Defense Advanced Research Projects AgencyTagged Content List

Technologies for Trustworthy Computing and Information

Confidence in the integrity of information and systems

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Today, the expeditious delivery of electronic documents, messages, and other data is relied on for everything from communications to navigation. As the near instantaneous exchange of information has increased in volume, so has the variety of electronic data formats–from images and videos to text and maps. Verifying the trustworthiness and provenance of this mountain of electronic information is an exceedingly difficult task as individuals and organizations routinely engage with data shared by unauthenticated and potentially compromised sources.
Whether a piece of information is private, proprietary, or sensitive to national security, systems owners and users have little guarantees about where their information resides or of its movements between systems. When a user enters information on a phone, for example, it is difficult to provably track that the data remains on the phone or whether it is uploaded to a server beyond the device. The national defense and security communities are similarly left with few options when it comes to ensuring that sensitive information is appropriately isolated, particularly when it’s loaded to an internet-connected system.
A key ingredient in effective teams – whether athletic, business, or military – is trust, which is based in part on mutual understanding of team members’ competence to fulfill assigned roles. When it comes to forming effective teams of humans and autonomous systems, humans need timely and accurate insights about their machine partners’ skills, experience, and reliability to trust them in dynamic environments. At present, autonomous systems cannot provide real-time feedback when changing conditions such as weather or lighting cause their competency to fluctuate. The machines’ lack of awareness of their own competence and their inability to communicate it to their human partners reduces trust and undermines team effectiveness.
Today, machine learning (ML) is coming into its own, ready to serve mankind in a diverse array of applications – from highly efficient manufacturing, medicine and massive information analysis to self-driving transportation, and beyond. However, if misapplied, misused or subverted, ML holds the potential for great harm – this is the double-edged sword of machine learning.
February 20, 2019,
The Defense Advanced Research Projects Agency (DARPA) Defense Sciences Office (DSO) is sponsoring a Proposers Day webcast to provide information to potential proposers on the objectives of an anticipated Broad Agency Announcement (BAA) for the Competency-Aware Machine Learning (CAML) program. The Proposers Day will be held via prerecorded webcast on February 20, 2019 at 11:00 AM EST and will repost at 3:00 PM EST. Advance registration is required for viewing the webcast.