It won't be in the Olympics anytime soon but Oki Mek considers artificial intelligence "a team sport." As the chief artificial intelligence officer for the Department of Health and Human Services, Mek may be a little biased, but as his agency works through its AI strategy -- released in January -- collaboration and knowledge exchange will be paramount. The strategy aims to promote AI adoption, and to ensure that algorithms are fair, legal and ethical. Three core pieces of the strategy are adoption and bringing the entire department up to speed on the language of AI; scaling best practices, and accelerated adoption. As for the first piece, Mek said culture change plays a pivotal role. "The main risks here is not AI itself, it's not the technology itself, it's more of a culture shift.

The Department of Health and Human Services is developing an artificial intelligence playbook to help teams overcome common obstacles and challenges that come with implementing AI technologies. HHS Chief AI Officer Oki Mek discussed the playbook and how it plays into his overall priority of making AI a collaboratively cultivated technology at the agency during a NextGov event July 29. He said that one of the elements that he hopes to include in the playbook is to help with barriers to data acquisition, which he added is especially difficult within HHS. "Having a playbook could really help in terms of, what are the obstacles that you will encounter when you go on this AI, machine learning journey, because the two biggest obstacles are really the data acquisition, getting the data, especially with Health and Human Services because health records and data are very heavily regulated, so data acquisition will be tough," Mek said. "We could help provide some guidance and some lessons learned, some best practices around that." Mek added that the playbook could also provide some guidance around cleaning data, since cleaning and processing data is a big component of getting it ready for AI usage. The playbook will also provide definitions around AI, which Mek argued is a broad term that can have different meanings and applications.

Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on exogenous variables, and functional assignments. Specifying such models can be extremely difficult in practice. The process requires substantial domain expertise, and does not scale easily to large systems, multiple systems, or novel system modifications. At the same time, many application domains, such as molecular biology, are rich in structured causal knowledge that is qualitative in nature. This manuscript proposes a general approach for querying a causal biological knowledge graph, and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question. We demonstrate the feasibility, accuracy and versatility of this approach using two case studies in systems biology. The first demonstrates the appropriateness of the underlying assumptions and the accuracy of the results. The second demonstrates the versatility of the approach by querying a knowledge base for the molecular determinants of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced cytokine storm, and performing counterfactual inference to estimate the causal effect of medical countermeasures for severely ill patients.

In the remote work environment spawned by the COVID-19 pandemic, more flexible, quicker methods of getting systems the authority to securely operate is more critical than ever, said a top IT advisor at the Department of Health and Human Services. "Machine learning is critical in terms of fighting fire with fire. You're going to lose that battle" with hackers, said Oki Mek, senior advisor to the agency's CIO and its ReImagine project. HHS is one of the agencies at the center of the federal government's response to the COVID pandemic. The agency is "getting hit hard" by hackers attempting to penetrate its networks, said Mek.