UKRI Trustworthy Autonomous Systems Node in Trust

工程系统越来越多地被自主使用，在没有人工干预的情况下做出决策和采取行动。这些自主系统已经部署在工业部门，但在受控场景中(例如，静态自动化生产线、固定传感器)。当任务的复杂性增加或环境失控时(例如，用于海上风电场检查的无人机)，或者与世界上的人和实体有高度互动(例如，自动驾驶汽车)，或者他们必须作为团队工作(例如，在工厂工作的Cobots)，他们开始遇到困难。信任愿景是，这些系统学习通常不必要地失去信任的情况，根据特定的人和环境调整这种预测。利益攸关方的信任将通过透明的互动来管理，增加利益攸关方使用自主系统的信心，这意味着它们可以在以前从未想过的情况下被采用，例如从事危及人类的工作(例如，第一反应人员或与流行病有关的任务)。En-Trust“Trust”节点将对人类和自治系统(AS)如何通过构建共享的现实、基于相互理解(通过可信的交互)而协同工作进行基础性研究。EnTRUST节点将创建一个英国卓越的TRUST研究中心，该中心将为未来自主系统的设计提供信息，确保它们在各种应用中得到广泛使用和接受。这种交叉的多学科方法以心理学和认知科学为基础，由三个“信任支柱”组成：1)人类在AS中信任的计算模型；2)这些模型在面对错误和失控环境时的适应性；3)在现实场景中的广泛领域的用户验证和评估。这一信任框架将探索如何通过纳入人类对自主系统的信任的主观观点来最好地建立、维持和修复信任，从而最大化其积极的社会和经济效益。

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.