Understandable and trustworthy explainable robots: A sensemaking perspective

Abstract This article discusses the fundamental requirements for making explainable robots trustworthy and comprehensible for non-expert users. To this extent, we identify three main issues to solve: the approximate nature of explanations, their dependence on the interaction context and the intrinsic limitations of human understanding. The article proposes an organic solution for the design of explainable robots rooted in a sensemaking perspective. The establishment of contextual interaction boundaries, combined with the adoption of plausibility as the main criterion for the evaluation of explanations and of interactive and multi-modal explanations, forms the core of this proposal.

[1]  Pat Langley,et al.  Explainable Agency in Human-Robot Interaction , 2016 .

[2]  B. Malle.  How the Mind Explains Behavior: Folk Explanations, Meaning, and Social Interaction , 2004 .

[3]  Tim Miller,et al.  Explanation in Artificial Intelligence: Insights from the Social Sciences , 2017, Artif. Intell..

[4]  E. Vincent Cross,et al.  Explaining robot actions , 2012, 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[5]  Markus Vincze,et al.  Towards a Robot for Supporting Older People to Stay Longer Independent at Home , 2014, ISR 2014.

[6]  Tong Wang,et al.  Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute , 2019, ICML.

[7]  Susan Leigh Star.  This is Not a Boundary Object: Reflections on the Origin of a Concept , 2010 .

[8]  Filip Karlo Dosilovic,et al.  Explainable artificial intelligence: A survey , 2018, 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[9]  Olivia B. Newton,et al.  Effects of Social Cues on Social Signals in Human-Robot Interaction During a Hallway Navigation Task , 2018, Proceedings of the Human Factors and Ergonomics Society Annual Meeting.

[10]  Thomas B. Sheridan,et al.  Human–Robot Interaction , 2016, Hum. Factors.

[11]  Brian Scassellati,et al.  Social robots for education: A review , 2018, Science Robotics.

[12]  Jeffrey C. Zemla,et al.  Evaluating everyday explanations , 2017, Psychonomic bulletin & review.

[13]  Ester Martínez-Martín,et al.  Personal Robot Assistants for Elderly Care: An Overview , 2018, Personal Assistants.

[14]  Trevor J. M. Bench-Capon,et al.  Discovering Inconsistency through Examination Dialogues , 2005, IJCAI.

[15]  G. Harman.  The Inference to the Best Explanation , 1965 .

[16]  E. Pronin,et al.  Chapter 1 The Introspection Illusion , 2009 .

[17]  Tim Miller,et al.  A Grounded Interaction Protocol for Explainable Artificial Intelligence , 2019, AAMAS.

[18]  F. Keil,et al.  Explanation and understanding , 2015 .

[19]  Bruce A. MacDonald,et al.  Robots in Older People's Homes to Improve Medication Adherence and Quality of Life: A Randomised Cross-Over Trial , 2014, ICSR.

[20]  D. Hilton.  Conversational processes and causal explanation. , 1990 .

[21]  Horst-Michael Groß,et al.  Robot companion for domestic health assistance: Implementation, test and case study under everyday conditions in private apartments , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[22]  T. Lombrozo.  Causal–explanatory pluralism: How intentions, functions, and mechanisms influence causal ascriptions , 2010, Cognitive Psychology.

[23]  Or Biran,et al.  Explanation and Justification in Machine Learning : A Survey Or , 2017 .

[24]  Maartje M. A. de Graaf,et al.  How People Explain Action (and Autonomous Intelligent Systems Should Too) , 2017, AAAI Fall Symposia.

[25]  Mohamed Abouelenien,et al.  Deception Detection using Real-life Trial Data , 2015, ICMI.

[26]  Charles S. Peirce,et al.  Pragmatism as a Principle and Method of Right Thinking: The 1903 Harvard Lectures on Pragmatism , 1997 .

[27]  S. L. Star,et al.  This is Not a Boundary Object: Reflections on the Origin of a Concept , 2010 .

[28]  Mohan S. Kankanhalli,et al.  Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda , 2018, CHI.

[29]  Weng-Keen Wong,et al.  Too much, too little, or just right? Ways explanations impact end users' mental models , 2013, 2013 IEEE Symposium on Visual Languages and Human Centric Computing.

[30]  K. Weick,et al.  Organizing and the Process of Sensemaking , 2005 .

[31]  S. Wermter,et al.  Explainable Goal-Driven Agents and Robots - A Comprehensive Review and New Framework , 2020, ArXiv.

[32]  H. Grice.  Logic and conversation , 1975 .

[33]  Pedro J. Sanz,et al.  An autonomous assistant robot for book manipulation in a library , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[34]  Davide Calvaresi,et al.  Explainable Agents and Robots: Results from a Systematic Literature Review , 2019, AAMAS.

[35]  Andrea Gawrylewski,et al.  The Introspection Illusion , 2018, Scientific American Mind.

[36]  Joelle Pineau,et al.  Pearl: A Mobile Robotic Assistant for the Elderly , 2002 .

[37]  Amina Adadi,et al.  Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) , 2018, IEEE Access.

[38]  Joe Tullio,et al.  How it works: a field study of non-technical users interacting with an intelligent system , 2007, CHI.

[39]  T. Tsujimura,et al.  Librarian robot controlled by mathematical AIM model , 2009, 2009 ICCAS-SICE.

