Value-oriented and ethical technology engineering in Industry 5.0: a human-centric perspective for the design of the Factory of the Future

Manufacturing and industry practices are undergoing an unprecedented revolution as a consequence of the convergence of emerging technologies such as artificial intelligence, robotics, cloud computing, virtual and augmented reality, among others. This fourth industrial revolution is similarly changing the practices and capabilities of operators in their industrial environments. This paper introduces and explores the notion of the Operator 4.0 as well as how this novel way of conceptualizing the human operator necessarily implicates human values in the technologies that constitute it. The design approach known as value sensitive design (VSD) is used to explore how these Operator 4.0 technologies can be designed for human values. Expert elicitation surveys were used to determine the values of industry stakeholders and examples of how the VSD methodology can be adopted by engineers in order to design for these values is illustrated. The results provide preliminary adoption strategies that industrial teams can take to Operator 4.0 technology for human values

SHARED PATH Service Design and Artificial Intelligence in Designing Human-Centred Digital Services

Digitalization and the growing service economy place challenges on organizations for transforming their service offerings to match the high user expectations. Services increasingly exploit digital technologies which play an important role in the creation of service experiences. One of the examples is artificial intelligence (AI), which may actively perform in customer service, but also provide solutions in the back end of services. While AI actively takes part in the creation of service value, the line between human and machine in the service encounters blurs. This creates new type of service components which need to be designed as part of digital service journeys. This dissertation is constructed around seven scientific publications that explore the merging of AI and service design in creating human-centred digital service solutions. The focus in the publications is on applying service design principles to AI-enabled services, from which an AI assistant is an example. AI assistants interact with users through text and voice interfaces and can be perceived as a gateway to complex digital service ecosystems. AI assistants are rather new as services, and they touch upon areas that, besides the design challenges, are ethically, philosophically and legally demanding. Here, service designers face changes both in the design process and in their role as designers. This study was conducted as a qualitative research with roots in the practice of design research. The main research data consist of five case studies and seven expert interviews analysed through coding, content analysis and visual mapping to answer the following research question: How is AI affecting the practice of service design and the design of digital services? The findings from the publications are concluded under the following four topics: (1) AI changes the design of digital service interactions, (2) AI assistants perform as actors in digital services, (3) AI needs to be human-centred rather than human-like and (4) AI assists and augments the practice of service design. Under these topics, the discussion highlights the ethical considerations and humanization aspect of AI as a part of designing and the design outcomes as AI-enabled services.Digitalisaatio ja kasvava palvelukeskeinen markkinatalous asettavat organisaatioille muutoshaasteitta, jotta palvelutarjonnalla pystyttäisiin vastaamaan käyttäjien korkeisiin odotuksiin. Palvelut hyödyntävät yhä enenevissä määrin digitaalista teknologiaa osana palvelukokemusten tuottamista. Yhtenä esimerkkinä teknologioista on tekoäly, jolla voi jo olla aktiivinen osa asiakaspalvelussa sekä ratkaisujen tuottajana palveluiden taustajärjestelmissä. Kun tekoälyn rooli palveluarvon tuottamisessa kasvaa, raja ihmisen ja koneen välillä voi hämärtyä. Tekoäly luo näin uudenlaisia palveluelementtejä, jotka tulee muotoilla osaksi digitaalisia palvelupolkuja. Väitöstyö pohjautuu seitsemään tieteelliseen julkaisuun, joiden kautta tutkimus tarkastelee tekoälyn ja palvelumuotoilun yhteyttä ihmislähtöisten digipalveluiden muotoilemisessa. Julkaisut keskittyvät palvelumuotoilun näkökulmaan tekoälyavusteisten palveluiden kehittämisessä ja käyttävät esimerkkikontekstina tekoälyassistentteja. Tekoälyassistentti on digitaalisen palvelun muoto, joka on vuorovaikutuksessa asiakkaan kanssa joko tekstin tai puheen kautta. Tekoälyassistentti voi myös toimia keulakuvana laajemmalle palvelutarjonnalle ja palveluekosysteemeille. Tekoälyassistentit ovat palvelumuotona melko uusia ja niiden aihepiirit ovat muotoiluhaasteen lisäksi eettisesti, filosofisesti ja juridisesti haastavia. Tämä luo palvelumuotoilijalle haastavan asetelman niin muotoiluprosessiin kuin omaan työhön muotoilijana. Väitöstutkimus on toteutettu laadullisena tutkimuksena muotoilun tutkimuksen kentällä. Tutkimuksen ensisijainen aineisto koostuu viidestä tapaustutkimuksesta ja seitsemästä asiantuntijahaastattelusta. Aineistoa on analysoitu koodaamisen, sisällönanalyysin sekä visuaalisen analyysin keinoin. Analyysin kautta vastataan tutkimuskysymykseen: Mikä on tekoäly vaikutus palvelumuotoilutoimintaan ja digitaalisten palveluiden muotoilemiseen? Tutkimustulokset esitellään neljän aihepiirin kautta: (1) Tekoäly muuttaa digitaalisten palveluiden vuorovaikutusten muotoilua, (2) tekoälyassistentit ovat aktiivisia toimijoita digitaalisissa palveluissa, (3) tekoälyn on oltava ihmiskeskeistä, ei ihmismäistä, ja (4) tekoäly tukee ja laajentaa palvelumuotoilutoimintaa. Näiden aihepiirien kautta tutkimustulokset nostavat esiin tekoälyn eettiset ja inhimilliset näkökulmat osana tekoälyavusteisia palveluita sekä niitä tuottavaa palvelumuotoilutoimintaa

