Defense Advanced Research Projects AgencyTagged Content List

Technologies for Trustworthy Computing and Information

Confidence in the integrity of information and systems

Showing 25 results for Trust + Programs RSS
In order to transform machine learning systems from tools into partners, users need to trust their machine counterpart. One component to building a trusted relationship is knowledge of a partner’s competence (an accurate insight into a partner’s skills, experience, and reliability in dynamic environments). While state-of-the-art machine learning systems can perform well when their behaviors are applied in contexts similar to their learning experiences, they are unable to communicate their task strategies, the completeness of their training relative to a given task, the factors that may influence their actions, or their likelihood to succeed under specific conditions.
Unreliable software places huge costs on both the military and the civilian economy. Currently, most Commercial Off-the-Shelf (COTS) software contains about one to five bugs per thousand lines of code. Formal verification of software provides the most confidence that a given piece of software is free of errors that could disrupt military and government operations. Unfortunately, traditional formal verification methods do not scale to the size of software found in modern computer systems. Formal verification also currently requires highly specialized engineers with deep knowledge of software technology and mathematical theorem-proving techniques.
| Cyber | Formal | Trust |
Embedded computing systems are ubiquitous in critical infrastructure, vehicles, smart devices, and military systems. Conventional wisdom once held that cyberattacks against embedded systems were not a concern since they seldom had traditional networking connections on which an attack could occur. However, attackers have learned to bridge air gaps that surround the most sensitive embedded systems, and network connectivity is now being extended to even the most remote of embedded systems.
| Cyber | Formal | Trust |
Dramatic success in machine learning has led to a torrent of Artificial Intelligence (AI) applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by the machine’s current inability to explain their decisions and actions to human users (Figure 1). The Department of Defense (DoD) is facing challenges that demand more intelligent, autonomous, and symbiotic systems. Explainable AI—especially explainable machine learning—will be essential if future warfighters are to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners.
Embedded systems form a ubiquitous, networked, computing substrate that underlies much of modern technological society. Such systems range from large supervisory control and data acquisition (SCADA) systems that manage physical infrastructure to medical devices such as pacemakers and insulin pumps, to computer peripherals such as printers and routers, to communication devices such as cell phones and radios, to vehicles such as airplanes and satellites. Such devices have been networked for a variety of reasons, including the ability to conveniently access diagnostic information, perform software updates, provide innovative features, lower costs, and improve ease of use.
| Cyber | Formal | Trust |