Human-centered Approach Research Articles

The status quo of architecture and its impact on urban management: Christopher Alexander's insights

Christopher Alexander argued that modernist approach to architecture prioritizes innovation, abstraction, and mechanistic efficiency at the expense of human-centered and organic values. According to Christopher Alexander, this shift has led to a proliferation of buildings that are visually striking, but often feel cold, impersonal, and disconnected from the deeper needs of the people who inhabit and/or see them. Alexander called for a return to timeless architectural principles such as harmony, balance, and a deep connection to the natural and cultural context. He introduced the concept of living structure, which emphasizes creating spaces that resonate with the intrinsic order found in nature and human life, fostering environments that are not only functional and beautiful but also profoundly life-affirming. Alexander challenged the dominance of “iconic” but alienating designs, calling for a holistic, human-centered approach to architecture that prioritizes the well-being of individuals and communities, creating spaces that nurture a sense of place, belonging, and harmony with the world around them.

Human-centered approaches to AI-assisted work: the future of work?

Human-centered approaches to AI-assisted work: the future of work?

How to Collaborate Without Burning Out

Abstract: This article examines the challenge of collaboration burnout in the modern workplace and provides recommendations for leaders to facilitate productive collaboration without overwhelming employees. Drawing on research and the author's experience as an organizational consultant, key drivers of burnout are identified as excessive and inefficient meetings, unclear collaboration expectations, and poor work-life integration. Five practical recommendations are outlined for leaders to mitigate these risks, including establishing clear collaboration guidelines, fostering an efficiency culture, empowering autonomy through flexibility and trust, encouraging flexible work arrangements, and modeling good work-life harmony. The recommendations are brought to life through examples of their application in contexts like law firms, software companies, and advertising agencies. The article argues that a balanced, human-centered approach to collaboration considering employee well-being is critical for organizations to sustain engaged, innovative teams over the long-term.

Can a Transparent Machine Learning Algorithm Predict Better than Its Black Box Counterparts? A Benchmarking Study Using 110 Data Sets.

We developed a novel machine learning (ML) algorithm with the goal of producing transparent models (i.e., understandable by humans) while also flexibly accounting for nonlinearity and interactions. Our method is based on ranked sparsity, and it allows for flexibility and user control in varying the shade of the opacity of black box machine learning methods. The main tenet of ranked sparsity is that an algorithm should be more skeptical of higher-order polynomials and interactions a priori compared to main effects, and hence, the inclusion of these more complex terms should require a higher level of evidence. In this work, we put our new ranked sparsity algorithm (as implemented in the open source R package, sparseR) to the test in a predictive model "bakeoff" (i.e., a benchmarking study of ML algorithms applied "out of the box", that is, with no special tuning). Algorithms were trained on a large set of simulated and real-world data sets from the Penn Machine Learning Benchmarks database, addressing both regression and binary classification problems. We evaluated the extent to which our human-centered algorithm can attain predictive accuracy that rivals popular black box approaches such as neural networks, random forests, and support vector machines, while also producing more interpretable models. Using out-of-bag error as a meta-outcome, we describe the properties of data sets in which human-centered approaches can perform as well as or better than black box approaches. We found that interpretable approaches predicted optimally or within 5% of the optimal method in most real-world data sets. We provide a more in-depth comparison of the performances of random forests to interpretable methods for several case studies, including exemplars in which algorithms performed similarly, and several cases when interpretable methods underperformed. This work provides a strong rationale for including human-centered transparent algorithms such as ours in predictive modeling applications.

