Advances in Intelligent Systems and Computing 1152

Tareq AhramRedha TaiarVincent Gremeaux-BaderKamiar Aminian Editors

Human Interaction, Emerging Technologies and Future Applications IIProceedings of the 2nd International Conference on Human Interaction and Emerging Technologies: Future Applications (IHIET – AI 2020), April 23–25, 2020, Lausanne, Switzerland

Advances in Intelligent Systems and Computing

Volume 1152

Series Editor

Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences,Warsaw, Poland

Advisory Editors

Nikhil R. Pal, Indian Statistical Institute, Kolkata, India

Rafael Bello Perez, Faculty of Mathematics, Physics and Computing,Universidad Central de Las Villas, Santa Clara, Cuba

Emilio S. Corchado, University of Salamanca, Salamanca, Spain

Hani Hagras, School of Computer Science and Electronic Engineering,University of Essex, Colchester, UK

László T. Kóczy, Department of Automation, Széchenyi István University,Gyor, Hungary

Vladik Kreinovich, Department of Computer Science, University of Texasat El Paso, El Paso, TX, USA

Chin-Teng Lin, Department of Electrical Engineering, National ChiaoTung University, Hsinchu, Taiwan

Jie Lu, Faculty of Engineering and Information Technology,University of Technology Sydney, Sydney, NSW, Australia

Patricia Melin, Graduate Program of Computer Science, Tijuana Instituteof Technology, Tijuana, Mexico

Nadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro,Rio de Janeiro, Brazil

Ngoc Thanh Nguyen , Faculty of Computer Science and Management,Wrocław University of Technology, Wrocław, Poland

Jun Wang, Department of Mechanical and Automation Engineering,The Chinese University of Hong Kong, Shatin, Hong Kong

The series “Advances in Intelligent Systems and Computing” contains publicationson theory, applications, and design methods of Intelligent Systems and IntelligentComputing. Virtually all disciplines such as engineering, natural sciences, computerand information science, ICT, economics, business, e-commerce, environment,healthcare, life science are covered. The list of topics spans all the areas of modernintelligent systems and computing such as: computational intelligence, soft comput-ing including neural networks, fuzzy systems, evolutionary computing and the fusionof these paradigms, social intelligence, ambient intelligence, computational neuro-science, artificial life, virtual worlds and society, cognitive science and systems,Perception and Vision, DNA and immune based systems, self-organizing andadaptive systems, e-Learning and teaching, human-centered and human-centriccomputing, recommender systems, intelligent control, robotics and mechatronicsincluding human-machine teaming, knowledge-based paradigms, learning para-digms, machine ethics, intelligent data analysis, knowledge management, intelligentagents, intelligent decision making and support, intelligent network security, trustmanagement, interactive entertainment, Web intelligence and multimedia.

The publications within “Advances in Intelligent Systems and Computing” areprimarily proceedings of important conferences, symposia and congresses. Theycover significant recent developments in the field, both of a foundational andapplicable character. An important characteristic feature of the series is the shortpublication time and world-wide distribution. This permits a rapid and broaddissemination of research results.

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More information about this series at http://www.springer.com/series/11156

Tareq Ahram • Redha Taiar •

Vincent Gremeaux-Bader •

Kamiar AminianEditors

Human Interaction, EmergingTechnologies and FutureApplications IIProceedings of the 2nd InternationalConference on Human Interactionand Emerging Technologies: FutureApplications (IHIET – AI 2020),April 23–25, 2020, Lausanne, Switzerland

123

EditorsTareq AhramInstitute for Advanced Systems EngineeringUniversity of Central FloridaOrlando, FL, USA

Redha TaiarCampus du Moulin de la HousseUniversité de Reims ChampagneArdenne GRESPIReims Cedex, France

Vincent Gremeaux-BaderDépartement de l’appareil locomoteur,Champ de l’AirCentre Hospitalier UniversitaireVaudois (CHUV)Lausanne, Switzerland

Kamiar AminianLaboratory of Movement Analysisand MeasurementÉcole Polytechnique Fédéralede LausanneLausanne, Switzerland

ISSN 2194-5357 ISSN 2194-5365 (electronic)Advances in Intelligent Systems and ComputingISBN 978-3-030-44266-8 ISBN 978-3-030-44267-5 (eBook)https://doi.org/10.1007/978-3-030-44267-5

© The Editor(s) (if applicable) and The Author(s), under exclusive licenseto Springer Nature Switzerland AG 2020This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, expressed or implied, with respect to the material containedherein or for any errors or omissions that may have been made. The publisher remains neutral with regardto jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AGThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This book, entitled Human Interaction and Emerging Technologies II: FutureApplications, aims to provide a global forum for presenting and discussing novelhuman interaction, emerging technologies and engineering approaches, tools,methodologies, techniques, and solutions for integrating people, concepts, trends,and applications in all areas of human interaction endeavor. Such applicationsinclude, but are not limited to, health care and medicine, sports medicine, trans-portation, optimization, and urban planning for infrastructure development, man-ufacturing, social development, a new generation of service systems, as well assafety, risk assessment, and cybersecurity in both civilian and military contexts.Indeed, rapid progress in developments in cognitive computing, modeling, andsimulation, as well as smart sensor technology, will have a profound effect on theprinciples of human interaction and emerging technologies at both the individualand societal levels in the near future. This interdisciplinary book will also expandthe boundaries of the current state-of-the-art by investigating the pervasive com-plexity that underlies the most profound problems facing contemporary societytoday. Emerging technologies included in this book cover a variety of technologiessuch as educational technology, information technology, nanotechnology,biotechnology, cognitive science, robotics, and artificial intelligence.

The book, which gathers selected papers presented at the 2nd InternationalConference on Human Interaction and Emerging Technologies: FutureApplications (IHIET—AI 2020), April 23–25, 2020, Lausanne, Switzerland,focuses on advancing the theory and applications for human interaction require-ments as part of an overall system development life cycle, by adopting ahuman-centered design approach that utilizes and expands on the current knowl-edge of user-centered design and systems engineering supported by cognitivesoftware and engineering, data analytics, simulation and modeling, and next gen-eration visualizations. This book also presents many innovative studies with aparticular emphasis on the development of technology throughout the life cycledevelopment process, including the consideration of user experience in the designof human interfaces for virtual, augmented, and mixed reality applications.

v

Reflecting on the above-outlined perspective, the papers contained in this vol-ume are organized into nine sections, including

Section 1 Human-centered DesignSection 2 Artificial Intelligence and ComputingSection 3 Human–computer InteractionSection 4 Augmented, Virtual, and Mixed Reality SimulationSection 5 Applications in Sport and MedicineSection 6 Healthcare and Medical ApplicationsSection 7 Human Technology and Future of WorkSection 8 Management, Training, and Business Applications

We would like to extend our sincere thanks to the Centre HospitalierUniversitaire Vaudois (CHUV) in Lausanne, Switzerland, for their collaborationand kind support. Our appreciation also goes to the members of the ScientificProgram Advisory Board who have reviewed the accepted papers that are presentedin this volume.

