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Towards a maturity model of human-centered AI – A reference for AI implementation at the workplace
Abstract
The continuous acquisition of new digital competences and the development of situational learning assistance systems will become more important than ever in the coming years, because the world of work is becoming more complex, more informative and all above more data-driven. Jobs are changing due to increasing digitalisation, whereby the use of modern technologies must be designed in a way, that employees can continue to work productively in the company despite these changes and benefit purposefully from digital solutions. The research results presented under the main topic „Competence development and learning assistance systems for the data-driven future“ address this problem of state of the art technologies in the workplace and their effects on workers. The members of the Scientific Society for Work and Business Organisation (WGAB) present innovative concepts and research results for practitioners and scientists and thus provide valuable input for current challenges.
... Maturity models are widespread approaches to support the dialogue and transfer from research to practise as they aim at systemizing academic knowledge for practical application. Some of them neglect the non-technological issues (Aronsson et al. 2021) while others explore a broader view on relevant dimensions (Lichtenthaler 2020, Wilkens et al. 2021a). However, the implementation journeys in practice tend to follow a narrow approach giving primarily emphasis to technological issues. ...
... I. The perspective reflecting on the human-centricity in job design (Wilkens et al. 2021a). This is the way to address the transformation challenge from a sociomaterial perspective. ...
... The maturity model for human-centred job design (Wilkens et al. 2021a) allows to identify, at an early stage of development, which necessary and sufficient conditions in a concrete AI-enabled workplace are central to guarantee human-centricity in the specific job domain. This determination is made from the perspective of the AI users, focusing on the concrete work process within the actual organizational conditions. ...
Failures in digital transformation projects are reported frequently. This is especially true for small and medium sized enterprises (SMEs). Considered as a major reason is a missing socio-technical systems (STS) perspective. Although it is widely approved that the interplay between technological characteristics, individual behaviour, and organizational properties matters for digital transformation, these multiple dimensions of digital change tend to be underestimated when it comes to practice. The aim of this paper is to find explanations for this shortcoming and to derive propositions how to design a successful socio-technical implementation journey for the digital transformation in SMEs. The paper refers to the conceptual baselines of STS theory and compares perspectives occurring in the disciplines of work science and engineering science. This is complemented by a comparative analysis of two monodisciplinary as well as one interdisciplinary case study. Reflecting on current limitations the paper proposes a framework for a holistic STS approach that is more likely to be adapted in practice. This integrated maturity approach for digital transformation projects brings together former separated STS perspectives and thus explores a multi-stakeholder approach.
... In a narrow definition, and in the context of this paper, humanizing AI involves the creation and utilization of AI tools that: (i) enhance human potential, build trust, and minimize fear (ii) can interact with humans in a natural, human-like manner, and (iii) can process information during these interactions in a manner similar to human cognitive processes (Fenwick and Molnar, 2022). AI evolves over a path of maturity spanning a continuum of contemporary cognitive architectures to more socio-cognitive and cross-domain architectures (e.g., Gupta et al., 2023), and in terms of implementation and human-centricity, needs to be interpreted in the context of place and time (Wilkens et al., 2021a). These advancements can help create AI with more general intelligence and support ongoing efforts to bring humans and machines closer together. ...
... We propose adopting a multi-disciplinary, humancentric, and integrated approach that can address the current concerns and fears surrounding AI development and deployment in the workplace. AI evolves over a path of maturity spanning a continuum of contemporary cognitive architectures to more sociocognitive and cross-domain architectures (e.g., Gupta et al., 2023), and in terms of implementation and human-centricity, needs to be interpreted in the context of place and time (Wilkens et al., 2021a). This paper, therefore, categorizes the AI-HRM journey into technocratic, human-AI integration, and fully-embedded AI phases, each presenting unique challenges and opportunities. ...