Towards a neuro-symbolic cycle for human-centered explainability

Deep learning is being very successful in supporting humans in the interpretation of complex data (such as images and text) for critical decision tasks. However, it still remains difficult for human experts to understand how such results are achieved, due to the “black box” nature of the deep models used. In high-stake decision making scenarios such as the interpretation of medical imaging for diagnostics, such a lack of transparency still hinders the adoption of these techniques in practice. In this position paper we present a conceptual methodology for the design of a neuro-symbolic cycle to address the need for explainability and confidence (including trust) of deep learning models when used to support human experts in high-stake decision making, and we discuss challenges and opportunities in the implementation of such cycle as well as its adoption in real world scenarios. We elaborate on the need to leverage the potential of hybrid artificial intelligence combining neural learning and symbolic reasoning in a human-centered approach to explainability. We advocate that the phases of such a cycle should include i) the extraction of knowledge from a trained network to represent and encode its behaviour, ii) the validation of the extracted knowledge through commonsense and domain knowledge, iii) the generation of explanations for human experts, iv) the ability to map human feedback into the validated representation from i), and v) the injection of some of this knowledge in a non-trained network to enable knowledge-informed representation learning. The holistic combination of causality, expressive logical inference, and representation learning, would result in a seamless integration of (neural) learning and (cognitive) reasoning that makes it possible to retain access to the inherently explainable symbolic representation without losing the power of the deep representation. The involvement of human experts in the design, validation and knowledge injection process is crucial, as the conceptual approach paves the way for a new human–ai paradigm where the human role goes beyond that of labeling data, towards the validation of neural-cognitive knowledge and processes.

Articulation of Hadith in Minangkabau Socio-Religious Contexts: A Study on the Collection of Speeches by Sheikh Abdul Lathif Syakur (1882-1963)

The collection of speeches (CoS) written by Sheikh Abdul Lathif Syakur (SALS), a scholar who experienced both the colonial and old-order eras, serves as a thematic compilation of hadith reflecting the mid-20th century Minangkabau writing tradition. Through this collection, SALS demonstrated his expertise as a hadith scholar by analyzing the context of his era and interpreting social realities based on hadiths pertinent to society. This article aims to delve into the socio-religious vision that SALS constructs through the themes articulated in his speeches. Using a descriptive-historical method and thematic analysis, this article reveals that SALS founded his socio-religious vision on four main pillars: renewal, community integrity, moderation, and the roles of women in public life. SALS's ability to adapt hadith to his time, combined with his inclusive and human-centered approach, portrays him as a progressive scholar, challenging the rigid stereotype of hadith scholars.

Emerging Partnerships of SDGs and HRM Practices During the Pandemic: A Study of the Indian IT Industry

Post-pandemic, the world of work has changed, and the pandemic has accelerated the responsible business agenda, representing a huge opportunity for the human-centric approach to HRM to transform the world of work. The drive towards responsible and sustainable business demands a new way of thinking, and the old conventional thinking approach addressing piece-meal solutions is an approach of the past. The human resource management (HRM) practices of multinational corporation (MNC) subsidiaries in India in the IT sector are studied within an institutional theory framework to look at how MNCs operating in India are incorporating the Sustainable Development Goals (SDGs). Applying the tenets of DiMaggio and Powell’s (1983) work, we understand that an MNC subsidiary located in India will be influenced by both potentially contradictory pulls from institutional factors in the local environment and international isomorphic processes. With a qualitative approach and primary data collected via in-depth interviews with 52 HR professionals (including host and subsidiaries) from 8 MNCs, our findings indicate that the subsidiaries based in India are mostly adopting the pattern exhibited by larger organizations in their environment that are viewed as successful (mimetic isomorphism) in implementing the SDGs. There is still a huge scope for improvement in these subsidiaries in terms of investing in people’s capabilities for future sustainable work. The research contributes to the bourgeoning literature on HRM practices and how MNC subsidiaries operate in different contexts and cultural settings in the IT sector.

Linked Care: A Harmonized Workflow for Medication Ordering, Prescription and Dispensing in Mobile Care Settings.