We hope that this book, which presents the current state-of-the-art in humaninteraction and emerging technologies, will be a valuable source of both theoreticaland applied knowledge enabling the human-centered design and applications of avariety of products, services, and systems for their safe, effective, and pleasurableuse by people around the world.

Tareq AhramRedha Taiar

Vincent Gremeaux-Bader

April 2020

Kamiar Aminian

vi Preface

Contents

Human-Centered Design

Designing Presence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Kevin Clark and Kazuhiko Yamazaki

Hume’s Guillotine in Designing Ethically Intelligent Technologies . . . . . 10Pertti Saariluoma

Symbols and Functions in Human Machine Interface:Are Google Icons a Possible Solution for Intercultural Usability? . . . . . 16Andreas Papageorgiou, Kamalatharsi Mutuura, and Oliver Christ

A Democratic, Green Ocean Management Frameworkfor Environmental, Social and Governance (ESG) Compliance . . . . . . . 21Evangelos Markopoulos, Ines Selma Kirane, Emma Luisa Gann,and Hannu Vanharanta

An AcciMap of the Edinburgh Tram Network ProjectDelivery Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Amangul A. Imanghaliyeva

Workload and Visual Scanning Techniques of Expert and NoviceHelicopter Pilots During Simulated Flight in Open Sea . . . . . . . . . . . . . 39Giuseppe Rainieri, Federico Fraboni, Martin Tušl, Gabriele Russo,Davide Giusino, Marco De Angelis, Annagrazia Tria,and Luca Pietrantoni

Kansei Design and Its Applications in Architectureand the Built Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45Paolo Caratelli and Maria Alessandra Misuri

Avoiding Post-Merger Corporate Downsize Restructuring:The Democratic Employee-Culture Fit Model (DeECFit) . . . . . . . . . . . . 51Evangelos Markopoulos, Ines Selma Kirane, Emma Luisa Gann,and Hannu Vanharanta

vii

Interview Survey Method for Extracting Cultural Trait Applicableto Concept Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63Uulen Tumurkhaduur, Baiyu Zhang, and Kazuhiko Yamazaki

Design of Human-Centred Technical Systems, Productsand Human Capital Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69Evgeny Kolbachev, Elena Sidorova, and Polina Vaneeva

A Dual-Axis Force Sensor with Passive Eddy Current Damperfor Precision Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75Xiantao Sun, Wenjie Chen, Weihai Chen, and Cungang Hu

A Critical Analysis of Music Recommendation Systemsand New Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82Dushani Perera, Maneesha Rajaratne, Shiromi Arunathilake,Kasun Karunanayaka, and Buddy Liyanage

The Ergonomic Evaluations of Three Front Baby Carriers:Mother’s Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88Chao Yin Wu, Hsiao Rong Huang, and Mao Jiun Wang

Artificial Intelligence and Computing

Designing Trust in Artificial Intelligence: A Comparative StudyAmong Specifications, Principles and Levels of Control . . . . . . . . . . . . . 97Fernando Galdon, Ashley Hall, and Laura Ferrarello

Solving the Revolving Door Problem: Machine Learningfor Readmission Risk Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103Alexander Mitts, Tiffany D’souza, Bryan Sadler, Dominick Battistini,and David Vuong

Can a Machine Be Intelligent? The New Concept of IntelligentMachine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110Vaclav Jirovsky and Vaclav Jirovsky Jn

Simplified Indoor Localization Data Acquisitionby Use of Recurrent LSTM Networks on SequentialGeomagnetic Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115Benny Platte, Rico Thomanek, Christian Roschke, Tony Rolletschke,Frank Zimmer, and Marc Ritter

Study on Software Log Anomaly Detection Systemwith Unsupervised Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 122Rin Hirakawa, Keitaro Tominaga, and Yoshihisa Nakatoh

Intent Inference of Driver Deceleration Behavior by UsingUnscented Kalman Filter Integrated with Conventional ArtificialNeural Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129Hironori Suzuki and Sho Wakabayashi

viii Contents

A Deep Learning Approach for Fishing Vessel Classificationfrom VMS Trajectories Using Recurrent Neural Networks . . . . . . . . . . 135Luepol Pipanmekaporn and Suwatchai Kamonsantiroj

An AcciMap for the Kleen Energy Power Plant Project Explosion . . . . 142Amangul A. Imanghaliyeva

Forecasting by Using the Optimal Time Series Method . . . . . . . . . . . . . 148Marwan Abdul Hameed Ashour, Iman Amer Hameed Al-Dahhan,and Areej K. Hassan

Calculation and Visualization of the Speed of Movementof the Working Point of the Exploratory Research Process . . . . . . . . . . 155Olga Popova, Boris Popov, Vladimir Karandey, and Vladimir Afanasyev

Artificial Intelligence as Answer to Cognitive Revolution Challenges . . . 161Nicolay Vasilyev, Vladimir Gromyko, and Stanislav Anosov

Continuous Control in Deep Reinforcement Learning with DirectPolicy Derivation from Q Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168Aydar Akhmetzyanov, Rauf Yagfarov, Salimzhan Gafurov,Mikhail Ostanin, and Alexandr Klimchik

Research on Cooperative Operation of Air CombatBased on Multi-agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175Jianqiang Zheng, Qinghua Ma, Shujun Yang, Shuaiwei Wang,Yiming Liang, and Jirong Ma

Traffic Sign Classification Using Embedding LearningApproach for Self-driving Cars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180Rauf Yagfarov, Vladislav Ostankovich, and Aydar Akhmetzyanov

Emergency Case Report Application Applying Location BasedService Framework on Mobile Smart Devices . . . . . . . . . . . . . . . . . . . . 185Shutchapol Chopvitayakun

Mapping of Mangrove Change with Remote Sensing in SamutSongkhram Province, Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191Walaiporn Phonphan and Manatsanan Thanakunwutthirot

Analysis of the Work System in an Object of the New Mediaand the Effects Generated in the Processesof Interaction with a User . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198Lorena Olmos Pineda and Jorge Gil Tejeda

