In today’s knowledge-driven world, the federal government is sensing the transformative perspective of knowledge analysis in decision-making. Data is a critical asset that enables government agencies to gain insights, identify trends, and better count citizens.
Government agencies such as the U. S. Centers for Disease Control and Prevention (CDC) have not been able to do so. The U. S. Department of Health and National Institutes of Health (NIH) rely on complex analytics to drive innovative study initiatives, drive medical advances, and monitor and respond to public health threats faster and more effectively. Data analytics allows those agencies to notice patterns of knowledge and progress on tactics they may never have done before.
However, while knowledge analysis provides transparent benefits, the question of how to gain more knowledge while protecting knowledge privacy remains a key consideration. As government agencies collect and analyze gigantic amounts of sensitive nonpublic information, it is of utmost importance to ensure that knowledge is secure. Privacy and public trust. Striking a balance between transparency, which allows for greater public accountability, and protecting Americans’ privacy is a complex challenge.
On the occasion of the May 2023 GovFuture Forum at George Mason University (GMU) in the Washington, DC area, government leaders Stacey Marovich (senior health informatics scientist, CDC’s National Institute for Occupational Safety and Health (NIOSH)), Dr. Marovich. Susan Gregurick (Associate Director of Data Science and Director of the Office of Data Science Strategy, NIH) and industry veteran John Landers (Solutions Engineer, Public Sector Sales Engineering, Alteryx) shared their perspectives on the use of knowledge and complex analytics in government.
The key points of this roundtable are detailed below.
Stacey Marovich (CDC):
“One of our most demanding situations is the harmonization of knowledge. Public fitness works because knowledge is collected at the local and state level. Therefore, it is collected in many, many other states. Every state, city or region does things differently. So, we are looking to harmonize and standardize knowledge at the national level, so that it can be used for study purposes. And of course, one of our biggest messes is just the budget [because] we count on Congress for our budget.
“We also seek at all times to integrate new resources and stay one step ahead of new teams that emerge. We are looking to modernize our team and be able to use knowledge in a more actionable way with real-time knowledge gathering. And the impetus for that is actually where I’d like to see things evolve. Basically, NIOSH deserves to be used as advanced as you can imagine and have knowledge collected and coded in real time at the time the knowledge is collected.
Dr. Susan Gregurick (NIH):
“For us, one of the most demanding situations is to look for transparency and bias. If you use giant language models, they hallucinate, which in the health sector can be a pretty big problem. So for us, we’re actually looking at not just the big language models, but AI and ML in general, how we can create more moral, transparent, and less biased biases in our datasets and in our algorithms. For us, this is a big challenge due to the widespread use of ChatGPT. and Google Bard and other features where other people ask similar questions about your health. And we need to make sure that the data they get is accurate and that they perceive where that data is coming from and they perceive the accuracy. So it’s a big challenge for us And of course we’re going to fund a lot of studies in this domain as we move forward.
“I think I’m most passionate about integrating giant language models with very fast express domain language models. What I’d like to see is this ability to take the accumulated data from those more accurate iterative language patterns and organize the data. You can no longer manually manage data. It’s an outdated way of thinking. We want to use analytics to retain data, give it a measure of quantity and quality, and then feed it back into our algorithms for further assessment of bias and transparency.
John Landers (Alteryx):
“There is a tension between those who collect knowledge and preserve it and those who need to use it for research. And I think that tension will be built in, and in some cases, you actually need to know what the answer is to get information. “
“The advancement of artificial intelligence and ChatGPT in the last two weeks has been huge. Being vigilant to update everyone to work in a stylish environment is incredibly important. And I think there’s a lot of excitement around AI and ChatGPT, even though most people probably don’t realize what that means and what the implications are. But it’s going to excite other people and there’s going to be a breakthrough in that generation and in the tech spaces around that conversation.
Data analytics has an indispensable tool in the federal government’s arsenal, enabling evidence-based decision-making, especially within agencies like the CDC and NIH. The ability to make good use of knowledge allows these agencies to monitor public health, conduct research and make decisions. Possible informed options based on knowledge. However, the culpable use of knowledge must be accompanied by a commitment to transparency and privacy. As the federal government continues to unleash the force of knowledge analytics and looks to the future, it will have to navigate this delicate balance to maximize the benefits knowledge can deliver while protecting Americans’ privacy and maintaining public trust.
The full recording of the May 2023 GovFuture Forum event is available on the GovFuture website.