The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems

The trustworthiness of Robots and Autonomous Systems (RAS) has gained a prominent position on many research agendas towards fully autonomous systems. This research systematically explores, for the first time, the key facets of humancentered AI (HAI) for trustworthy RAS. In this article, five key properties of a trustworthy RAS initially have been identified. RAS must be (i) safe in any uncertain and dynamic surrounding environments; (ii) secure, thus protecting itself from any cyberthreats; (iii) healthy with fault tolerance; (iv) trusted and easy to use to allow effective human-machine interaction (HMI), and (v) compliant with the law and ethical expectations. Then, the challenges in implementing trustworthy autonomous system are analytically reviewed, in respects of the five key properties, and the roles of AI technologies have been explored to ensure the trustiness of RAS with respects to safety, security, health and HMI, while reflecting the requirements of ethics in the design of RAS. While applications of RAS have mainly focused on performance and productivity, the risks posed by advanced AI in RAS have not received sufficient scientific attention. Hence, a new acceptance model of RAS is provided, as a framework for requirements to human-centered AI and for implementing trustworthy RAS by design. This approach promotes human-level intelligence to augments human’s capacity while focusing on contributions to

[1]  Jinsheng Gao,et al.  A reasoning system about knowledge extraction in human-computer interaction , 2016, 2016 Chinese Control and Decision Conference (CCDC).

[2]  Angelo Cangelosi,et al.  A Developmental Cognitive Architecture for Trust and Theory of Mind in Humanoid Robots , 2020, IEEE Transactions on Cybernetics.

[3]  Nils Ole Tippenhauer,et al.  The KNOB is Broken: Exploiting Low Entropy in the Encryption Key Negotiation Of Bluetooth BR/EDR , 2019, USENIX Security Symposium.

[4]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[5]  Ashutosh Tiwari,et al.  The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[6]  Ouarda Hachour Neural path planning for mobile robots , 2011 .

[7]  Stephan Lukosch Designing for Augmented Humans and Intelligence , 2019, CSCWD.

[8]  Gonçalo Carriço,et al.  The EU and artificial intelligence: A human-centred perspective , 2018 .

[9]  Roba Abbas,et al.  Ethics and System Design in a New Era of Human-Computer Interaction [Guest Editorial] , 2019, IEEE Technol. Soc. Mag..

[10]  Rajkumar Roy,et al.  A Design Approach to IoT Endpoint Security for Production Machinery Monitoring , 2019, Sensors.

[11]  Hajime Asama,et al.  A system for self-diagnosis of an autonomous mobile robot using an internal state sensory system: fault detection and coping with the internal condition , 2003, Adv. Robotics.

[12]  E. P. Nadeer,et al.  Hybrid System Model Based Fault Diagnosis of Automotive Engines , 2018 .

[13]  Sanchuan Xu A survey of knowledge-based intelligent fault diagnosis techniques , 2019 .

[14]  Matti Valovirta,et al.  Experimental Security Analysis of a Modern Automobile , 2011 .

[15]  Son N. Tran,et al.  Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Syed S. H. Rizvi,et al.  A Threat to Vehicular Cyber Security and the Urgency for Correction , 2017 .

[17]  Xianbin Wang,et al.  Fast Authentication and Progressive Authorization in Large-Scale IoT: How to Leverage AI for Security Enhancement , 2019, IEEE Network.

[18]  J. C. Gerdes,et al.  Implementable Ethics for Autonomous Vehicles , 2016 .

[19]  Roberto Baldoni,et al.  Survey on the Usage of Machine Learning Techniques for Malware Analysis , 2017, Comput. Secur..

[20]  Ciro Natale,et al.  A Distributed Tactile Sensor for Intuitive Human-Robot Interfacing , 2017, J. Sensors.

[21]  Virender Ranga,et al.  Machine Learning Based Intrusion Detection Systems for IoT Applications , 2019, Wireless Personal Communications.

[22]  Xiang Yang Xu,et al.  Adaptive control of the shifting process in automatic transmissions , 2017 .

[23]  Manuela Veloso A Few Issues on Human-Robot Interaction for Multiple Persistent Service Mobile Robots , 2014, AAAI Fall Symposia.

[24]  Bernt Schiele,et al.  Computer vision systems , 2003, Machine Vision and Applications.

[25]  Stefan Katzenbeisser,et al.  Security in Autonomous Systems , 2019, 2019 IEEE European Test Symposium (ETS).

[26]  David A. Wagner,et al.  Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).

[27]  Jagadish Chandra Mohanta,et al.  A knowledge based fuzzy-probabilistic roadmap method for mobile robot navigation , 2019, Appl. Soft Comput..

