UKRI Trustworthy Autonomous Systems Node in Trust

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Engineered systems are increasingly being used autonomously, making decisions and taking actions without human intervention. These Autonomous Systems are already being deployed in industrial sectors but in controlled scenarios (e.g. static automated production lines, fixed sensors). They start to get into difficulties when the task increases in complexity or the environment is uncontrolled (e.g. drones for offshore windfarm inspection), or where there is a high interaction with people and entities in the world (e.g. self-driving cars) or where they have to work as a team (e.g. cobots working in a factory). The EN-TRUST Vision is that these systems learn situations where trust is typically lost unnecessarily, adapting this prediction for specific people and contexts. Stakeholder trust will be managed through transparent interaction, increasing the confidence of the stakeholders to use the Autonomous Systems, meaning that they can be adopted in scenarios never before thought possible, such as doing the jobs that endanger humans (e.g. first responders or pandemic related tasks). The EN-TRUST 'Trust' Node will perform foundational research on how humans and Autonomous Systems (AS) can work together by building a shared reality, based on mutual understanding through trustworthy interaction. The EN-TRUST Node will create a UK research centre of excellence for trust that will inform the design of Autonomous Systems going forward, ensuring that they are widely used and accepted in a variety of applications. This cross-cutting multidisciplinary approach is grounded in Psychology and Cognitive Science and consists of three "pillars of trust": 1) computational models of human trust in AS; 2) adaptation of these models in the face of errors and uncontrolled environments; and 3) user validation and evaluation across a broad range of sectors in realistic scenarios. This EN-TRUST framework will explore how to best establish, maintain and repair trust by incorporating the subjective view of human trust towards Autonomous Systems, thus maximising their positive societal and economic benefits.