RFP issued for biometric and other AI tech to support US federal agencies

RFP issued for biometric and other AI tech to support US federal agencies

The Program Support Center within the U.S. Department of Health and Human Services (HHS) has launched a request for proposals to provide AI technologies and support services, including facial recognition, robotic process automation, and machine and supervised learning, Nextgov reports. While the request is issued by HHS, the contract is intended to support all federal agencies.

The five-year, $49 million contract for Intelligent Automation/Artificial Intelligence is part of a broader effort to digitize and automate low-value work done by federal employees to move them to higher-value responsibilities. The Office of Personnel Management has said automation could reduce 60 percent of federal employee’s workloads by up to 30 percent, and make 5 percent redundant.

Program Support Center officers write in the RFP that the solutions will be used to reduce backlogs, predict fraudulent transactions, and identify critical suspects with facial recognition.

Vendors are required to show past success in government projects in at least three areas among applied ideation and design support, engineering and process engineering, systems design, and prototyping and model-making support. Test and evaluation, quality assurance, user training and logistics, operations and maintenance support are also built into the final contract. Multiple awards are expected for each functional area of the contract.

HHS and other federal agencies will be able to issue task orders through the Program Support Center once the AI service is established. Individual task orders are expected to be priced around $300,000, but could be as high as $8 million with multiple functional areas packaged together, according to Nextgov.

Bids are due by January 30.

The Government Accountability Office pointed out last year that HHS needs to implement information security and access controls to protect at-risk personal information, as federal digital systems lag behind the Office’s recommendations.

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