The functions of human resource management (HRM) have changed radically in the past 20 years due to market and technological forces, becoming more cross-functional and data-driven. In the age of AI, the role of HRM professionals in organizations continues to evolve. Artificial intelligence (AI) is transforming many HRM functions and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. A growing body of evidence highlights the benefits AI brings to the field of HRM. Despite the increased interest in AI-HRM scholarship, focus on human-AI interaction at work and AI-based technologies for HRM is limited and fragmented. Moreover, the lack of human considerations in HRM tech design and deployment can hamper AI digital transformation efforts. This paper provides a contemporary and forward-looking perspective to the strategic and human-centric role HRM plays within organizations as AI becomes more integrated in the workplace. Spanning three distinct phases of AI-HRM integration (technocratic, integrated, and fully-embedded), it examines the technical, human, and ethical challenges at each phase. Using a human-centric approach, our framework provides suggestions on how to overcome these existing challenges in people management, culture, and compliance. Our paper highlights the importance of the evolving role of HRM in the AI-driven organization and provides a roadmap on how to bring humans and machines closer together in the workplace.
... Humanizing AI should occur at various interconnected levels (within the organization) and act as a conduit to addressing many of the ethical and people challenges between humans and machines [31]. As AI matures, it moves toward more advanced cognitive architectures [13], necessitating context-specific interpretations of its use and human-centricity [37]. However, focusing only on creating AI systems that mimic human characteristics is not sufficient. ...
The rapid advancement of Artificial Intelligence (AI) in the business sector has led to a new era of digital transformation. AI is transforming processes, functions, and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. However, the implementation and adoption of AI systems in the organization is not without challenges, ranging from technical issues to human-related barriers, leading to failed AI transformation efforts or lower than expected gains. We argue that while engineers and data scientists excel in handling AI and data-related tasks, they often lack insights into the nuanced human aspects critical for organizational AI success. Thus, Human Resource Management (HRM) emerges as a crucial facilitator, ensuring AI implementation and adoption are aligned with human values and organizational goals. This paper explores the critical role of HRM in harmonizing AI's technological capabilities with human-centric needs within organizations while achieving business objectives. Our positioning paper delves into HRM's multifaceted potential to contribute toward AI organizational success, including enabling digital transformation, humanizing AI usage decisions, providing strategic foresight regarding AI, and facilitating AI adoption by addressing concerns related to fears, ethics, and employee well-being. It reviews key considerations and best practices for operationalizing human-centric AI through culture, leadership, knowledge, policies, and tools. By focusing on what HRM can realistically achieve today, we emphasize its role in reshaping roles, advancing skill sets, and curating workplace dynamics to accommodate human-centric AI implementation. This repositioning involves an active HRM role in ensuring that the aspirations, rights, and needs of individuals are integral to the economic, social, and environmental policies within the organization. This study not only fills a critical gap in existing research but also provides a roadmap for organizations seeking to improve AI implementation and adoption and humanizing their digital transformation journey.
... The key challenge here is that the system design follows an engineering approach of standardising processes for production flows while considering the technology as a tool for regulating systems' need and compensating individual shortcomings in fatigue, disabilities etc. (Wilkens et al. 2021). The underlying process optimization reduces (unintendendly) the space for human creative intent for performative interaction for system regulation. ...
Industrial management is facing many challenges, one need is to adopt sustainable practices. As climate change is becoming an increasingly urgent problem, industrial management must find ways to reduce waste and pollution, conserve natural resources, and protect the environment and employees. Industrial management also needs to embrace new technologies like virtual reality, artificial intelligence, and the Internet of Things. These challenges are becoming more prevalent, managers and decision makers must find ways to adopt these new technologies in order to remain competitive. Furthermore, with the workforce becoming more diverse and globalized organisations have to attract and retain top talent. For industrial management it is important to create an inclusive work environment. Current research approaches from the members of the Scientific Society for Work and Business Organisation (WGAB) are presented in this book.