Development of Web Application in English Subject . . . . . . . . . . . . . . . 204Busarin Eamthanakul, Orrawan Rewthong, and Sansanee Sansiribhan

Contents ix

Human–Computer Interaction

A Distributed Multimodal Multi-user Virtual Environmentfor Visualization and Query of Complex Data . . . . . . . . . . . . . . . . . . . . 213Jean-François Lapointe, Julio J. Valdés, Luc Belliveau,Norman G. Vinson, Bruno Emond, and Serge Léger

Individual Trace in Knowledge Space: A Novel Design Approachfor Human-Systems Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219Damian Chapman and Stephen Jia Wang

Make Me Messenger: Critiquing Children as Design Informants . . . . . . 225Dev Lamichhane and Janet C. Read

Reduce Stress Through Empathic Machine to Improve HCI . . . . . . . . . 232Karl Daher, Mathias Fuchs, Elena Mugellini, Denis Lalanne,and Omar Abou Khaled

App Use While Phubbing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238Yeslam Al-Saggaf

Optimization for Collaborative Learning Environmentsby Matching Team Members with Analyzing Students’Various Data Using ICT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245Keiko Tsujioka

Comparative Research on Terminology Databasesin Europe and China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252Jiali Du, Christina Alexantris, and Pingfang Yu

MOOC as an Innovative Tool for Design Teaching . . . . . . . . . . . . . . . . 258Rosa Retuerto Luna and Marco Neves

Machine’s Statistical Parsing and Human’s Cognitive Preferencefor Garden Path Sentences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264Jiali Du, Pingfang Yu, and Xinguang Li

Profiles of Professional Drivers Based on Drowsinessand Distraction Alerts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272Sónia Soares, Zafeiris Kokkinogenis, Sara Ferreira, and António Couto

Shaping Digital Literacy in Knowledge Society . . . . . . . . . . . . . . . . . . . 279Valentina Milenkova, Boris Manov, and Dobrinka Peicheva

Icon Design Recommendations for Central Consolesof Intelligent Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285Fang You, Yifan Yang, Mengting Fu, Jifang Wang, Xiaojun Luo,Liping Li, Preben Hansen, and Jianmin Wang

x Contents

Augmented, Virtual and Mixed Reality Simulation

Personage VR – A Virtual Reality Story-Telling Tool to RaiseAwareness About Ageism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295Francesco Carrino, Valentin Moullet, Omar Abou Khaled,Elena Mugellini, and Christian Maggiori

Evaluating Visual Perception by Tracking Eye Movementin Architectural Space During Virtual Reality Experiences . . . . . . . . . . 302Nayeon Kim and Hyunsoo Lee

Reflections on the Adoption of Virtual Adaptive Learning Toolfor Industrial Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309Alberto Martinetti, Micaela Demichela, Steven Spoolder, Joep von Berg,and Leo van Dongen

Validation of Driving Simulation in a Virtual Reality Setting:The Effects of Age, Sex and Simulation Technologyon Driving Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315Oliver Christ, Kaspar Kaufmann, Simon Wehrli, Emanuel Mistretta,Stefan Arisona, Thomas Wyssenbach, and Simon Schubiger

Using Virtual Reality and Gamification for a Restorative Therapyand Rehabilitation Support Equipment . . . . . . . . . . . . . . . . . . . . . . . . . 321Luís Soares, César Páris, Anabela Gomes, Jorge Laíns, Filipe Carvalho,and Luis Roseiro

Training in Immersive Virtual Reality: A Short Reviewof Presumptions and the Contextual Interference Effect . . . . . . . . . . . . . 328Cyrill Ziegler, Andreas Papageorgiou, Mathias Hirschi,Rosina Genovese, and Oliver Christ

3D Multi-user Virtual Environments in Education . . . . . . . . . . . . . . . . . 334Petr Svoboda

Early-Detection and Treatment of Torticollisin Infants Using Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340D. Michael Franklin, Kimberly Castle, and Rachael Walton-Mouw

Applications in Sport and Medicine

FEEDI - A Smart Wearable Foot-Band for Navigationand Guidance Using Haptic Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . 349Simon Stock, Alain Bertemes, Marco Stang, Martin Böhme,Daniel Grimm, and Wilhelm Stork

Contents xi

Human Factors in Interfaces for Rehabilitation-AssistiveExoskeletons: A Critical Review and Research Agenda . . . . . . . . . . . . . 356Davide Giusino, Federico Fraboni, Giuseppe Rainieri,Marco De Angelis, Annagrazia Tria, Laura Maria Alessandra La Bara,and Luca Pietrantoni

Spontaneous Physical Activity and Sedentary Patterns Analyzedin a General Population of Adults by the eMouve Application . . . . . . . . 363Sylvie Rousset, Deborah Coyault Abele, Maelane Benoit, Rihab Zemni,Philippe Lacomme, and Gérard Fleury

Quasi-experimental Study of Exertion, Recovery,and Worker Perceptions Related to Passive Upper-BodyExoskeleton Use During Overhead, Low Force Work . . . . . . . . . . . . . . 369Christine Daratany and Alvaro Taveira

Effect of Cognitive Load with Baby Crying on Postural Stabilityin Air Force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374Kristyna Rusnakova, Miloslav Stehlik, Jitka Soumarova,and Cestmir Oberman

Healthcare and Medical Applications

Supporting the Arm Ability Training of Stroke Patientsby a Social-Humanoid Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383Peter Forbrig and Thomas Platz

Innovation and Technology in One New Hospital in Montreal:A Lived Experience of Healthcare Professionals . . . . . . . . . . . . . . . . . . 389Zakia Hammouni

Service Innovation in Health Care: The Role of Health Platformsas Innovators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396Rudolf Fischer

Evaluation of Gerontechnologies: A Support to Decision Makingand Prescription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402Djamel Aissaoui and Javier Barcenilla

The Wide Area Virtual Environment - A Novel ImmersiveEnvironment for Medical Team Training . . . . . . . . . . . . . . . . . . . . . . . . 409Alan Liu, Eric Acosta, Jamie Cope, Valerie Henry, Fernando Reyes,Joseph Bradascio, and Wesley Meek

Early Detection of Foodborne Illnesses in Social Media . . . . . . . . . . . . . 415Jacky Casas, Elena Mugellini, and Omar Abou Khaled

Emotional Work and Organizational Culture in ColombianHealth Institutions. A Multidimensional Construction . . . . . . . . . . . . . . 421Olga Piñeros and Carlos Marín

xii Contents

Healthcare Devices for Children: Strategies to ImproveUser Experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427Laura Giraldi, Marta Maini, and Francesca Morelli