[40]  Tim Miller,et al.  Towards a Grounded Dialog Model for Explainable Artificial Intelligence , 2018, ArXiv.

[41]  Thomas Hellström,et al.  Understandable robots - What, Why, and How , 2018, Paladyn J. Behav. Robotics.

[42]  Helen F. Hastie,et al.  Explain Yourself: A Natural Language Interface for Scrutable Autonomous Robots , 2018, HRI 2018.

[43]  F. Keil.  Folkscience: coarse interpretations of a complex reality , 2003, Trends in Cognitive Sciences.

[44]  D Feil-Seifer,et al.  Socially Assistive Robotics , 2011, IEEE Robotics & Automation Magazine.

[45]  Raymond Sheh,et al.  Different XAI for Different HRI , 2017, AAAI Fall Symposia.

[46]  Cynthia Rudin,et al.  Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.

[47]  J. Wisdom.  Other Minds , 1941, Royal Institute of Philosophy Lectures.

[48]  Peter R. Lewis,et al.  Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems , 2018, IEEE Technology and Society Magazine.

[49]  Travis J. Wiltshire,et al.  Toward understanding social cues and signals in human–robot interaction: effects of robot gaze and proxemic behavior , 2013, Front. Psychol..

[50]  Hani Hagras,et al.  Toward Human-Understandable, Explainable AI , 2018, Computer.

[51]  Been Kim,et al.  Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.

[52]  Adrian David Cheok,et al.  Internet of Speech: A Conceptual Model , 2018, REKA 2018.

[53]  Alison Cawsey,et al.  User modelling in interactive explanations , 1993, User Modeling and User-Adapted Interaction.

[54]  Feng Wu,et al.  KeJia Robot-An Attractive Shopping Mall Guider , 2015, ICSR.

[55]  Douglas Walton,et al.  Examination dialogue: An argumentation framework for critically questioning an expert opinion , 2006 .

[56]  Jie Sun.  Emotion recognition and expression in therapeutic social robot design , 2014, HAI.

[57]  T. Lombrozo.  The structure and function of explanations , 2006, Trends in Cognitive Sciences.

[58]  Vr Sanal Kumar,et al.  Conceptual Design of a Wi-Fi and GPS Based Robotic Library Using an Intelligent System , 2015 .

[59]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[60]  O. Khatib,et al.  The rise of social robots : a review of the recent literature , 2016 .

[61]  Huai Liu,et al.  Metamorphic Testing , 2018, ACM Comput. Surv..

[62]  B. Malle.  Attribution theories: How people make sense of behavior. , 2011 .

[63]  Päivi Heikkilä,et al.  A Social Service Robot in a Shopping Mall: Expectations of the Management, Retailers and Consumers , 2017, HRI.

[64]  Tony Belpaeme,et al.  Children conform, adults resist: A robot group induced peer pressure on normative social conformity , 2018, Science Robotics.

[65]  Pat Langley,et al.  Explainable Agency for Intelligent Autonomous Systems , 2017, AAAI.

[66]  P. Thagard,et al.  Explanatory coherence , 1993 .

[67]  Anca D. Dragan,et al.  Explainable Robotic Systems , 2018, HRI.

[68]  B. Malle,et al.  Actor-observer asymmetries in explanations of behavior: new answers to an old question. , 2007, Journal of personality and social psychology.

[69]  Kathleen M. Sutcliffe,et al.  Special Issue: Frontiers of Organization Science, Part 1 of 2: Organizing and the Process of Sensemaking , 2005, Organ. Sci..

[70]  Siobhan Chapman.  Logic and Conversation , 2005 .

[71]  Sofiane Boucenna,et al.  Evaluating the Engagement with Social Robots , 2015, International Journal of Social Robotics.

[72]  T. Lombrozo,et al.  Inference to the Best Explanation (IBE) Versus Explaining for the Best Inference (EBI) , 2015, Science Education.

[73]  Alan R. Wagner,et al.  Overtrust of robots in emergency evacuation scenarios , 2016, 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[74]  Brian Scassellati,et al.  Socially assistive robotics [Grand Challenges of Robotics] , 2007, IEEE Robotics & Automation Magazine.

[75]  Tom Ziemke,et al.  Physical vs. Virtual Agent Embodiment and Effects on Social Interaction , 2016, IVA.

[76]  Andreas Theodorou,et al.  Why is my robot behaving like that? Designing transparency for real time inspection of autonomous robots , 2016 .

[77]  Alpha Lam.  Using Remote Cache Service for Bazel , 2018, ACM Queue.

[78]  T. Lombrozo,et al.  Simplicity and probability in causal explanation , 2007, Cognitive Psychology.

[79]  Søren Overgaard,et al.  The Problem of Other Minds: Wittgenstein's Phenomenological Perspective , 2006 .

[80]  Richard Wiseman,et al.  The Eyes Don’t Have It: Lie Detection and Neuro-Linguistic Programming , 2012, PloS one.

[81]  Satoshi Aoki,et al.  Scalable Component-Based Manzai Robots as Automated Funny Content Generators , 2016, J. Robotics Mechatronics.

[82]  Trevor Darrell,et al.  Multimodal Explanations: Justifying Decisions and Pointing to the Evidence , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[83]  Douglas Walton,et al.  A dialogue system specification for explanation , 2011, Synthese.

[84]  Franco Turini,et al.  A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..

[85]  Leema K. Berland,et al.  Making sense of argumentation and explanation , 2009 .