... These different understandings result from the transdisciplinary character of the research field. With dis-ciplines as diverse as psychology, computer science, information systems, engineering, social sciences, and work science looking at the subject, distinct aspects are brought into focus (Wilkens et al. 2021a). Complexity gets even higher when dealing with real-world systems and all their different technical and non-technical components, subsystems as well as various stakeholders from different organizational levels and their interdependencies (Shneiderman 2020). ...
Artificial intelligence (AI) and machine learning are increasingly used in workplaces and human life in general. However, voices from indus-trial practice as well as academia point to issues with purely technology-oriented approaches to the development and implementation of AI applica-tions. These can include low user acceptance and negative impact on job identification. In response, approaches for a human-centered design of work with AI come into focus. This paper contributes an analytical tool for identifying potentials as a first step in a process model for the development and deployment of AI in a human-centered way. The tool was empirically validated in various real-world settings. The implementation was carried out by the interdisciplinary team of the HUMAINE project consisting of social scientists, engineers, psychologists, information systems researchers and computer scientists. The paper presents the scope, the methods used as well as lessons learned from multiple executions of this concept.
... Auf Basis eines systematischen Reviews arbeiteten Wilkens et al. [10] folgende vier Ausprägungsperspektiven von menschenzentrierter Mensch-KI-Arbeitsgestaltung heraus. [20,22]. KI kann dabei zur Steigerung der Effizienz, Genauigkeit und Qualität einer Arbeitsausführung dienen, da in Situationen mit mehrdeutiger Informationslage hohe Anforderungen an die Genauigkeit und Fehlervermeidung in Entscheidungsprozessen gestellt werden [23 -25]. ...
Der Einsatz von Künstlicher Intelligenz (KI) in Produktion und Fertigung wird die Arbeitswelt und die Fabrikfertigung verändern. Dieser Beitrag widmet sich der Frage, wie die Arbeit mit KI im Interesse des Menschen gestaltet werden kann. Dafür werden verschiedene Perspektiven auf menschzentrierte KI vorgestellt. Nachfolgend wird das Modell SMART-er Arbeitsgestaltung eingeführt [37, 43]. Dieses dient als Heuristik, vor dessen Hintergrund Kriterien für die Entwicklung menschzentrierter hybrider Intelligenzen diskutiert werden. Abschließend werden fünf Gestaltungsschritte abgeleitet, die Unternehmen bei der menschzentrierten Gestaltung von Mensch-KI-Teams helfen können.
This chapter examines the transformative role of next-generation artificial intelligence (AI) in education, reshaping traditional learning environments and challenging educators to redefine their professional roles. This chapter discusses how AI enhances educational practices by enabling personalized instruction, supporting data-driven decision-making, and streamlining classroom management, thus improving the efficiency and effectiveness of teaching. The chapter also addresses the pedagogical shifts, ongoing professional development, and ethical considerations necessary for educators to integrate AI into the classroom successfully. By offering strategies for AI integration and highlighting the vital role of teachers in this evolving educational landscape, this chapter seeks to equip educators with the tools and insights to confidently and innovatively embrace the future of teaching.
This research work addresses the development and evaluation of a maturity model for artificial intelligence (AI) deployment capability of manufacturing companies. Based on the literature research and 11 maturity models, an industry-specific maturity model is developed. The maturity model contains sequential maturity levels with defined maturity dimensions and 29 indicators. Among the main findings is that the developed model is capable of calculating an AI deployment capability level and fully counterbalance the maturity indicators.
Purpose
The discourse on the human-centricity of AI at work needs contextualization. The aim of this study is to distinguish prevalent criteria of human-centricity for AI applications in the scientific discourse and to relate them to the work contexts for which they are specifically intended. This leads to configurations of actor-structure engagements that foster human-centricity in the workplace.
Theoretical foundation
The study applies configurational theory to sociotechnical systems’ analysis of work settings. The assumption is that different approaches to promote human-centricity coexist, depending on the stakeholders responsible for their application.
Method
The exploration of criteria indicating human-centricity and their synthesis into configurations is based on a cross-disciplinary literature review following a systematic search strategy and a deductive-inductive qualitative content analysis of 101 research articles.
