The human cognitive biases that result in anthropomorphism, the moral confusion surrounding the status of robots, and wider societal concerns related to the deployment of artificial intelligence at scale all motivate the study of robot transparency --- the design of robots such that they may be fully understood by humans. Based on the hypothesis that robot transparency leads to better (in the sense of more accurate) mental models of robots, I investigate how humans perceive and understand a robot when they encounter it, both in online video and direct physical encounter. I also use Amazon Mechanical Turk as a platform to facilitate online experiments with larger population samples. To improve transparency I use a visual real-time transparency tool providing a graphical representation of the internal processing and state of a robot. I also describe and deploy a vocalisation algorithm for transparency. Finally, I modify the form of the robot with a simple bee-like cover, to investigate the effect of appearance on transparency. I find that the addition of a visual or vocalised representation of the internal processing and state of a robot significantly improves the ability of a naive observer to form an accurate model of a robot's capabilities, intentions and purpose. This is a significant result across a diverse, international population sample and provides a robust result about humans in general, rather than one geographic, ethnic or socio-economic group in particular. However, all the experiments were unable to achieve a Mental Model Accuracy (MMA) of more than 59%, indicating that despite improved transparency of the internal state and processing, naive observers' models remain inaccurate, and there is scope for further work. A vocalising, or 'talking', robot greatly increases the confidence of naive observers to report that they understand a robot's behaviour when observed on video. Perhaps we might be more easily deceived by talking robots than silent ones. A zoomorphic robot is perceived as more intelligent and more likeable than a very similar mechanomorphic robot, even when the robots exhibit almost identical behaviour. A zoomorphic form may attract closer visual attention, and whilst this results in an improved MMA, it also diverts attention away from transparency measures, reducing their efficacy to further increase MMA. The trivial embellishment of a robot to alter its form has significant effects on our understanding and attitude towards it. Based on the concerns that motivate this work, together with the results of the robot transparency experiments, I argue that we have a moral responsibility to make robots transparent, so as to reveal their true machine nature. I recommend the inclusion of transparency as a fundamental design consideration for intelligent systems, particularly for autonomous robots. This research also includes the design and development of the 'Instinct' reactive planner, developed as a controller for a mobile robot of my own design. Instinct provides facilities to generate a real-time 'transparency feed'--- a real-time trace of internal processing and state. Instinct also controls agents within a simulation environment, the 'Instinct Robot World'. Finally, I show how two instances of Instinct can be used to achieve a second order control architecture.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:760960 |
| Date | January 2018 |
| Creators | Wortham, Robert H. |
| Contributors | Bryson, Joanna |
| Publisher | University of Bath |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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