An arms race for an artificial general intelligence would be detrimental for and even pose an existential threat to humanity if it results in an unfriendly AGI. In this paper, an all-pay contest model is developed to derive implications for public policy to avoid such an outcome. It is established that, in a winner-takes-all race, where players must invest in R&D, only the most competitive teams will participate. Thus, given the difficulty of AGI, the number of competing teams is unlikely (...) ever to be very large. It is also established that the intention of teams competing in an AGI race, as well as the possibility of an intermediate outcome, is important. The possibility of an intermediate prize will raise the probability of finding the dominant AGI application and, hence, will make public control more urgent. It is recommended that the danger of an unfriendly AGI can be reduced by taxing AI and using public procurement. This would reduce the pay-off of contestants, raise the amount of R&D needed to compete, and coordinate and incentivize co-operation. This will help to alleviate the control and political problems in AI. Future research is needed to elaborate the design of systems of public procurement of AI innovation and for appropriately adjusting the legal frameworks underpinning high-tech innovation, in particular dealing with patenting by AI. (shrink)

This paper provides an early evaluation of Artificial Intelligence against COVID-19. The main areas where AI can contribute to the fight against COVID-19 are discussed. It is concluded that AI has not yet been impactful against COVID-19. Its use is hampered by a lack of data, and by too much data. Overcoming these constraints will require a careful balance between data privacy and public health, and rigorous human-AI interaction. It is unlikely that these will be addressed in time to be (...) of much help during the present pandemic. In the meantime, extensive gathering of diagnostic data on who is infectious will be essential to save lives, train AI, and limit economic damages. (shrink)

This paper draws lessons from the COVID-19 pandemic for the relationship between data-driven decision making and global development. The lessons are that users should keep in mind the shifting value of data during a crisis, and the pitfalls its use can create; predictions carry costs in terms of inertia, overreaction and herding behaviour; data can be devalued by digital and data deluges; lack of interoperability and difficulty reusing data will limit value from data; data deprivation, digital gaps and digital divides (...) are not just a by-product of unequal global development, but will magnify the unequal impacts of a global crisis, and will be magnified in turn by global crises; having more data and even better data analytical techniques, such as artificial intelligence, does not guarantee that development outcomes will improve; decentralised data gathering and use can help to build trust – particularly important for coordination of behaviour. (shrink)