AI adoption in agriculture is low.
Alms ‘23 [Natalie Alms, Natalie Alms is a staff reporter at Nextgov/FCW covering federal technology policy, service delivery, customer experience and the government's tech workforce. She is a graduate of Wake Forest University and has written for the Salisbury (N.C.) Post. Connect with Natalie on Twitter at @AlmsNatalie, 8-23-2023, "Agriculture Department is taking a ‘cautious’ approach to generative AI", Nextgov, https://www.nextgov.com/artificial-intelligence/2023/08/agriculture-department-taking-cautious-approach-generative-ai/389679/, AKwon]
The Agriculture Department is approaching generative artificial intelligence cautiously as it wades through questions about the technology’s efficacy, as well as potential impacts on privacy, equity and security, Chris Alvares, the department’s responsible AI officer and chief data officer, said during a Nextgov/FCW event on Wednesday.
“I do see a lot of opportunities, a lot of potential, for generative AI to help USDA be more efficient, deliver programs more effectively, understand the complexity of our organization and be able to describe that better than we can today without those tools,” he said. “But there's a lot of uncertainty around how to use those tools… in a federal agency and how to do that safely and securely, so we need to take a cautious approach.”
Alvares’s comments echo USDA chief information officer Gary Washington’s July take on AI more broadly, Nextgov/FCW “we have to be very cautious.”
The remarks give insight into how one part of the federal government is approaching generative AI. Washington himself said that some agencies “have been proactive about dipping their toes into [AI], but I just felt like we need to put some guardrails around it because that could potentially be dangerous.”
Generative AI has received increasing amounts of attention since the release of OpenAI’s ChatGPT. Microsoft opened up to ChatGPT and other generative AI tools to government customers in June.
The technology has the to help government agencies solve data problems, rewrite jargon and improve chatbots.
But, as Alvares said, generative AI is also the subject of a long list of questions about , bias, civil rights and privacy implications — especially given the large amounts of data needed to train algorithms — and even the accuracy of the tools’ outputs.