The U.S. Army is looking for input from industry on the best products, research, innovations, and operational concepts to best protect its data sets for use in artificial intelligence (AI) and machine learning (ML) applications.
In a May 5 request for information (RFI) posted to SAM.gov, the Army said it is looking for information in support of an Army Science Board (ASB) study titled “Testing, Validating, and Protecting Army Data Sets for Use in Artificial Intelligence (AI) and Machine Learning (ML) Applications.”
“To complete its analyses and explore as many viable sources of data as possible, the ASB is soliciting information from organizations external to the Army, including industry (traditional defense contractors as well as and non-traditional and/or small businesses), government laboratories, Federally Funded Research and Development Contractors (FFRDCs), and academia,” the RFI says.
“Based on information submitted in response to this request, the board may conduct additional market research,” it adds.
Specifically, the study team is looking for details on methodologies and techniques used for data set protection and security – including data encryption, data privacy, security auditing and testing, integrity and non-repudiation methods, and restoration of capabilities following a data set compromise.
Additionally, the Army wants information on testing AI-enhanced systems in battlefield applications. For example, it wants input on “approaches to real-world and simulated testing, including robustness against adversarial AI technologies and assessment of system performance under various realistic scenarios.”
The Army also wants “quantifiable measures and frameworks for assessing the effectiveness, accuracy, and reliability of AI-enhanced systems in military context against representative threats.” For instance, the RFI says this could mean “pitting Army units, against an opposing force with intent to win, in joint experimentation or training exercises.”
Finally, the Army wants input on integration and interoperability strategies, how validation and verification apply to AI data sets, and how to demonstrate to users and stakeholders that the AI system is trustworthy, reliable, and effective.
Responses to the RFI are due on May 12.