Results
The article outlines eight criteria of human-centricity, two of which face challenges of human-centered technology development (trustworthiness and explainability), three challenges of human-centered employee development (prevention of job loss, health, and human agency and augmentation), and three challenges of human-centered organizational development (compensation of systems’ weaknesses, integration of user-domain knowledge, accountability, and safety culture). The configurational theory allows contextualization of these criteria from a higher-order perspective and leads to seven configurations of actor-structure engagements in terms of engagement for (1) data and technostructure, (2) operational process optimization, (3) operators’ employment, (4) employees’ wellbeing, (5) proficiency, (6) accountability, and (7) interactive cross-domain design. Each has one criterion of human-centricity in the foreground. Trustworthiness does not build its own configuration but is proposed to be a necessary condition in all seven configurations.
Discussion
The article contextualizes the overall debate on human-centricity and allows us to specify stakeholder-related engagements and how these complement each other. This is of high value for practitioners bringing human-centricity to the workplace and allows them to compare which criteria are considered in transnational declarations, international norms and standards, or company guidelines.
Introduction: With the advancement of technology and the increasing utilization of AI, the nature of human work is evolving, requiring individuals to collaborate not only with other humans but also with AI technologies to accomplish complex goals. This requires a shift in perspective from technology-driven questions to a human-centered research and design agenda putting people and evolving teams in the center of attention. A socio-technical approach is needed to view AI as more than just a technological tool, but as a team member, leading to the emergence of human-AI teaming (HAIT). In this new form of work, humans and AI synergistically combine their respective capabilities to accomplish shared goals.
Methods: The aim of our work is to uncover current research streams on HAIT and derive a unified understanding of the construct through a bibliometric network analysis, a scoping review and synthetization of a definition from a socio-technical point of view. In addition, antecedents and outcomes examined in the literature are extracted to guide future research in this field.
Results: Through network analysis, five clusters with different research focuses on HAIT were identified. These clusters revolve around (1) human and (2) task-dependent variables, (3) AI explainability, (4) AI-driven robotic systems, and (5) the effects of AI performance on human perception. Despite these diverse research focuses, the current body of literature is predominantly driven by a technology-centric and engineering perspective, with no consistent definition or terminology of HAIT emerging to date.
Discussion: We propose a unifying definition combining a human-centered and team-oriented perspective as well as summarize what is still needed in future research regarding HAIT. Thus, this work contributes to support the idea of the Frontiers Research Topic of a theoretical and conceptual basis for human work with AI systems.
Introduction
Artificial intelligence (AI) is seen as a driver of change, especially in the context of business, due to its progressive development and increasing connectivity in operational practice. Although it changes businesses and organizations vastly, the impact of AI implementation on human workers with their needs, skills, and job identity is less considered in the development and implementation process. Focusing on humans, however, enables unlocking synergies as well as desirable individual and organizational outcomes.
Methods
The objective of the present study is (a) to develop a survey-based inventory from the literature on work research and b) a first validation with employees encountering an AI application. The Job Perception Inventory (JOPI) functions as a work-analytical tool to support the human-centered implementation and application of intelligent technologies. It is composed of established and self-developed scales, measuring four sections of work characteristics, job identity, perception of the workplace, and the evaluation of the introduced AI.
Results
Overall, the results from the first study from a series of studies presented in this article indicate a coherent survey inventory with reliable scales that can now be used for AI implementation projects.
Discussion
Finally, the need and relevance of the JOPI are discussed against the background of the manufacturing industry.
Objective
We define human–autonomy teaming and offer a synthesis of the existing empirical research on the topic. Specifically, we identify the research environments, dependent variables, themes representing the key findings, and critical future research directions.
Background
Whereas a burgeoning literature on high-performance teamwork identifies the factors critical to success, much less is known about how human–autonomy teams (HATs) achieve success. Human–autonomy teamwork involves humans working interdependently toward a common goal along with autonomous agents. Autonomous agents involve a degree of self-government and self-directed behavior (agency), and autonomous agents take on a unique role or set of tasks and work interdependently with human team members to achieve a shared objective.