[28]  Gang Xu,et al.  Recognition of speed signs in uncertain and dynamic environments , 2019 .

[29]  Rachid Alami,et al.  An implemented theory of mind to improve human-robot shared plans execution , 2016, 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[30]  Michael Fisher,et al.  Probabilistic Model Checking of Robots Deployed in Extreme Environments , 2018, AAAI.

[31]  Angelo Cangelosi,et al.  Would a robot trust you? Developmental robotics model of trust and theory of mind , 2019, Philosophical Transactions of the Royal Society B.

[32]  T. Martin McGinnity,et al.  Linguistic Decision Making for Robot Route Learning , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[33]  Jay Lee,et al.  Industrial Artificial Intelligence for industry 4.0-based manufacturing systems , 2018, Manufacturing Letters.

[34]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[35]  Bo Lang,et al.  Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey , 2019, Applied Sciences.

[36]  Zhang Qingta Design of a Smart Visual Sensor Based on Fast Template Matching , 2013 .

[37]  Norbert Elkmann,et al.  Tactile sensing: A key technology for safe physical human robot interaction , 2011, 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[38]  Sri Parameswaran,et al.  Side channel attacks in embedded systems: A tale of hostilities and deterrence , 2015, Sixteenth International Symposium on Quality Electronic Design.

[39]  Angelo Cangelosi,et al.  When Would You Trust a Robot? A Study on Trust and Theory of Mind in Human-Robot Interactions , 2020, 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN).

[40]  Lyazzat Atymtayeva Automation of HCI Engineering processes: System Architecture and Knowledge Representation , 2015 .

[41]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[42]  Yue Li,et al.  PathMarker: protecting web contents against inside crawlers , 2019, Cybersecur..

[43]  Devesh Bhatt,et al.  Considerations in Assuring Safety of Increasingly Autonomous Systems [STUB] , 2018 .

[44]  Yifan Zhao,et al.  Online Anomaly Detection of Time Series at Scale , 2019, 2019 International Conference on Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA).

[45]  Colin Potts,et al.  Design of Everyday Things , 1988 .

[46]  Wei Zhang,et al.  The roles of initial trust and perceived risk in public’s acceptance of automated vehicles , 2019, Transportation Research Part C: Emerging Technologies.

[47]  Ali Hussein Zolait,et al.  The role of User Entity Behavior Analytics to detect network attacks in real time , 2018, 2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT).

[48]  Alessandra Pedrocchi,et al.  Control of a Humanoid NAO Robot by an Adaptive Bioinspired Cerebellar Module in 3D Motion Tasks , 2019, Comput. Intell. Neurosci..

[49]  Apostolos P. Fournaris,et al.  Exploiting Hardware Vulnerabilities to Attack Embedded System Devices: a Survey of Potent Microarchitectural Attacks , 2017 .

[50]  Hongmei He,et al.  A Comprehensive Obstacle Avoidance System of Mobile Robots Using an Adaptive Threshold Clustering and the Morphin Algorithm , 2018, UKCI.

[51]  Tom Rodden,et al.  Principles of robotics: regulating robots in the real world , 2017, Connect. Sci..

[52]  Chang Nho Cho,et al.  Neural Network Based Adaptive Actuator Fault Detection Algorithm for Robot Manipulators , 2019, J. Intell. Robotic Syst..

[53]  陈佳佳,et al.  Lidar Based Dynamic Obstacle Detection, Tracking and Recognition , 2016 .

[54]  Brian K. Kooy The Internet Encyclopedia of Philosophy , 2009 .

[55]  Guang-Zhong Yang,et al.  Normalization in Training U-Net for 2-D Biomedical Semantic Segmentation , 2018, IEEE Robotics and Automation Letters.

[56]  Xukai Zou,et al.  A Prototype Model for Self-Healing and Self-Reproduction In Swarm Robotics System , 2006, 2006 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing.

[57]  Alex Fridman,et al.  Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy , 2018, ArXiv.

[58]  Qin Zhang,et al.  Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Utilization of Statistical Data and Domain Knowledge in Complex Cases , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[59]  Lotfi A. Zadeh,et al.  Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift , 2008, IEEE Computational Intelligence Magazine.

[60]  Sahin Yildirim,et al.  Fault detection on robot manipulators using artificial neural networks , 2011 .

[61]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches , 2015, IEEE Transactions on Industrial Electronics.

[62]  P. König,et al.  Moral Judgements on the Actions of Self-Driving Cars and Human Drivers in Dilemma Situations From Different Perspectives , 2019, Frontiers in Psychology.

[63]  Spencer Rugaber,et al.  Using knowledge representation to understand interactive systems , 1997, Proceedings Fifth International Workshop on Program Comprehension. IWPC'97.