HypnOS: A Sleep Monitoring and Recommendation Systemto Improve Sleep Hygiene in Intelligent Homes . . . . . . . . . . . . . . . . . . . 433Eleni Tsolakou, Asterios Leonidis, Vasilios Kouroumalis, Maria Korozi,Margherita Antona, and Constantine Stephanidis

Mathematical Modelling and Computer Analysis of Diabetesto Develop Novel Index for Diagnosis and Risk Predictionof Pathogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440Kazumi Omata

Lean Healthcare Model Using Knowledge Managementand Change Management Approaches to Reduce Delaysfor Care in the Health Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445Alvaro Maravi-Cardenas, Miguel Fuentes-Chahuaylla,Juan Peñafiel-Carrera, Nestor Mamani-Macedo,Carlos Raymundo-Ibañez, and Francisco Dominguez

Development of User-Drawn Doodles for Communication andReporting of Dietary Intake in Health Management . . . . . . . . . . . . . . . 452Ying-Chieh Liu, Chien-Hung Chen, Su-Ju Lu, Yu-Sheng Lin,and Hsin-Yun Chen

Quantitative Methods for Assessing Functional Reservesin Predicting the Effectiveness of Medical Rehabilitation of Patientswith Diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459Irina Kurnikova, Sofia Buturlina, Svetlana Kislaya, Ramchandra Sargar,and Ekaterina Mukhametgaleeva

Effects of the Physical Therapy Application for Elderly . . . . . . . . . . . . . 462Kunyanuth Kularbphettong, Sililux Katesiri, and Nareenart Raksuntorn

Probiotic Lactic Acid Bacteria Isolation from FermentedBeef (Naem) Samples for Use as Starter Culture . . . . . . . . . . . . . . . . . . 468Jaruwan Chutrtong

The Human Interface Interaction Design Based on BloodOxygen Meter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474Yi Zhang

The Display of Conformal Symmetry in Lungs Formationof Human Fetuses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481Galina Spirina

Occupational Health and Safety Management Modelfor Mining Contracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486Yakelin Cano, Grimaldo Quispe, Heyul Chavez,Nestor Mamani-Macedo, Carlos Raymundo-Ibañez,and Francisco Dominguez

Contents xiii

Human-Technology and Future of Work

Reduction of Cognitive Load in Complex Assembly Systems . . . . . . . . . 495Dominic Bläsing, Sven Hinrichsen, and Manfred Bornewasser

Synthetic Consequential Reasoning: Facilitating the Designof Synthetic Morality in Highly Automated Systemsvia a Multidimensional-scalar Framework . . . . . . . . . . . . . . . . . . . . . . . 501Fernando Galdon and Ashley Hall

Green Capitalism: Democratizing Sustainable Innovationby Recycling Intellectual Capital Energy . . . . . . . . . . . . . . . . . . . . . . . . 507Evangelos Markopoulos, Emma Luisa Gann, Ines Selma Kirane,and Hannu Vanharanta

Information Management Strategies in Manual Assembly . . . . . . . . . . . 520Sven Hinrichsen, Benjamin Adrian, and Manfred Bornewasser

Expression of Feelings in Twitter: A Decision Tree Approach . . . . . . . . 526Yeslam Al-Saggaf

How Can We Rescue the User from the DigitalTransformation Tornado? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 532Stefano Rizzo

The Right to Reparations: A New Digital Right for RepairingTrust in the Emerging Era of Highly Autonomous Systems . . . . . . . . . . 538Fernando Galdon and Ashley Hall

Work-Compatibility Based Accident Prediction Modelfor the Workforce of an Underground Coal Mine in India . . . . . . . . . . 544Arra Kumar, Gunda Yuga Raju, and Suprakash Gupta

Muscle Fatigue Monitoring: Using HD-sEMG Techniques . . . . . . . . . . . 551Xiangyu Liu and Meiyu Zhou

An Investigation of Chinese Driving Behaviorfrom Driver’s Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557Long Liu, Jue Li, and Daniel Sällberg

Properties of Emulsion Sausage with Partial Replacementof Fat by Dragon Fruit Peel Powder . . . . . . . . . . . . . . . . . . . . . . . . . . . 563Nuntaporn Aukkanit, Siriyakorn Sroyraya, and Tamonwan Duljumnong

Rapid Imaging of Latent Fingerprints Using Xanthone Compoundson Silica Nanoparticles Detected by UV Spectrophotometry . . . . . . . . . 569Chanyapat Sangsuwon

xiv Contents

The Assessment of Environment Impact Quotient FieldUse Rating from the Rate of Pesticides in Padd in Bang RachanDistrict, Sing Buri Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577Talisa Niemmanee, Kunya Borwornchokchai, and Pantip Kayee

Smart Textile for Architecture: Living with Technology . . . . . . . . . . . . 583Ana Oliveira

Management, Training and Business Applications

Identifying High Performance Indicators (HPI) for Close CombatForces in a Military Training Environment . . . . . . . . . . . . . . . . . . . . . . 591Rory O’Brien, Kenneth Pitts, and Jay Brimstin

Digitalization of the Last Mile of a Humanitarian Supply Chain . . . . . . 596Maurizio Caon, Omar Abou Khaled, Paul Vaucher, Dany Mezher,and George Mc Guire

Comprehensive Strategic Risk Management System to ReduceEvaluation Times in Small-Scale Mining Projects . . . . . . . . . . . . . . . . . 603Fernando Loarte-Flores, Yaneth Vasquez-Olivera,Nestor Mamani-Macedo, Carlos Raymundo-Ibañez,and Francisco Dominguez

Intra-work Conditions. Objective of the Organizational Managementfor the Healthy Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 610Carlos Marín and Olga Piñeros

Safety Management Model with a Behavior-Based Safety CoachingApproach to Reduce Substandard Behaviors in the Mining Sector . . . . 616Brahayan Gómez, Roberto Sánchez, Yaneth Vásquez,Nestor Mamani-Macedo, Carlos Raymundo-Ibañez,and Francisco Dominguez

Public Management Model with a Sustainable DevelopmentApproach Based on Lean Six Sigma: Formalization of Small-Scaleand Artisanal Mining in Peru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625Yuler Montalvo, Vidal Aramburú, Nestor Mamani-Macedo,Carlos Raymundo-Ibañez, and Francisco Dominguez

Design and Implementation of Online Law Consultation Systemin Higher Vocational Colleges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 632Lili Li