Method
We searched the literature on human–autonomy teaming. To meet our criteria for inclusion, the paper needed to involve empirical research and meet our definition of human–autonomy teaming. We found 76 articles that met our criteria for inclusion.
Results
We report on research environments and we find that the key independent variables involve autonomous agent characteristics, team composition, task characteristics, human individual differences, training, and communication. We identify themes for each of these and discuss the future research needs.
Conclusion
There are areas where research findings are clear and consistent, but there are many opportunities for future research. Particularly important will be research that identifies mechanisms linking team input to team output variables.
Artificial Intelligence (AI) has diffused into many areas of our private and professional life. In this research note, we describe exemplary risks of black-box AI, the consequent need for explainability, and previous research on Explainable AI (XAI) in information systems research. Moreover, we discuss the origin of the term XAI, generalized XAI objectives and stakeholder groups, as well as quality criteria of personalized explanations. We conclude with an outlook to future research on XAI.
Purpose
The aim of this paper is to outline how artificial intelligence (AI) can augment learning process in the workplace and where there are limitations.
Design/methodology/approach
The paper is a theoretical-based outline with reference to individual and organizational learning theory, which are related to machine learning methods as they are currently in use in the workplace. Based on these theoretical insights, the paper presents a qualitative evaluation of the augmentation potential of AI to assist individual and organizational learning in the workplace.
Findings
The core outcome is that there is an augmentation potential of AI to enhance individual learning and development in the workplace, which however should not be overestimated. AI has a complementarity to individual intelligence, which can lead to an advancement, especially in quality, accuracy and precision. Moreover, AI has a potential to support individual competence development and organizational learning processes. However, a further outcome is that AI in the workplace is a double-edged sword, as it easily shows reinforcement effects in individual and organizational learning, which have a backside of unintended effects.
Research limitations/implications
The conceptual outline makes use of examples for illustrating phenomenon but needs further empirical analysis. The research focus on the meso level of the workplace does not fully refer to macro level outcomes.
Practical implications
The practical implication is that it is a matter of socio-technical job design to integrate AI in the workplace in a valuable manner. There is a need to keep the human-in-the-loop and to complement AI-based learning approaches with non-AI counterparts to reach augmentation.
Originality/value
The paper faces workplace learning from an interdisciplinary perspective and bridges insights from learning theory with methods from the machine learning community. It directs the social science discourse on AI, which is often on macro level to the meso level of the workplace and related issues for job design and therefore provides a complementary perspective.
This paper identifies and measures AI-related developments in science, algorithms and technologies using information from scientific publications, open source software (OSS) and patents. A three-pronged approach relying on established bibliometric and patent-based methods, and machine learning (ML) implemented on purposely collected OSS data, unveils a marked increase of AI-related developments over time. Since 2015, AI-related publications increased at 23% a year; in 2014-18, OSS commits related to AI grew about three times as other OSS contributions; in 2017 the share of AI-related IP5 patent families averaged more than 2.3%. The growing role of China in the AI space emerges throughout.
To guide future discussions about managing artificial intelligence (AI), this article suggests an AI management framework with five maturity levels, which are comparable to the five levels of the autonomous driving framework from no automation to complete automation. If companies move beyond Isolated Ignorance (Level 0), they are characterized by an Initial Intent (Level 1), which typically evolves towards an Independent Initiative (Level 2). A more advanced management leads to Interactive Implementation (Level 3) and Interdependent Innovation (Level 4). On this basis, a close combination of AI and human knowledge enables a sustainable competitive advantage with Integrated Intelligence (Level 5). This framework draws on the intelligence-based approach to company performance, and it provides the basis for an AI maturity assessment in organizations. It further helps to identify many firms’ managerial challenges as well as major organizational limitations even in those firms that are often considered as AI leaders.
