Improving robot transparency: Real-time visualisation of robot AI substantially improves understanding in naive observers

Deciphering the behaviour of intelligent others is a fundamental characteristic of our own intelligence. As we interact with complex intelligent artefacts, humans inevitably construct mental models to understand and predict their behaviour. If these models are incorrect or inadequate, we run the risk of self deception or even harm. Here we demonstrate that providing even a simple, abstracted real-time visualisation of a robot's AI can radically improve the transparency of machine cognition. Findings from both an online experiment using a video recording of a robot, and from direct observation of a robot show substantial improvements in observers' understanding of the robot's behaviour. Unexpectedly, this improved understanding was correlated in one condition with an increased perception that the robot was ‘thinking’, but in no conditions was the robot's assessed intelligence impacted. In addition to our results, we describe our approach, tools used, implications, and potential future research directions.

[1]  Joanna Bryson,et al.  Flexible Latching: A Biologically-Inspired Mechanism for Improving the Management of Homeostatic Goals , 2010, Cognitive Computation.

[2]  Michael Fisher,et al.  Verifying autonomous systems , 2013, CACM.

[3]  B. A. MacDonald,et al.  Developer Oriented Visualisation of a Robot Program An Augmented Reality Approach , 2006 .

[4]  Andreas Theodorou,et al.  Designing and implementing transparency for real time inspection of autonomous robots , 2017, Connect. Sci..

[5]  Kerstin Dautenhahn,et al.  Would You Trust a (Faulty) Robot? Effects of Error, Task Type and Personality on Human-Robot Cooperation and Trust , 2015, 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[6]  Dana Kulic,et al.  Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots , 2009, Int. J. Soc. Robotics.

[7]  Lora Aroyo,et al.  User Interaction with User-Adaptive Information Filters , 2007, HCI.

[8]  Andreas Theodorou,et al.  Robot transparency, trust and utility , 2016, Connect. Sci..

[9]  Lawrence W Barsalou,et al.  Simulation, situated conceptualization, and prediction , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[10]  Emily C. Collins,et al.  Help! I Can't Reach the Buttons: Facilitating Helping Behaviors Towards Robots , 2015, Living Machines.

[11]  Christian Blum,et al.  Towards an Ethical Robot: Internal Models, Consequences and Ethical Action Selection , 2014, TAROS.

[12]  Joanna Bryson,et al.  Extended ramp goal module: Low-cost behaviour arbitration for real-time controllers based on biological models of dopamine cells , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[13]  Bruce A. MacDonald,et al.  Developer oriented visualisation of a robot program , 2006, HRI '06.

[14]  Pamela J. Hinds,et al.  Who Should I Blame? Effects of Autonomy and Transparency on Attributions in Human-Robot Interaction , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.

[15]  Interaction with User-Adaptive Information Filters , .

[16]  Andrea Lockerd Thomaz,et al.  Effects of nonverbal communication on efficiency and robustness in human-robot teamwork , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Robert H. Wortham,et al.  Instinct: a biologically inspired reactive planner for embedded environments , 2016 .

[18]  Andreas Theodorou,et al.  ABOD3: A Graphical Visualization and Real-Time Debugging Tool for BOD Agents , 2017, EUCognition.

[19]  Francis T. McAndrew,et al.  On the nature of creepiness , 2016 .

[20]  Simon Colton,et al.  Evolving Behaviour Trees for the Commercial Game DEFCON , 2010, EvoApplications.

[21]  이영식 Communication 으로서의 영어교육 , 1986 .

[22]  Joanna J. Bryson,et al.  Intelligence by design: principles of modularity and coordination for engineering complex adaptive agents , 2001 .

[23]  Rebecca Saxe,et al.  Reading minds versus following rules: Dissociating theory of mind and executive control in the brain , 2006, Social neuroscience.

[24]  Min Kyung Lee,et al.  How do people talk with a robot?: an analysis of human-robot dialogues in the real world , 2009, CHI Extended Abstracts.

[25]  DautenhahnKerstin,et al.  Principles of robotics , 2017 .

[26]  Henriette Cramer,et al.  Interaction with user-adaptive information filters.: trust, transparency and acceptance. , 2007, CHI Extended Abstracts.

[27]  T. Kanda,et al.  Six-and-a-half-month-old children positively attribute goals to human action and to humanoid-robot motion , 2005 .