Service Model Under the Lean and Change ManagementApproaches to Reduce Delivery Times and Optimize the Qualityof Processes in a Company in the Metal-Mechanic Sector . . . . . . . . . . . 637Tom Orihuela-Meza, Juan Peñafiel-Carrera, Nestor Mamani-Macedo,Carlos Raymundo-Ibañez, and Francisco Dominguez

Contents xv

Production Management Model for Reducing Product DevelopmentWaiting Time by Applying Lean Manufacturing Model for SMEExporters in the Textile Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644Katerine Becerra-Guevara, Xiomara Carbajal-Alayo,Nestor Mamani-Macedo, Gianpierre Zapata, Carlos Raymundo-Ibañez,and Francisco Dominguez

Construction of Law Network Courses in Higher Vocational Colleges . . . 651Lili Li

Lean Six Sigma Operational Assessment Methodwith a Modified DMA-IC Cycle for Reducing Non-productiveTimes at Mining SMEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656Fabricio Aguero, Gianfranco Ramírez, Vidal Aramburu,Nestor Mamani-Macedo, Carlos Raymundo-Ibañez,and Francisco Dominguez

An Analytical Study of Aptitude Tests for Entrance to ArchitectureEducation: A Case of India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663Poonam Khan and Nikhil Ranjan Mandal

An Application of Social Network Analysis to Study Interconnectionof Courses in Mathematics Education Curriculum . . . . . . . . . . . . . . . . . 669Kanyarat Bussaban

Management Projects Model to Reduce Lead Time of Base StationTelecom Construction in SME Based on Lean Focus and Agility . . . . . 676Christian Iberico-Tafur, Ricardo Sun-Itozu, Maribel Perez-Paredes,Nestor Mamani-Macedo, Carlos Raymundo-Ibañez,and Francisco Dominguez

Drilling-and-Blasting Mesh Design for Underground MiningUsing the Holmberg Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683Max Poma, Grimaldo Quispe, Nestor Mamani-Macedo, Gianpierre Zapata,Carlos Raymundo-Ibañez, and Francisco Dominguez

Tennis Organization Service for Middle-Aged and ElderlyPeople in Wuhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 690Chongyang Zhang and Qi Luo

Analysis of the Work System in an Object of the New Mediaand the Effects Generated in the Processes of Interaction witha Weak - Visual Person . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696Jorge Gil Tejeda and Lorena Olmos Pineda

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703

xvi Contents

Human-Centered Design

Designing Presence

Kevin Clark1(&) and Kazuhiko Yamazaki2

1 ChoiceFlows Inc., Content Evolution LLC, Chapel Hill, NC, USAkevin.clark@choiceflows.com

2 Mushashino University, X-Design Academy, Nishitokyo, Tokyo, Japan

Abstract. Experience design and engagement is evolving to become the designof presence with the supporting practices and tools of SenseMapping, CC-Align,Experience Vision and Scenario-Based Design Methods.

Keywords: CC-Align � Context � Design � Engagement � Experience �Experience design � Fully-human-compatible � Interaction � Mutual benefit �Presence � Presence design � Relevance � SenseMapping

1 Introduction

Experience design and engagement is evolving to be the design of presence. Presence issensing the state of being in a specific space. Designing Presence is about connectingphysical and mental states – creating sense, sensibility and coherence. Presence isrelevance. Presence is bringing identity into context. Design of presence includeslistening and leading by including the design of the organization and bringing aboutmutual benefit for the stakeholders it chooses to serve [1].

Designing Presence transcends and includes experience design and stakeholderengagement. Designing Presence as a practice is embodied in SenseMapping [2](Fig. 1).

SenseMapping provides a perspective to design products, systems and services thatare fully human compatible; that strongly and openly – and subtly and suggestively –

project presence. SenseMapping allows for the design of presence by considering andconnecting all five human senses (seeing, hearing, feeling, tasting, smelling) to humansensemaking (head, heart and gut - along with interests and values).

Designing Presence is supported by CC-Align research that discovers the con-nective tissue between the values and interests of customers and companies – and by

Fig. 1. Visual layout example of SenseMapping; one format.

© The Editor(s) (if applicable) and The Author(s), under exclusive licenseto Springer Nature Switzerland AG 2020T. Ahram et al. (Eds.): IHIET 2020, AISC 1152, pp. 3–9, 2020.https://doi.org/10.1007/978-3-030-44267-5_1

UserStrategic Experience (USE) and the Experience Vision design methodology thatlink design processes and customer experiences to business objectives.

2 User Strategic Experience (USE)

Design of Presence is positively correlated with the USE Model: User StrategicExperience [3], where the wants and needs of customers and stakeholders interlace withthe business model and objectives of the business or organization. It is also positivelycorrelated with Experience Vision providing a strong framework for experience designthat supports business objectives, including the Scenario Based Design Method(SBDM) [4] (Fig. 2).

The ability to describe internal and external alignment is critical for designingpresence. The CC-Align [5].

Method developed by Choiceflows which is a contraction of the term “company-customer alignment”, is a way to benchmark current state of internal and externalinterests and values calibration. CC-Align then finds undiscovered opportunities fordesign and strategic interaction that is beneficial to the organization and those it serves.When repeated, it is a measurement for understanding and documenting improvement.

Evolutionary use cases include “sense-layering” for the IBM Merlin Center for nextgeneration banking experiences [6] reimagining the global portfolio of 400 IBM ClientBriefing Centers [7], and the emergence of multi-tract journey mapping first deliveredat Toyota [8] and today in use by many Content Evolution and SenseMapping practiceclients around the world.

Fig. 2. USE: User Strategic Experience model for co-creating value.

4 K. Clark and K. Yamazaki

3 The Merlin Center

The Merlin Center as described in a keynote article in CMO Magazine is a placedesigned to showcase retail banking in the future. Located in Stamford, Connecticut,the Merlin Center concept was created by the John Ryan Group to showcase archi-tectural design possibilities for customer interaction, and IBM provided ideas andtechnologies to the table (Fig. 3).

Located in a nondescript warehouse location, the exterior of the Merlin Centergives no clue to what’s inside. IBM corporate strategic design is brought in to make thecenter an over-the-top memorable experience. The inside is already visually competent.

In fact, there is a piece of magic early in a visit that is worthy of the DisneyImagineers. When you start your briefing, you sit in a well-appointed executive con-ference room. You sit and listen to an overview of the banking industry, strategic trends– all being projected on a screen in the front of the room, animated with the obligatoryPowerPoint charts.