We propose a central role for work design in understanding the effects of digital technologies. We give examples of how new technologies can – depending on various factors ‐ positively and negatively affect job resources (autonomy/control, skill use, job feedback, relational aspects) and job demands (e.g., performance monitoring), with consequences for employee well‐being, safety, and performance. We identify four intervention strategies. First, work design choices need to be proactively considered during technology implementation, consistent with the sociotechnical systems principle of joint optimization. Second, human‐centred design principles should be explicitly considered in the design and procurement of new technologies. Third, organizationally‐oriented intervention strategies need to be supported by macro‐level policies. Fourth, there is a need to go beyond a focus on upskilling employees to help them adapt to technology change, to also focus on training employees, as well as other stakeholders, in work design and related topics. Finally, we identify directions for moving the field forward, including new research questions (e.g., job autonomy in the context of machine learning; understanding designers’ work design mindsets; investigating how job crafting applies to technology); a re‐orientation of methods (e.g., interdisciplinary, intervention studies); and steps for achieving practical impact.
Effective collaboration between humans and agents depends on humans maintaining an appropriate understanding of and calibrated trust in the judgment of their agent counterparts. The Situation Awareness-based Agent Transparency (SAT) model was proposed to support human awareness in human–agent teams. As agents transition from tools to artificial teammates, an expansion of the model is necessary to support teamwork paradigms, which require bidirectional transparency. We propose that an updated model can better inform human–agent interaction in paradigms involving more advanced agent teammates. This paper describes the model's use in three programmes of research, which exemplify the utility of the model in different contexts – an autonomous squad member, a mediator between a human and multiple subordinate robots, and a plan recommendation agent. Through this review, we show that the SAT model continues to be an effective tool for facilitating shared understanding and proper calibration of trust in human–agent teams.
Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets ("big data"), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods per se, there are many opportunities and challenges for the imaging community, including the development of a common nomenclature, better ways to share image data, and standards for validating AI program use across different imaging platforms and patient populations. AI surveillance programs may help radiologists prioritize work lists by identifying suspicious or positive cases for early review. AI programs can be used to extract "radiomic" information from images not discernible by visual inspection, potentially increasing the diagnostic and prognostic value derived from image datasets. Predictions have been made that suggest AI will put radiologists out of business. This issue has been overstated, and it is much more likely that radiologists will beneficially incorporate AI methods into their practices. Current limitations in availability of technical expertise and even computing power will be resolved over time and can also be addressed by remote access solutions. Success for AI in imaging will be measured by value created: increased diagnostic certainty, faster turnaround, better outcomes for patients, and better quality of work life for radiologists. AI offers a new and promising set of methods for analyzing image data. Radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI.
The focus of the following article is on the use of new robotic systems in the manufacturing industry with respect to the social dimension. Since “intuitive” human–machine interaction (HMI) in robotic systems becomes a significant objective of technical progress, new models of work organization are needed. This hypothesis will be investigated through the following two aims: The first aim is to identify relevant research questions related to the potential use of robotic systems in different systems of work organization at the manufacturing shop-floor level. The second aim is to discuss the conceptualization of (old) organizational problems of human–robot interaction (HRI). In this context, the article reflects on the limits of cognitive and perceptual workload for robot operators in complex working systems. This will be particularly relevant whenever more robots with different “roles” are to be increasingly used in the manufacturing industry. The integration of such complex socio-technical systems needs further empirical and conceptual research with regard to “social” aspects of the technical dimension. Future research should, therefore, also integrate economic and societal issues to understand the full dimensions of new human–robot interaction in industry today.
