In policy debates concerning the governance and regulation of Artificial Intelligence (AI), both the Precautionary Principle (PP) and the Innovation Principle (IP) are advocated by their respective interest groups. Do these principles offer wholly incompatible and contradictory guidance? Does one necessarily negate the other? I argue here that provided attention is restricted to weak-form PP and IP, the answer to both of these questions is "No." The essence of these weak formulations is the requirement to fully account for type-I error costs arising from erroneously preventing the innovation's diffusion through society (i.e. mistaken regulatory red-lighting) as well as the type-II error costs arising from erroneously allowing the innovation to diffuse through society (i.e. mistaken regulatory green-lighting). Within the Signal Detection Theory (SDT) model developed here, weak-PP red-light (weak-IP green-light) determinations are optimal for sufficiently small (large) ratios of expected type-I to type-II error costs. For intermediate expected cost ratios, an amber-light 'wait-and-monitor' policy is optimal. Regulatory sandbox instruments allow AI testing and experimentation to take place within a structured environment of limited duration and societal scale, whereby the expected cost ratio falls within the 'wait-and-monitor' range. Through sandboxing regulators and innovating firms learn more about the expected cost ratio, and what respective adaptations -- of regulation, of technical solution, of business model, or combination thereof, if any -- are needed to keep the ratio out of the weak-PP red-light zone. Nevertheless AI foundation models are ill-suited for regulatory sandboxing as their general-purpose nature precludes credible identification of misclassification costs.

The rapid development of generative artificial intelligence (AI) technologies raises concerns about the accountability of sociotechnical systems. Current generative AI systems rely on complex mechanisms that make it difficult for even experts to fully trace the reasons behind the outputs. This paper first examines existing research on AI transparency and accountability and argues that transparency is not a sufficient condition for accountability but can contribute to its improvement. We then discuss that if it is not possible to make generative AI transparent, generative AI technology becomes ``artificially created nature'' in a metaphorical sense, and suggest using the precautionary principle approach to consider AI risks. Finally, we propose that a platform for citizen participation is needed to address the risks of generative AI.

Key variables associated with the direction and intensity of concern include prior experience using a large language model such as ChatGPT, general trust in science, adherence to the precautionary principle versus support for unrestricted innovation, and demographic factors such as gender. By analyzing these relationships, the paper provides valuable insights into the American public's response to AI that are particularly important in the development of policy to regulate or further encourage its development. Key words: artificial intelligence, survey research, public opinion, precautionary principle, ChatGPT Introduction Artificial intelligence is defined as "a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions that impact real or virtual environments" (National Artificial Intelligence Act of 2020, H.R. 6216). According to this definition, AI systems leverage both machine and human inputs to (a) perceive real and virtual environments, (b) transform these perceptions into models through automated analysis, and (c) use these models to generate options for information or action. Currently, AI is employed in a range of applications including mapping technologies, handwriting recognition for mail sorting, spam filtering, language translation, financial trading, and more.

There is an ongoing debate on balancing the benefits and risks of artificial intelligence (AI) as AI is becoming critical to improving healthcare delivery and patient outcomes. Such improvements are essential in resource-constrained settings where millions lack access to adequate healthcare services, such as in Africa. AI in such a context can potentially improve the effectiveness, efficiency, and accessibility of healthcare services. Nevertheless, the development and use of AI-driven healthcare systems raise numerous ethical, legal, and socio-economic issues. Justice is a major concern in AI that has implications for amplifying social inequities. This paper discusses these implications and related justice concepts such as solidarity, Common Good, sustainability, AI bias, and fairness. For Africa to effectively benefit from AI, these principles should align with the local context while balancing the risks. Compared to mainstream ethical debates on justice, this perspective offers context-specific considerations for equitable healthcare AI development in Africa.

Artificial intelligence (AI) has the potential to deliver significant social and economic benefits, including reducing accidental deaths and injuries, making new scientific discoveries, and increasing productivity.[1] However, an increasing number of activists, scholars, and pundits see AI as inherently risky, creating substantial negative impacts such as eliminating jobs, eroding personal liberties, and reducing human intelligence.[2] Some even see AI as dehumanizing, dystopian, and a threat to humanity.[3] As such, the world is dividing into two camps regarding AI: those who support the technology and those who oppose it. Unfortunately, the latter camp is increasingly dominating AI discussions, not just in the United States, but in many nations around the world. There should be no doubt that nations that tilt toward fear rather than optimism are more likely to put in place policies and practices that limit AI development and adoption, which will hurt their economic growth, social ...

THE importance of science in society has no greater spokesperson than Lord Martin Rees. From his perch at Cambridge--and a centre he formed on studying existential risks--he has served as both a promoter, populariser and the moral conscience of scientific endeavour far beyond his academic field of astrophysics. In "Our Final Century" in 2003 (retitled more breathlessly "Our Final Hour" in the American edition) he presented a range of global challenges, from bioterrorism to nuclear weapons. He put the risk of human extinction by 2100 from our technologies at around 50%. His latest book, "On the Future", is more sanguine.

FLORENCE – For policymakers anywhere, the best way to make decisions is to base them on evidence, however imperfect the available data may be. But what should leaders do when facts are scarce or non-existent? That is the quandary facing those who must grapple with the fallout of "advanced predictive algorithms" – the binary building blocks of machine learning and artificial intelligence (AI). In academic circles, AI-minded scholars are either "singularitarians" or "presentists." Singularitarians generally argue that while AI technologies pose an existential threat to humanity, the benefits outweigh the costs.

Volume; ever increasing volume which breaks down traditional data-holding capacity Variety; more and more heterogeneous data from many formats and types are bombarding the data environment Velocity; more and more data is time sensitive now; frequent updates are taking place instead of relying on historical old data and data in real time is being generated now by the internet of things, amongst others. Veracity; how valid and reliable is the data? Since now we have so much data, any point of view can be supported by selective adaption of data. Velocity; more and more data is time sensitive now; frequent updates are taking place instead of relying on historical old data and data in real time is being generated now by the internet of things, amongst others. Veracity; how valid and reliable is the data?