The growing demand for care amid changing demographics poses significant challenges exacerbated by decreasing healthcare professional availability. In Austria, the Linked Care project aims to address these challenges by developing an intersectoral, harmonized IT-supported workflow for medication ordering, prescription, and dispense in mobile care settings. A human centered design approach, with user-focused interviews and workshops was used to identify requirements and analyze the workflows. Activity diagrams were used represent workflows. The resulting harmonized workflow, developed through iterative collaboration with care organizations, integrates the LC platform into existing care software. To test and demonstrate the harmonized workflow, mockups were created and evaluated for usability, resulting in positive feedback and suggestions for enhancements. Current workflows revealed media breaches and inefficiencies, which the proposed harmonized workflow seeks to address. The paper concludes with implications for future developments, including the subsequent adoption of a HL7 FHIR Implementation Guide for Austria, based on the defined harmonized workflow, to streamline intersectoral communication and improve efficiency in mobile care settings.

A human centered design approach to define and measure documentation quality using an EHR virtual simulation.

Electronic health record (EHR) documentation serves multiple functions, including recording patient health status, enabling interprofessional communication, supporting billing, and providing data to support the quality infrastructure of a Learning Healthcare System. There is no definition and standardized method to assess documentation quality in EHRs. Using a human-centered design (HCD) approach, we define and describe a method to measure documentation quality. Documentation quality was defined as timely, accurate, user-centered, and efficient. Measurement of quality used a virtual simulated standardized patient visit via an EHR vendor platform. By observing and recording documentation efforts, nurse practitioners (NPs) (N = 12) documented the delivery of an Age-Friendly Health System (AFHS) 4Ms (what Matters, Medication, Mentation, and Mobility) clinic visit using a standardized case. Results for timely documentation indicated considerable variability in completion times of documenting the 4Ms. Accuracy varied, as there were many types of episodes of erroneous documentation and extra time in seconds in documenting the 4Ms. The type and frequency of erroneous documentation efforts were related to navigation burden when navigating to different documentation tabs. The evaluated system demonstrated poor usability, with most participants scoring between 60 and 70 on the System Usability Scale (SUS). Efficiency, measured as click burden (the number of clicks used to navigate through a software system), revealed significant variability in the number of clicks required, with the NPs averaging approximately 13 clicks above the minimum requirement. The HCD methodology used in this study to assess the documentation quality proved feasible and provided valuable information on the quality of documentation. By assessing the quality of documentation, the gathered data can be leveraged to enhance documentation, optimize user experience, and elevate the quality of data within a Learning Healthcare System.

A new direction for neighbourhood governance and community construction in China: the case of Zhejiang province’s ‘Future Communities’

ABSTRACT In March 2019, Zhejiang province introduced a new approach to China’s neighbourhood governance, placing digital technology at the centre of communities. Utilizing a case study design, we examine this approach and the role of technology in cultivating community autonomy, public participation, and local social interaction. The findings show how sociospatial dynamics guide the orchestration and reception of smart innovations for community engagement and empowerment, indicating what works and for whom. The empirical work provides a basis for future recommendations, signalling the limitations of digital innovation within communal life and reinforcing a human-centred approach to developing smart systems of community development.

An ethical framework for human-robot collaboration for the future people-centric manufacturing: A collaborative endeavour with European subject-matter experts in ethics

Envisioning humans and (smart) robots collaboratively working on the manufacturing shop floor, sharing spaces, tasks and objectives, reflects the ambitious goal that the ideal factory of the future aspires to attain. However, ensuring the effective implementation of this novel form of labour organisation remains an ongoing area of research. Key aspects such as the future role of workers, potential psychological risks, and the overall ethical considerations of human-robot (H-R) collaboration warrant further investigation until the underpinning safety challenges have been addressed. This study presents a novel ethical framework for H-R collaboration in manufacturing, which involved 30 subject-matter experts in ethics within the European context in a collaborative design process conducted through a year-long three-round data collection qualitative Delphi study. The ethical framework adopts a human-centric approach, recognising the influences that expand beyond the specific context of H-R dynamics on the shop floor, towards organisational and societal governance for a more responsible integration of (smart) robotics into the professional settings. Ethics, in this regard, aims to foster ethical awareness and accountability in the processes and practices of design and innovation, involving all stakeholders who play a role in shaping the future of Industry 5.0.