Complex design problems often cannot be solved by individuals or by homogenous groups. Communities of interest (CoIs) (defined by their collective concern with the resolution of a problem) bring together stakeholders from different communities of practice (CoP). Reaching a common understanding between these stakeholders is a major challenge due to the "symmetry of ignorance" caused by their respective cultures and their use of different knowledge systems. Our research has focused on the development of conceptual frameworks and innovative socio-technical environments to exploit the "symmetry of ignorance" as a source for social creativity among CoIs. Gerhard Fischer 2 IRIS'24, Norway 1
The paper presents findings from a systematic literature review exploring the meaning of human-centered AI. The review includes 85 papers from certain disciplines and leads to a distinction of five co-existing perspectives: (1) a deficit-oriented, (2) a data reliability-oriented, (3) a protection oriented , (4) a potential-oriented and (5) a political-oriented understanding of how to reach human centricity while using AI in the workplace. Each perspective gives emphasis to another core dimension for evaluating the level of human-centricity. This goes from compensating individual weaknesses with the help of AI to enhancing data reliability and protecting individual integrity, to leveraging individual potential and guaranteeing individual control over AI. Each perspective is exemplified with a use case.
The increasing use of People Analytics to manage people in organizations ushers in an era of algorithmic management. People analytics are said to allow decision-makers to make evidence-based, bias-free, and objective decisions, and expand workers' opportunities for personal and professional growth. Drawing on a virtue ethics approach, we argue that the use of people analytics in organizations can create a vicious cycle of ethical challenges - algorithmic opacity, datafication, and nudging - which limit people's ability to cultivate their virtue and flourish. We propose that organizations can mitigate these challenges and help workers develop their virtue by reframing people analytics as a fallible companion technology, introducing new organizational roles and practices, and adopting alternative technology design principles. We discuss the implications of this approach for organizations and for the design of people analytics, and propose directions for future research.
Experiences gained during project acquisition and ongoing research projects show that among the most effective ways to quick understand-ing for many customers, especially those representing Small and Medium-sized Enterprises (SMEs), is hands-on experience. What hours of presentation and talk cannot explain - five minutes of a live demonstration can. This evidence highlights the importance of realizing platforms on which complex concepts from basic research can be seen running in practice, in easy-to-understand packages. For example, when considering the potential benefits that automation could bring to SMEs (e.g., improved time to market, efficiency and quality), a demonstrator can be a convincing mean to show the potential change from classical rigid automation systems to more flexible counterparts from modern trends. In this context, SMEs can directly benefit from a demonstrator by getting introduced to flexible production systems that can re-duce their risk of investment.
Artificial intelligence (AI) is a branch of computer science and a technology aimed at developing the theories, methods, algorithms, and applications for simulating and extending human intelligence. Modern AI enables going from an old world-where people give computers rules to solve problems-to a new world-where people give computers problems directly and the machines learn how to solve them on their own using a set of algorithms. An algorithm is a self-contained sequence of instructions and actions to be performed by a computational machine. Starting from an initial state and initial input, the instructions describe computational steps, which, when executed, proceed through a finite number of well-defined successive states, eventually producing an output and terminating at a final ending state. AI algorithms are a rich set of algorithms used to perform AI tasks, notably those pertaining to perception and cognition that involve learning from data and experiences simulating human intelligence.
Context: Maturity models offer organizations a simple but effective possibility to measure the quality of their processes. Emerged out of software engineering, the application fields have widened and maturity model research is becoming more important. During the last two decades the publication amount steadily rose as well. Until today, no studies have been available summarizing the activities and results of the field of maturity model research. Objective: The objective of this paper is to structure and analyze the available literature of the field of maturity model research to identify the state-of-the-art research as well as research gaps. Method: A systematic mapping study was conducted. It included relevant publications of journals and IS conferences. Mapping studies are a suitable method for structuring a broad research field concerning research questions about contents, methods, and trends in the available publications. Results: The mapping of 237 articles showed that current maturity model research is applicable to more than 20 domains, heavily dominated by software development and software engineering. The study revealed that most publications deal with the development of maturity models and empirical studies. Theoretical reflective publications are scarce. Furthermore, the relation between conceptual and design-oriented maturity model development was analyzed, indicating that there is still a gap in evaluating and validating developed maturity models. Finally, a comprehensive research framework was derived from the study results and implications for further research are given. Conclusion: The mapping study delivers the first systematic summary of maturity model research. The categorization of available publications helps researchers gain an overview of the state-of-the-art research and current research gaps. The proposed research framework supports researchers categorizing their own projects. In addition, practitioners planning to use a maturity model may use the study as starting point to identify which maturity models are suitable for their domain and where limitations exist.