Smooth and Resilient Human–Machine Teamwork as an Industry 5.0 Design Challenge

Smart machine companions such as artificial intelligence (AI) assistants and collaborative robots are rapidly populating the factory floor. Future factory floor workers will work in teams that include both human co-workers and smart machine actors. The visions of Industry 5.0 describe sustainable, resilient, and human-centered future factories that will require smart and resilient capabilities both from next-generation manufacturing systems and human operators. What kinds of approaches can help design these kinds of resilient human–machine teams and collaborations within them? In this paper, we analyze this design challenge, and we propose basing the design on the joint cognitive systems approach. The established joint cognitive systems approach can be complemented with approaches that support human centricity in the early phases of design, as well as in the development of continuously co-evolving human–machine teams. We propose approaches to observing and analyzing the collaboration in human–machine teams, developing the concept of operations with relevant stakeholders, and including ethical aspects in the design and development. We base our work on the joint cognitive systems approach and propose complementary approaches and methods, namely: actor–network theory, the concept of operations and ethically aware design. We identify their possibilities and challenges in designing and developing smooth human–machine teams for Industry 5.0 manufacturing systems

A socio-technical approach for assistants in human-robot collaboration in industry 4.0

The introduction of technologies disruptive of Industry 4.0 in the workplace integrated through human cyber-physical systems causes operators to face new challenges. These are reflected in the increased demands presented in the operator's capabilities physical, sensory, and cognitive demands. In this research, cognitive demands are the most interesting. In this perspective, assistants are presented as a possible solution, not as a tool but as a set of functions that amplify human capabilities, such as exoskeletons, collaborative robots for physical capabilities, virtual and augmented reality for sensory capabilities. Perhaps chatbots and softbots for cognitive capabilities, then the need arises to ask ourselves: How can operator assistance systems 4.0 be developed in the context of industrial manufacturing? In which capacities does the operator need more assistance? From the current paradigm of systematization, different approaches are used within the context of the workspace in industry 4.0. Thus, the functional resonance analysis method (FRAM) is used to model the workspace from the sociotechnical system approach, where the relationships between the components are the most important among the functions to be developed by the human-robot team. With the use of simulators for both robots and robotic systems, the behavior of the variability of the human-robot team is analyzed. Furthermore, from the perspective of cognitive systems engineering, the workspace can be studied as a joint cognitive system, where cognition is understood as distributed, in a symbiotic relationship between the human and technological agents. The implementation of a case study as a human-robot collaborative workspace allows evaluating the performance of the human-robot team, the impact on the operator's cognitive abilities, and the level of collaboration achieved in the human-robot team through a set of metrics and proven methods in other areas, such as cognitive systems engineering, human-machine interaction, and ergonomics. We conclude by discussing the findings and outlook regarding future