Then, about 10 min into the briefing, the presenter says – “yet it would be better tosee these trends come to life, wouldn’t it?” …and with that a button is pushed on thelectern and what appeared to be a solid wall disappears into the ceiling – revealing astreet scene complete with asphalt and streetlights – and across that street the bank ofthe future mocked up inside the warehouse. For people being briefed it has the desired“wow” effect and sets the stage for opening minds and hearts to what is possible.

So far, so good. The space looks great. There is a lot of tech you can seedemonstrated, yet you can’t touch it yourself. The space doesn’t sound like anything,smell or taste like anything to support the core messages being delivered. IBM design isabout to change that to amp-up that WOW.

We called what we did “SenseLayering” for the space. Adding layers of intentionalsensory stimulus to the Merlin Center to make it more experiential and to increase thesense of flow where time disappears in purposeful engagement [9]. This is the genesisof the SenseMapping practice that would emerge almost a decade later and after thepublication of my book Brandscendence: Three Essential Elements of Enduring Brandswhere all five human senses are discussed as core contributors to a fully engagingbrand.

Rewind. Let’s walk into the Merlin Center from start after performing strategicSenseLayering augmented design. Now when you enter, in addition to seeing acompetent and inviting space as a start transition from the warehouse exterior, you also

Fig. 3. Visual excerpts from CMO Magazine article “Experience Preferred,” 2005.

Designing Presence 5

smell coffee and fresh cookies baking. An air oven near the entrance provides thewelcoming aroma of baking – and the coffee area is moved from a back space to thefront of the house.

We studied spas and found ways to redesign the washrooms to be more thanfunctional. Flowers, lounge furniture, cloth wash towels, spring water dispensers – andan injected background scent of forest highlands during spring foliage development –earthy fresh with a dash of vanilla.

The bank of the future became more sensory competent. It was infused with freshflowers and interior plantings at the entrance – and injected with a light yet bright citrusto awaken the senses. You could now touch a lot more of the exhibits in the center anduse them yourself hands-on without a demonstrator.

The bank of the future also starts to sound like something. It is given life with streetsounds when the wall disappears – and quiet sounds of clients in conversations withbankers and background sounds of machines in use, such as printers and copiers –

keyboards clicking as phantom people type. The main greeting area now has light jazzplaying softly in the background and the washrooms light classical.

It was as we say today about the SenseMapping process, a more “fully human”space in every sense. The new design of the Merlin Center invited people to beawakened, in a state of flow, and fully engaged and present. After the Merlin Centerexperience “Discovering your WOW!” is the name of a book of design inspirations Iwrote with Ron Smith for internal use at IBM to guide the next generation of expe-riences for clients visiting the company’s portfolio of 400 Executive Briefing Centersaround the world.

4 Toyota and Multi-track Journey Mapping

After working on the Merlin Center and global IBM Briefing Centers – and doingstrategic work on the experience of strategic outsourcing clients – I graduate from IBM30 years of service. I joined IBM early in life, so too early to simply accept retirementas a permanent state. In the next decade I start several companies, including ContentEvolution and Choiceflows (Fig. 4).

Fig. 4. Toyota multi-track journey map; connecting Toyota with its customers.

6 K. Clark and K. Yamazaki

In the early days of Content Evolution, we provide our first multi-track journeymap, pictured above. It is a first by showing connections both to the journey beingtaken by customers – and the journey being taken by key constituencies inside Toyota– and the connective tissue between inside and outside the business.

This is also a building block for SenseMaping that would manifest almost a decadelater. SenseMapping is the process of and the toolkit for Designing Presence.

5 CC-Align

Customer-Company Alignment, or CC-Align, is a practice of Choiceflows, a ContentEvolution member company. CC-Align provides a research methodology that dis-covers the values and interests – and intersections – of customers and the companiesthat serve them. CC-Align has roots going back to the 1980s with experiments per-formed by Dr. Jordan Louviere, COO of Choiceflows and one of the most citedresearchers in the world with a Google Scholar h-index of 102 (placing him in the top1% of all published researchers in the world) [10].

As graphically suggested in the Toyota model above, when company and customeractivities are aligned for relevance, context and mutual benefit as described inBrandscendence [11] in detail – there is more intention in interaction and positiveoutcomes. CC-Align finds the connections, and the disconnects. In fact, the results of aCC-Align experiment deliver a grey-scale of fully aligned interactions to completelymis-aligned relationships.

CC-Align is a specific version of a choice experiment to investigate and discernthese alignments and draws heavily on the Best-Worst Scaling methodology (BWS,also known as “CC-Diff” in the market research profession), invented by Dr. Louviere.

In one case CC-Align is used to determine alignment between sales representativesof a company and the customers they work with regularly. The CC-Align studydetermines the motivations and incentives are not creating a shared values and interestenvironment. By using the results of the CC-Align study and making changes in salesincentives and offers to customers, the business moves from number four to numbertwo in the market – and is then acquired by the number one company in the category,greatly rewarding shareholders. This is a multi-dimensional research that supportspresence design.

6 Experience Vision

The “Experience Vision: Vision Centered Design Method” is a comprehensive methodwhich makes it possible to propose new and innovative products, systems and servicesthat are currently unavailable, as well as proposing advances for those that currentlyexist [12]. It encompasses the entire HCD (Human Centered Design) process andpresents a new vision with experiential value for both user and business from an HCDviewpoint (Fig. 5).

Designing Presence 7

The Experience Vision process includes eight formats developed as a practicaldesign tools. They include Goal setting of the project, Intrinsic user value, Policy ofbusiness value, Persona, Value scenario, Activity scenario, Interaction scenario andExperience vision (summary). Case studies showed effectiveness and usefulness of theformats as a design tool for this method.

7 Design of Presence

Designing Presence using the SenseMapping methodology reveals the previouslyinvisible fusion of physical world design including products, systems and services –

user interaction and experience – and intentional connections with personal and sharedvalues. SenseMapping coupled with Experience Vision and CC-Align create a Pres-ence Design process for discovering, planning and making fully-human-compatiblemarketplace offerings.

References

1. Clark, K.: Brandscendence: Three Essential Elements of Enduring Brands – Relevance,Context and Mutual Benefit. Kaplan Press, Dearborn (2004)

2. Clark, K.: SenseMapping. Later enhanced by other Content Evolution colleagues, ContentEvolution practice and white paper (2019)

3. Clark, K.: USE: User Strategic Experience. Content Evolution white paper (2016)4. Yamazaki, K., Ueda, Y., Go, K., Hayakawa, S., Yanagida, K.: Experience Vision and

“Scenario Based Design Method”. Maruzen Press (2012)5. Louviere, J., Carson, R., Clark, K.: CC-Align. ChoiceFlows white paper, member of the

Content Evolution federation (2019)

Fig. 5. Experience Vision framework adapted from the book Experience Vision.