From the viewpoint of knowledge and intelligence, to bridge data mining and agents, this paper deal with an efficient way that is building intelligent systems by means of meta-synthesis proposed by Chinese scientists, which is used multiple human intelligence implantations for intelligent system, take full advantage of agents as well as analysis and meta-synthesis. Such systems combine the qualitative quantitative intelligence in knowledge conductive and human-centred Cooperative Problem-Solving (CPS), involving human centred Cooperative Problem-Solving (CPS), meta-synthetic engineering techniques, human human interactions and organisation based on group thinking technology, Human Computer Interactions (HCI), and computer computer interactions based on agents technology.
In this essay, I begin with the premise that everyday organizing is inextricably bound up with materiality and contend that this relationship is inadequately reflected in organizational studies that tend to ignore it, take it for granted, or treat it as a special case. The result is an understanding of organizing and its conditions and consequences that is necessarily limited. I then argue for an alternative approach, one that posits the constitutive entanglement of the social and the material in everyday life. I draw on some empirical examples to help ground and illustrate this approach in practice and conclude by suggesting that a reconfiguration of our conventional assumptions and considerations of materiality will help us more effectively recognize and understand the multiple, emergent, and shifting sociomaterial assemblages entailed in contemporary organizing.
Historically, the development of computer systems has been primarily a technology-driven phenomenon, with technologists believing that "users can adapt" to whatever they build. Human- centered design advocates that a more promising and enduring approach is to model users' natural behavior to begin with so that interfaces can be designed that are more intuitive, easier to learn, and freer of performance errors. In this paper, we illustrate different user-centered design principles and specific strategies, as well as their advantages and the manner in which they enhance users' performance. We also summarize recent research findings from our lab comparing the performance characteristics of different educational interfaces that were based on user-centered design principles. One theme throughout our discussion is human- centered design that minimizes users' cognitive load, which effectively frees up mental resources for performing better while also remaining more attuned to the world around them. Categories and Subject Descriptors
The use of autonomous, mobile professional service robots in diverse workplaces is expected to grow substantially over the next decade. These robots often will work side by side with people, collaborating with employees on tasks. Some roboticists have argued that, in these cases, people will collaborate more naturally and easily with humanoid robots as compared with machine-like robots. It is also speculated that people will rely. on and share responsibility more readily with robots that are in a position of authority. This study sought to clarify the effects of robot appearance and relative status on human-robot collaboration by investigating the extent to which people relied on and ceded responsibility to a robot coworker. In this study, a 3 x 3 experiment was conducted with human likeness (human, human-like robot, and machine-like robot) and status (subordinate, peer, and supervisor) as dimensions. As far as we know, this study is one of the first experiments examining how people respond to robotic coworkers. As such, this study attempts to design a robust and transferable sorting and assembly task that capitalizes on the types of tasks robots are expected to do and is embedded in a realistic scenario in which the participant and confederate are interdependent. The results show that participants retained more responsibility for the successful completion of the task when working with a machine-like as compared with a humanoid robot, especially when the machine-like robot was subordinate. These findings suggest that humanoid robots may be appropriate for settings in which people have to delegate responsibility to these robots or when the task is too demanding for people to do, and when complacency is not a major concern. Machine-like robots, however, may be more appropriate when robots are expected to be unreliable, are less well-equipped for the task than people are, or in other situations in which personal responsibility should be emphasized.
Human centered AI design for clinical monitoring and data management
- H S Adnan
- S Matthews
- M Hackl
- P P Das
- M Manaswini
- S Gadamsetti
- M Filali
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