research questions and possible developments.La introducción de tecnologías disruptivas de Industria 4.0 en el lugar de trabajo integradas a través de sistemas ciberfísicos humanos hace que los operadores enfrenten nuevos desafíos. Estos se reflejan en el aumento de las demandas presentadas en las capacidades físicas, sensoriales y cognitivas del operador. En esta investigación, las demandas cognitivas son las más interesantes. En esta perspectiva, los asistentes se presentan como una posible solución, no como una herramienta sino como un conjunto de funciones que amplifican las capacidades humanas, como exoesqueletos, robots colaborativos para capacidades físicas, realidad virtual y aumentada para capacidades sensoriales. Quizás chatbots y softbots para capacidades cognitivas, entonces surge la necesidad de preguntarnos: ¿Cómo se pueden desarrollar los sistemas de asistencia al operador 4.0 en el contexto de la fabricación industrial? ¿En qué capacidades el operador necesita más asistencia? A partir del paradigma actual de sistematización, se utilizan diferentes enfoques dentro del contexto del espacio de trabajo en la industria 4.0. Así, se utiliza el método de análisis de resonancia funcional (FRAM) para modelar el espacio de trabajo desde el enfoque del sistema sociotécnico, donde las relaciones entre los componentes son las más importantes entre las funciones a desarrollar por el equipo humano-robot. Con el uso de simuladores tanto para robots como para sistemas robóticos se analiza el comportamiento de la variabilidad del equipo humano-robot. Además, desde la perspectiva de la ingeniería de sistemas cognitivos, el espacio de trabajo puede ser estudiado como un sistema cognitivo conjunto, donde la cognición se entiende distribuida, en una relación simbiótica entre los agentes humanos y tecnológicos. La implementación de un caso de estudio como un espacio de trabajo colaborativo humano-robot permite evaluar el desempeño del equipo humano-robot, el impacto en las habilidades cognitivas del operador y el nivel de colaboración alcanzado en el equipo humano-robot a través de un conjunto de métricas y métodos probados en otras áreas, como la ingeniería de sistemas cognitivos, la interacción hombre-máquina y la ergonomía. Concluimos discutiendo los hallazgos y las perspectivas con respecto a futuras preguntas de investigación y posibles desarrollos.Postprint (published version

A Survey on Remote Operation of Road Vehicles

In recent years, the use of remote operation has been proposed as a bridge towards driverless mobility by providing human assistance remotely when an automated driving system finds a situation that is ambiguous and requires input from a remote operator. The remote operation of road vehicles has also been proposed as a way to enable drivers to operate vehicles from safer and more comfortable locations. While commercial solutions for remote operation exist, remaining challenges are being tackled by the research community, who is continuously testing and validating the feasibility of deploying remote operation of road vehicles on public roads. These tests range from the technological scope to social aspects such as acceptability and usability that affect human performance. This survey presents a compilation of works that approach the remote operation of road vehicles. We start by describing the basic architecture of remote operation systems and classify their modes of operation depending on the level of human intervention. We use this classification to organize and present recent and relevant work on the field from industry and academia. Finally, we identify the challenges in the deployment of remote operation systems in the technological, regulatory, and commercial scopes

