Rachel Haga

Test and evaluation (T&E) of AI-enabled systems (AIES) often emphasizes algorithm accuracy over robust, holistic system performance. While this narrow focus may be adequate for some applications of AI, for many complex uses, T&E paradigms removed from operational realism are insufficient. However, leveraging traditional operational testing (OT) methods for to evaluate AIESs can fail to capture novel sources of risk. This brief establishes a common AI vocabulary and highlights OT challenges posed by AIESs by answering the following questions...

Effective training of the broad set of users/operators of systems has downstream impacts on usability, workload, and ultimate system performance that are related to mission success. In order to measure training effectiveness, we designed a survey called the Operational Assessment of Training Scale (OATS) in partnership with the Army Test and Evaluation Center (ATEC). Two subscales were designed to assess the degrees to which training covered relevant content for real operations (Relevance subscale) and enabled self-rated ability to interact with systems effectively after training (Efficacy subscale)....

As the Department of Defense acquires new systems with artificial intelligence (AI) and autonomous (AI&A) capabilities, the test and evaluation (T&E) community will need to adapt to the challenges that these novel technologies present. The goals listed in this AI Roadmap address the broad range of tasks that the T&E community will need to achieve in order to properly test, evaluate, verify, and validate AI-enabled and autonomous systems. It includes issues that are unique to AI and autonomous systems, as well as legacy T&E shortcomings that will be compounded by newer technologies....