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Recent disclosures and leaks have revealed that the public, both in the US and internationally, are subject to some degree of mass electronic surveillance. Much of the resulting debate has focused on exactly who is performing the surveillance, what information is being recorded, and numerous legal intricacies involving these activities. Here, Danks describes the alternative consequentialist and deontological perspectives on the ethics of surveillance, casting the problem as an updated version of Blaise Pascal's wager. Adapted from the source document.

AbstractRecent progress in large language models has led to applications that can (at least) simulate possession of full moral agency due to their capacity to report context-sensitive moral assessments in open-domain conversations. However, automating moral decision-making faces several methodological as well as ethical challenges. They arise in the fields of bias mitigation, missing ground truth for moral "correctness", effects of bounded ethicality in machines, changes in moral norms over time, risks of using morally informed AI systems as actual advice, as well as societal implications an increasing importance of algorithmic moral decision-making would have. This paper comments on all these challenges and provides critical considerations for future research on full artificial moral agency. Importantly, some of the adduced challenges can be met by more careful technology design, but others necessarily require engagement with core problems of meta-ethics.

AbstractThe introduction of a new generation of AI systems has kicked off another wave of AI hype. Now that AI systems have added the ability to produce new content to their predictive capabilities, extreme excitement about their alleged capabilities and opportunities is matched only by long held fears about job loss and machine control.We typically understand the dynamics of AI hype to be something that happens to us, but in this commentary, we propose to flip the script. We suggest that AI hype is not a social fact, but a widely shared practice. We outline some negative implications of this practice and suggest how these can be mitigated, especially with regards to shifting ways of knowing and learning about AI, in the classroom and beyond. Even though pedagogical efforts (broadly understood) have benefited from AI hyping (there is now more varied AI training than ever), such efforts can also help minimize the impacts of hyping on the public's credulity toward extravagant claims made about AI's potential benefits and dangers.Below, we consider steps that can be taken to address this issue and illustrate pathways for more holistic AI educational approaches that participate to a lesser degree in the practice of AI hyping. We contend that designing better AI futures will require that AI hyping be blunted to enable grounded debates about the ways that AI systems impact people's lives both now and in the near future.