8 K. Clark and K. Yamazaki

6. Farah, S.: Experience preferred. CMO Magazine Cover Story, November 20057. Clark, K., Smith, R.: Unleashing the power of design thinking. Design Manage. Rev. 19,

8–15 (2008)8. Clark, K., Content Evolution: Multi-track Journey Mapping first introduced at Toyota (2009)9. Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Harper Perennial,

New York (1990)10. 50,000 Citations for Dr. Jordan Louviere of ChoiceFlows Inc. Business Wire, 2 January

202011. Clark, K.: Brandscendence, ibid12. Experience Vision; see reference, ibid

Designing Presence 9

Hume’s Guillotine in Designing EthicallyIntelligent Technologies

Pertti Saariluoma(&)

Cognitive Science, University of Jyväskylä,Box 35, 40014 Jyväskylä, Finland

ps@jyu.fi

Abstract. Intelligent machines can follow ethical rules in their behaviour.However, it is less clear whether intelligent systems can also create new ethicalprinciples. The former position can be called weak ethical AI and the latterstrong ethical AI. Hume’s guillotine which claims that one cannot derive valuesfrom facts appears to be a fundamental obstacle to strong ethical AI. Theanalysis of human ethical information processes provides clarity to the possi-bility of strong ethical AI. Human ethical information processing begins withpositive of negative emotions associated to situations. Situations can be seen asconsequences of actions and for this reason people can define rules aboutacceptability of typical actions. Finally, socio-ethical discourse create generalethical rules. Intelligent systems can provide important support in ethical processand thus the difference between weak and strong ethical AI is polar.

Keywords: Interaction design � Intelligent systems � Ethical design � Hume’sguillotine

1 Introduction

Once again, humankind is on the cusp of a new technology revolution. It hasencountered such situations many times in its history. Technologies, work processes,and societies have changed numerous times. Stone tools, fire, sailing, navigation,cannons, printing, clocks, steam engines, electricity, and nuclear energy provide goodexamples of technological revolutions leading to new forms of work and socialorganisation [1]. The ongoing revolution is based on intelligent technologies. They arecharacterised by their capacity to carry out tasks, which have previously required theintelligent information processing of the human mind.

The improved speed of computing and the fast growth of data have made it possibleto design technical artefacts with the capacity to do tasks, which thus far only peoplehave been able to carry out. Modern examples of emerging intelligent technologies arenot few. Artificial intelligence has penetrated numerous aspects of modern life.Industrial robots, office automation, intelligent medicine, changes in teaching, auton-omous traffic systems, and intelligent finance give us a fast vision of the future [2, 3].

In addition to fast routine processing of logical inferences, machines can makedecisions between alternative courses of action. They can even learn to make classi-fications of their own so that people are not able to predict the information states which

© The Editor(s) (if applicable) and The Author(s), under exclusive licenseto Springer Nature Switzerland AG 2020T. Ahram et al. (Eds.): IHIET 2020, AISC 1152, pp. 10–15, 2020.https://doi.org/10.1007/978-3-030-44267-5_2

intelligent systems can generate. Consequently, intelligent systems can select betweendifferent sense-making courses of actions.

The capacity for selective information processing makes it possible for modern AI-based systems to compare the values of different information states on sense-makinggrounds. A chess-playing computer for example, can find the best sequences of movesamong millions of legal alternatives. Intelligent choices make a machine’s actionsintelligent.

A very specific view is opened by ethics with respect to thinking intelligent choices.Some information states are more ethical than others, and thus it makes sense to discussethics in the context of acting intelligent machines. They can select some courses ofaction as they are more ethical with respect to certain ethical principles. Thus, intel-ligent technologies can make operational decisions on ethical grounds. They canchoose between different courses of actions on the grounds of implemented ethicalprinciples. For example, intelligent systems can prefer children to middle-aged peoplein making decisions about the order of medical operations. Such decisions are ethicaland carried out by intelligent machines.

For the reasons given, one can speak of ethics typical to using intelligent tech-nologies in two senses. One can speak of the ethical use of technical artefacts insociety, but one can also develop systems with ethical capacities of some type. In thispaper, focus is in the latter.

2 Hume’s Guillotine

A crucial question in considering future intelligent sociotechnical society is howmachines can be ethical at all. They are just systems with different electric states whichpeople map as information about reality. The electric states are mapped to factualinformation. In digital systems power is either higher or lower, and this makes itpossible to have two states. These states can stand for truth or false. Thus, informationin intelligent machines is apparently factual. Intelligent machines process facts.

Facts are different from values. While facts are binary and can be true or false,values are not dichotomous. Something can be obliged, forbidden, or allowed. Theproblem of relations to binary facts in binary machines and multiple state values isimportant in designing ethical information systems and is conceptually important indesigning ethically intelligent technologies.

One important problem in relations to facts and values was seen over 250 yearsago. Hume [4] wrote: “It is impossible that the distinction between moral good and evilcan be made by reason”. This aporia is called Hume’s guillotine or “is-ought to”problem, which is central to modern ethics. Hume’s guillotine claims that one cannotderive from how things are how they should be. When designing ethically intelligentmachines, Hume’s guillotine is a relevant conceptual problem. One can justly askwhether machines processing facts can have anything to do with ethics at all, and ifthey do, how is it possible?

Intelligent machines can be ethical in more than one sense. The first position is thatpeople implement their values in the evaluative structures of ethical programmes astraditional chess machines have their human implemented heuristics. This latter

Hume’s Guillotine in Designing Ethically Intelligent Technologies 11

position can respectively be termed weak ethical AI (WEAI). The position thatmachines are able to generate new ethical rules and principles themselves can be calledstrong ethical AI or intelligence (SEAI). In the context of the former position, Hume’sguillotine is apparently easier to solve than in the latter. However, firstly, it is importantto ask how ethical information processing is possible for people and how weak andstrong ethical AI differ from each other.

3 Ethical Process

Hume’s guillotine is still an important ethical dilemma today, and one cannot say that ithas been solved. To gain clarity on this issue, one must think how it is possible forpeople to process ethical information. The idea that human information processing canbe used to develop intelligent technologies has been called cognitive mimetic [5]. Here,the analysis of human ethical information processing can be used as a model forrespective machine information processing.

Ethics are possible as they are real. There are no grounds to doubt that people arecapable of creating ethical rules and norms. The process of creating ethical rules andnorms can be called an ethical process or ethical information process, which is anexample of human creative thinking. Ethical machines are machines which can par-ticipate in an ethical process.

