Editor's Note

Understanding Competencies of Machine Learning Systems and Building Human-AI Trust

BAE Systems’ MindfuL technology, developed under a DARPA contract, helps build understanding of a machine learning system’s capabilities by providing a user with statements such as these. (BAE Systems)

Greater incorporation of autonomy into aerospace will require more understanding of how autonomous systems function, both for certification and for effective partnership between these systems and their human users.

Machine learning, commonly used for pattern analysis and recognition, plays a major part in artificial intelligence for autonomy systems, particularly in pairing with sensors and cameras (i.e. obstacle avoidance, surveillance, search-and-rescue).

Improving our understanding of how ML systems work — along with when they are operating in unfamiliar contexts, and passing that information on to a human in the loop — is the subject of a DARPA project called Competency-Aware Machine Learning (CAML).

CAML’s objective is less about certification and more about providing operators insight into the capabilities of a given machine learning system, for example to assist in choosing the right ML assets for a task, and offering real-time alerts when a task moves outside of a system’s competencies and user intervention is needed.

“The goal is to increase the trust/competency understanding between the user and the ML system by enabling the system to understand its own competency, and have the capability to convey it to the users,” DARPA project manager Dr. Jiangying Zhou told Avionics International.

Since ML systems acquire their task-specific capabilities by analyzing lots of data, the CAML program will augment the ML system during the learning stage by enabling it to both learn how to do the task and understand the conditions in the data under which it performs well or poorly.

This is accomplished by keeping tack of scenarios the ML system encounters in the data, finding a way to tie the scenario with how well it performs the task, and keeping a memory of those experiences — not too different from how humans learn, according to Dr. Zhou.

“We remember our experiences of doing a task and remember when we did well and when we failed, and we make associations between our skills and the conditions,” Dr. Zhou said. “Then, we know when we have encountered a scenario that we might not be able to do well. The CAML program is trying to develop this capability, as a form of meta-learning, into the ML system, so the ML can have a form of self-awareness of what is learned to do.

One contracted company, BAE Systems, recently delivered to DARPA a CAML software program it calls MindfuL, which the company describes as being able to “audit” an ML system and provide end-users insight to build trust in the technology, including a baseline capability to detect when the system encounters a new environment for which it has not been trained.

“The MindfuL system provides estimates of conditions which affect ML performance, the underlying strategies the ML uses to perform the task, and a predicted competency score,” Julia Filiberti, a scientist at BAE Systems’ FAST Labs, told Avionics.

That competency score is determined by comparing past performance to a presented scenario, identifying conditions for a proposed use of the system through sensor data and comparing it to known experiences — or past situations and performance — stored in the program’s memory bank.

In the memory bank, the MindfuL system aggregates features of an environment into “topics,” such as “dark” or “rain” or “shiny,” which are then used to describe environments and compare them to new experiences, therefore classifying sensor inputs as “familiar” or “unfamiliar.” The difference between a new environment and past experiences is then calculated and presented to users as well as a relative entropy score.

“If a statistically significant relative entropy score is found, the operator is alerted to the unfamiliar situation,” Filiberti explained to Avionics. “Alerting the user to untrained environments further improves human understanding of the limitations and/or uncertainties inherent to the underlying ML system; these alerts increase transparency and build trust.”

BAE’s system communicates with the user through two interfaces. The first, for live use during deployment, sends the user statements about the program’s capabilities compared to its current environment, providing statements about conditions, strategies and performance estimates as well as potentially recommending user actions, such as manual override. The second interface allows the user to review data from past experiences and label them to further improve the accuracy of the system’s output statements, as well as attaching human-understandable context to machine-derived characteristics.

The MindfuL system was developed with size, weight and power (SWaP) requirements in mind, according to Filiberti, using memory mechanisms that reduce storage requirements for video sensory inputs by a factor of 40,000 or more, allowing experiences from 1 year of video sensor inputs at a 55 megabyte/second data rate on less than 2 terabytes of storage.

BAE’s solution also only needs to interact with the underlying ML system during its training process; for real-time application, it just needs access to the same sensor data ingested by the ML system.

DARPA’s four-year program aims to develop general competency-aware capabilities that can be incorporated into all ML systems, with potential applications including autonomous navigation and obstacle avoidance, UAV search and rescue, autonomous ground resupply vehicles, self-driving taxis and missing planning. Targeted platforms include unmanned air and ground system as well as robots.

“The current focus of the program testing on image classification and object identification,” Filiberti said. “We are pursuing application to ground-vehicle navigation, leader-follower convoying, and satellite imagery automated target recognition … Within this year we will have finished developing the capability to monitor several sensor data streams simultaneously along with other ML systems on board, such as a fault detection/response ML system.”

One natural application of the MindfuL system, according to Filiberti, is identifying degraded sensors, as environments will appear new when input data is compromised and therefore bears less resemblance to a system’s training data.

BAE Systems also intends to explore route planning as an application of the system, which could apply to ground, air or maritime autonomous navigation.

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