**4. Information science including artificial intelligence (AI)**

While innovations and advancements in electronically mediated information processing have led to countless valuable applications, it has been said that there has only been one breakthrough since the term artificial intelligence was introduced by John MccCarthy in 1955: the startling arrival of deep learning. In particular, the victory of an AI-mediated deep learning program over human players of the complex board game Go in 2016 produced a shocked reaction globally. This has been deemed a "Sputnik moment" regarding its impact since this technological achievement threatens the putative superiority of humans, and technophobes fear a singularity where the aggregate of computers, robots, and nanoparticles overpower, enslave, or even eliminate humanity. Technophiles, on the other hand, foresee a future of plenitude and security with humans only engaged in work that they deem worthy and non-repetitive, boring, or dangerous. A more moderate position recognizes both the achievements in information science and the often over-hyped promises of some investigators in the algorithmic artificial intelligence field. A very promising approach advocated by Topol [9] is deep medicine, which allocates routine tasks that deep learning excels at, while focusing the provider on the more difficult and nuanced process variables such as empathic engagement. The practice of digital twinning also shows promise for efficient use of both edge and cloud resources. Notorious and highly visible debacles, such as IBM's Watson effort at cancer intervention with MD Anderson Cancer Center are cautionary but illustrate the importance of scientific persistence and diligence in discriminating science from public relations. Indeed, this is only one illustration of the need to bolster the scientific literacy and transparency

of current practice for both journalists, "media influencers," and the general public. One of the most important critiques of AI is the anthropocentric and narcissistic identification of human intelligence as the paragon and peak of all possible types of intelligence. The search for artificial general intelligence (AGI) needs to be informed by the reality that there are many forms of intelligence that are not premised on human problem-solving ability and that human intelligence is frequently very flawed and biased. Of course, the recent emergence of both focused and active disinformation campaigns targeting not only COVID-19 issues but science in general, and issues of journal retractions and non-replicability of findings have damaged the healthy formation and dissemination of public health information, weakening the essential role of public health advocacy. A very substantive critique of AI is that if it succeeds in replacing many repetitive, boring, or dangerous jobs, there will be many displaced workers who may become part of an "unnecessary class" similar to the role of many of our elderly population. Anticipating such conflicts, there have been calls for an AI code of ethics and regulation of professionals, which extend the excellent but mainly ignored Asimov's three laws of robots. To add to the anxiety and uncertainty of the general public, much AI research and development has been initiated by the military, often with minimal transparency and justified weakly by claims of national security and document classification. Recent developments have shown the dysfunctional nature and negative outcomes associated with overclassification of documents, often based on political expediency not national security.
