to identify the threats and opportunities, as well as propose viable solutions for the preservation and sustainable development of humanity.  

In this paper, I argue that the primary AI-related ethical issue is that of the agendas driving AI. While there is a portion of AI-related ethical risks which stems from indeterministic elements of AI and their interaction with existing social and economic systems, because these are indeterministic and so not predictable, these risks will need to be addressed on a primarily reactionary basis. There is room here for ethical guidelines aimed at reducing these risks; however, it is in the area where we have clear, front-end control that we should focus our efforts. I will suggest that the current concerns of AI ethics, relating to the nature of AI – its non-transparent, “black-box” nature and its lack of moral agency – do not belong to this area. It is rather the black-box nature and moral agency of the agendas driving AI development and deployment which constitute the primary threats and which are almost entirely, in principle, within our control. It is these threats which I attempt to uncover and suggest ways to manage. 

Can AI ‘solve’ politics? In this paper, I explore optipolitics, i.e., the idea that politics – along with other complex social issues – can be framed as a mathematical optimization problem and solved as such. I begin by describing politics, optimization, and some reasons for applying the latter to the former. I then present a liberal democratic version of optipolitics and try to defend it against eight fundamental challenges. While I concede that none of the challenges can be satisfactorily overcome at present, many can be shown to be at least as difficult to overcome in conventional representative democracy. Most importantly, as a logical endpoint of AI-driven technocracy, I argue that optipolitics can serve as a useful starting-point for a critical debate about the future of democratic governance in the age of machines. 

The central argument of this paper is that the frameworks of responsible innovation (RI) and technology assessment (TA) are rooted in an antiquated political and geopolitical paradigm, thus necessitating a conceptual overhaul. This argument is supported by two primary reasons. First, RI and TA are not neutral towards technological innovation; instead, they inherently align with a specific political and geopolitical model: the liberal world order (LWO). This model currently faces significant challenges and crises, which we investigated through a literature review of RI and TA and a subsequent political and geopolitical analysis. Second, the very essence of our technologies has dramatically transformed over the past 20 years. We now live in a world dominated by intricate global engineering systems that are not only political but also geopolitical in nature. These transnational systems influence the decisions and interactions of nations. The current LWO framework struggles to effectively grasp and manage these influential global systems. In addition, this paper presents a reinterpreted version of Rodrik's trilemma. This reformulation was designed to consolidate and expand upon the insights already gained. It revisits the issues identified, emphasising the urgency of revamping both TA and RI. As we embark on this reassessment, the invaluable insights from philosophical reflections should not be underestimated.