Trust in the ‘Second Machine-Age’
It’s probable that no other profession or institution relies on trust more than the military. In fact, trust, rather than control, shapes modern military command philosophies. In truth, if you strip trust away, no organisation can survive in an ever-evolving world.
The so-called ‘Second Machine Age’, with a focus on automating cognitive rather than manually intensive tasks, sheds light on traditional notions of trust. The significant growth of Robotic Autonomous Systems (RAS) within defence has been widely lauded – the British Army recently unveiled plans to make greater use of RAS in a bid to prepare the forces to combat future battlefield challenges, and automation is considered particularly effective for delivering dull, dirty and dangerous tasks. However, RAS brings a range of ‘human’ issues which can lead to significant safety, legal and ethical implications. This piece explores how the development of trust in the use of RAS is paramount for ensuring they’re fully accepted and used appropriately to meet mission goals legally, safely and ethically.
Enhancing collaboration
The scope and scale of autonomy in the military is expected to rise dramatically over the coming years and this will require a change in approach as the autonomous system (AS) becomes more of a collaborative team member. High performing, collaborative human teams require utmost trust for delivering effective mission-critical tasks. This is where design comes into play, as it will be critical to create features and functionality that allow and enhance collaborative relationships between the human element and the AS.
With this in mind, we have developed a construct that helps identify key design features to be included in highly autonomous systems to enhance trust. The findings determine that the system should be understandable, transparent, humanised and intuitive with additional AS features and performance found to heighten trust, including aspects such as repeatability and reliability. The construct draws on two compatible approaches, the first being anthropomorphism, an inference process which attributes human-related characteristics to machines such as the ability for conscious feeling, which help to enhance trust. The second is a three-layered model which covers ‘dispositional trust’, which relates to an individual’s pre-disposed tendency to trust, ‘situational trust’, which relates to the context such as environmental setting, and ‘learned trust’, which is particularly relevant as it relates to the design features that may affect perceptions of performance and level in trust. The model provides a new lens for conceptualising the human-related aspects in trust development which can be used as a basis for the design of autonomous vehicles.
A greater degree of trust can be achieved by incorporating design features in five key areas, alongside higher levels of anthropomorphism, as has been found in trials with uncrewed vehicles (UxVs). The five areas include:
- Transparency - the explicit portrayal of the inner workings and logic of the AS
- Appearance - a well-designed interface that is aesthetically pleasing, with anthropomorphic features including name, gender and appropriate essential characteristics
- Ease of use - the provision of enhanced system usability and visual clarity of data, with ongoing salient feedback
- Communication style - the use of verbal communication, instead of text, with human voice rather than synthetic speech
- Level of operator control – highly autonomous machines may take the operator out of the loop altogether, but keeping the operator in the loop in some way can enhance trust
Trust built through training
Impressive design, however, means nothing unless users are trained effectively in how the AS works. Trust needs to be built through training, and people need to fully understand how autonomous capabilities work in order to trust the functionality. Emerging training technologies such as Virtual Reality (VR) and Augmented Reality (AR) are vital for helping users understand a system’s plan, action or decision and can go a long way to enhancing operator trust. Looking at uncrewed vehicles (UxVs) in the military, both technologies should be seen as another asset to be deployed to undertake missions and collect key data and information. In this context, a significant proportion of the training gap is very much limited to capabilities specific to different UxV types – much like those limited to e.g., a new helicopter. As such, this gap can be addressed by acquiring on-the-job knowledge, just as capabilities and limitations would need to be learnt with respect to a new helicopter.