Intelligent technologies for the aging brain: opportunities and challenges

Intelligent computing is rapidly reshaping healthcare. In light of the global burden of population aging and neurological disorders, dementia and elderly care are among the healthcare sectors that are most likely to benefit from this technological revolution. Trends in artificial intelligence, robotics, ubiquitous computing, neurotechnology and other branches of biomedical engineering are progressively enabling novel opportunities for technology-enhanced care. These Intelligent Assistive Technologies (IATs) open the prospects of supporting older adults with neurocognitive disabilities, maintain their independence, reduce the burden on caregivers and delay the need for long-term care (1, 2). While technology develops fast, yet little knowledge is available to patients and health professionals about the current availability, applicability, and capability of existing IATs. This thesis proposes a state-of-the-art analysis of IATs in dementia and elderly care. Our findings indicate that advances in intelligent technology are resulting in a rapidly expanding number and variety of assistive solutions for older adults and people with neurocognitive disabilities. However, our analysis identifies a number of challenges that negatively affect the optimal deployment and uptake of IATs among target users and care institutions. These include design issues, sub-optimal approaches to product development, translational barriers between lab and clinics, lack of adequate validation and implementation, as well as data security and cyber-risk weaknesses. Additionally, in virtue of their technological novelty, intelligent technologies raise a number of Ethical, Legal and Social Implications (ELSI). Therefore, a significant portion of this thesis is devoted to providing an early ethical Technology Assessment (eTA) of intelligent technology, hence contributing to preparing the terrain for its safe and ethically responsible adoption. This assessment is primarily focused on intelligent technologies at the human-machine interface, as these applications enable an unprecedented exposure of the intimate dimension of individuals to the digital infosphere. Issues of privacy, integrity, equality, and dual-use were addressed at the level of stakeholder analysis, normative ethics and human-rights law. Finally, this thesis is aimed at providing evidence-based recommendations for guiding participatory and responsible development in intelligent technology, and delineating governance strategies that maximize the clinical benefits of IATs for the aging world, while minimizing unintended risks

Application-driven visual computing towards industry 4.0 2018

245 p.La Tesis recoge contribuciones en tres campos: 1. Agentes Virtuales Interactivos: autónomos, modulares, escalables, ubicuos y atractivos para el usuario. Estos IVA pueden interactuar con los usuarios de manera natural.2. Entornos de RV/RA Inmersivos: RV en la planificación de la producción, el diseño de producto, la simulación de procesos, pruebas y verificación. El Operario Virtual muestra cómo la RV y los Co-bots pueden trabajar en un entorno seguro. En el Operario Aumentado la RA muestra información relevante al trabajador de una manera no intrusiva. 3. Gestión Interactiva de Modelos 3D: gestión online y visualización de modelos CAD multimedia, mediante conversión automática de modelos CAD a la Web. La tecnología Web3D permite la visualización e interacción de estos modelos en dispositivos móviles de baja potencia.Además, estas contribuciones han permitido analizar los desafíos presentados por Industry 4.0. La tesis ha contribuido a proporcionar una prueba de concepto para algunos de esos desafíos: en factores humanos, simulación, visualización e integración de modelos

Methodical Implementation Of Digital Data Consistency In Assembly Lines Of A Learning Factory

The possibility of acquiring data in production and manufacturing processes is almost limitless. But especially small and medium-sized enterprises (SMEs) lack the knowledge to successfully integrate digital tools and use real-time production data for critical decision-making. Numerous initiatives already exist to inform and support SMEs in Germany, funded at various levels by municipal, federal, and state entities. These initiatives offer expertise in digitalisation and provide diverse activities to support SMEs across different industrial sectors. To make abstract concepts such as artificial intelligence (AI) or digitalisation more tangible, demonstrations and practical best practice showcases demonstrate methodological approaches for facilitating independent implementation initiatives within SMEs. However, most of these activities primarily showcase rudimentary and isolated technological implementations, with limited integration into the complex environment of a manufacturing company. This paper focuses on a holistic methodical brownfield implementation of a demonstrator for digital data consistency in an assembly line of a learning factory by applying an extended methodology for implementing demonstrators and its validation by industrial participants. It stresses the complexity of production data acquisition in a practical environment and illustrates a best-practice showcase. Key performance indicators are visualized by acquiring, storing, and cross-linking data points. The demonstrator is implemented and evaluated by SMEs' representatives, to show promising potential for sustainable knowledge transfer into the SMEs