Human experience, i.e. conscious mental representation, forms a central componentof human information processing and thinking. The information contents of experi-ences and representations can be called mental contents. Mental representations havetheir cognitive and emotional dimensions. Both have an important role in ethicalinformation processing, but Hume’s guillotine cuts them apart.

Ethically, an important type of mental content is emotional valence [6]. Mostemotions can be divided into positive or negative, pleasant or unpleasant, and happy orsad. Therefore, all situations emerging in the course of actions can be experiencedpositively or negatively.

Emotionally grounded ethical thinking is normally labelled as emotivism [7]. Thesetheories begin with the idea that situations of life and respective experiences areemotionally positive or negative (pleasant and unpleasant). The emotional analysis ofconsequences of actions thus provides the basis for the ethical analysis of actions andaction types. For example, the so-called golden rule (one should not treat others inways that one would not like to be treated oneself) can be seen as a generalisation ofsituational experiences of deeds in which the principle is followed or violated. Thus,the emotional and ethical information process is in the analysis and experience of theemotional valence and can be taken as the first point of the ethical process.

Consequently, the development of ethical norms is grounded in the analysis ofemotional situations. However, it is not wise to end the analysis of the ethical processwith emotions. The situations of life are consequences of actions. Thus, the value ofactions can be defined on the grounds of the valence of the situations arising as aconsequence of particular types of actions. Norms describe what kinds of actions havehad emotionally positive or emotionally negative consequences. Actions leading topain are not acceptable and actions leading to positive emotions are good.

12 P. Saariluoma

The first step in defining ethical principles is to classify situations emotionally andactions leading to situations of two types respectively good or bad. Thus, one cangenerate ethical norm “avoid excessive use of alcohol as it leads to social and healthproblems”. Alcoholism is a situation in life and drinking is the action which ends to thissituation.

However, different people experience situations in different ways. Social interactioncan be painful for some while it is positive for another. Therefore, the general ethicalnorms can be seen to be consequence of informal (everyday) and formal (or political)discourses. This socio-ethical process has been investigated in discourse ethics % [8].Thus, it is essential to add to the ethical process the discourse between people in societyrelated to political analysis and even laws.

Hume missed that the ethical process and each norm in the generation process hasthree components. Firstly, there is an emotional analysis of situations in life. People dothis kind of analysis every moment of their life. Secondly, the ethical process includesfactual analysis of actions leading to the given types of situations. Finally, one needs toadd a socio-ethical discourse, which defines the social and historical properties of asituation. Though Hume understood clearly the triad of emotions, reason, and action,his guillotine unreasonably broke the process.

Hume’s guillotine is a consequence of a mistaken analysis of the ethical processand ethicality of actions. Hume does not pay attention to the fact that ethics arise fromthe simultaneous analysis of situations. Cognitive and emotional aspects of situationsare encoded in a parallel manner. This is why, the very question whether (cognitive)facts be used to define (emotional) values is senseless. Facts and values are two sides ofone and the same mental event. Social discourse works to get a generalised idea aboutthe relations of actions, cognitions, and emotions. Accurate analysis of the ethicalprocess makes it possible to study the problems of weak and strong AI from a newperspective.

4 Weak and Strong Ethical AI

The analysis of the ethical process aids us in considering the relations of weak andstrong ethical AI. Following the founding ideas of life-based design giving clarity tothe way ethics and ethical norms are created in human life enables researchers to studythe generation’s ethical design requirements and the ethical information processing fortechnologies. Searching for answers to two questions is central. Firstly what kinds oftechnologies should be developed, and secondly, how can these technologies be takenpart of everyday life [9].

Weak AI is not a difficult case. Ethical norms can be implemented in AI pro-grammes. It is possible to define the situation and their factual properties. This infor-mation can be recognised by intelligent systems in data, and associate ethical normscan be followed in actions. Thus, designers can build recognition association typeaction models with ethical contents. For example, if some situation is known to causepain, technology should act to avoid such situations.

However, strong AI is more challenging, and there are no clear-cut solutions to theproblems of designing strong ethical AI. Actually, the border between weak and strong

Hume’s Guillotine in Designing Ethically Intelligent Technologies 13

AI is not absolute, but systems can differ in their strength. The criterion for the strengthof an ethical AI (EAI) system is the capacity to create new ethical norms withouthuman involvement. Firstly, it is possible by means of data analysis to study possiblepain or negative valence causing situations. For example, data mining can find factorscausing illnesses, which have been unknown so far. Such research has existed for along time. For example, Durkheim [10] found a link between religions, social dis-course, and suicides, and a connection between smoking and lung cancer was found inthe sixties. There is no logical obstacle to finding such associations by means ofintelligent systems. Thus, human-supported AI and data mining can be used to findnovel factual grounds for new ways of behaving. This kind of EAI is machine-supported AI.

Another possibility is to ask machines to recognise features, which are known tocause emotionally negative experiences. It is also possible to register human responsesto different types of situations to classify them as emotionally negative. AI programmescan actively search for new combinations so that the human component is one-stepfurther from the previous case. The information found can be associated with theactions ending in negative situations, and thus new information can be used to createnew ethical rules.

Finally, the core issue is whether intelligent systems can create new previouslyunknown ethical norms without human involvement to process on the grounds of theirfactual data. Machines can analyse by different means emotional valences typical tosome situations. They can also associate the results of emotional analysis to the actions.They can even analyse general social attitudes in these situations. The autonomy ofethical systems can thus be gradually increased. But human involvement can be rel-atively direct in creating new ethical rules.

5 Final Discussion

Since information systems are involved in carrying out increasingly complicatedactions. It is essential to develop ethical capacities for these systems. Their operationalroles can be very independent, and thus it is essential that they can follow sense-makingethical practices.

Apparently, Hume’s guillotine can make it hard to develop ethical autonomy forfuture systems. Intelligent systems are in the first place factual information processingdevices, and it is not easy to see how one could derive values from facts. Despiteconceptual difficulties, it is important to think how intelligent systems can followethical norms in their actions.

Our analysis suggests that there seems to be two poles in ethical informationprocessing, which can be called weak and strong ethical AI. The first kind of systemcan apply given ethical rules in given situations. They can recognise critical featuresin situations and choose their actions on the ground. In such cases, ethics are just ahuman implanted feature in a recognition action system. This kind of ethical processorcan be called weak AI.

Nevertheless, despite Hume’s guillotine, people are able to create ethical thoughtsand information processes. Thus, it must be possible to create machine-supported

14 P. Saariluoma