Smart All-Hazard Responses Framework (SARF): Human Factors and Ergonomics Approach

The integration of Human Factors and Ergonomics (HFE) into emergency responses is crucial due to frequent human errors and inadequate human-system interactions which impede effective emergency and disaster management. Therefore, this research aims to develop and validate the Smart All-hazards Response Framework (SARF) that systematically incorporates HFE principles across all aspects of disaster management, from planning through post-response analysis. The framework focuses on reducing human errors, enhancing human-system interactions, and boosting system resilience. The research methodology includes a detailed literature review, the development of the HFE-integrated framework, and rigorous testing through experimental studies and simulations across various scenarios. Key questions address the integration of HFE to optimize system performance and the impact of ergonomic interventions on responder safety in high-risk environments. By advancing a holistic, human-centered approach to emergency and disaster management, this study aims to significantly enhance the effectiveness, safety, and adaptability of responses to all kinds of hazards.

Judgmental adjustment of demand forecasting models using social media data and sentiment analysis within industry 5.0 ecosystems

Industry 5.0 ecosystems focus on a human-centric approach to operations and supply chain management by integrating stakeholders, advanced technologies, and processes. While incorporating social media (SM) information into demand forecasting can significantly improve accuracy, it also brings about several challenges. This paper proposes an approach to leverage Big Data originating from SM networks combined with human judgment to build demand forecasts for new products. The structured methodology is demonstrated to improve forecast accuracy in a real case of a F&B company while providing several insights into the challenges and opportunities of integrating advanced information technology into the demand forecasting process. The main challenges include effectively categorising the impact factors of SM on demand forecasting, translating insights from SM into actionable decisions, and ensuring the accuracy and reliability of the data obtained from SM networks. Future studies should involve collaborative expert input and validating the approach across various companies and industries.

Building a digital supportive supervision system for improving health service delivery in Low- and Middle-Income countries through the collaborative requirements development methodology (CRDM): Experience from Tanzania

Background Supportive supervision is pivotal for both health service providers and managers to improve the quality of services and health outcomes. Tanzania's digital supportive supervision system is called Afya Supportive Supervision System (AfyaSS ®). The latter was designed and developed using a human-centered approach called Collaborative Requirements Development Methodology (CRDM). This paper describes the experiences of building the digital supportive supervision system through CRDM in Tanzania, a transition from the paper-based supportive supervision system. Methods In 2018, with the support of PATH, the government of Tanzania adopted a participatory human-centered design by carrying out desk reviews of existing guidelines and tools, field visits, stakeholder workshops, and user advisory groups to gather information for developing a digital supportive supervision system. The gathered information was analyzed with the lens of identifying the common challenges and system requirements. Results AfyaSS was successfully developed using CRDM and deployed in all regions across the country. It has consolidated multiple checklists for distinct health domains, and dashboard functionalities to track progress toward health system indicators, objectives, and action plans. As part of the deployment, several resources were developed to aid in the deployment process, such as a comprehensive user manual, facilitator's guide, training slides, and video tutorials. Health workers and managers can be empowered and motivated to implement comprehensive and coherent supportive supervision by using the lessons learned from this digitalization process to transform the current supervision processes to improve the quality of care by offering instruments that promote evidence-based actions. Conclusion A human-centered approach has been shown to be useful in developing digital tools for use in Tanzania’s health system. Tanzania's lessons can be applied in other low- and middle- income countries (LMICs) with similar contexts when considering implementing digital health interventions. While using the human-centered approach, it is crucial to establish a system development roadmap, conduct appropriate training, provide sustained information and communication technology, and management support for unforeseen issues, and ensure ongoing maintenance.

Large-Scale Assessments for Learning: A Human-Centred AI Approach to Contextualizing Test Performance

Large-scale assessments play a key role in education: educators and stakeholders need to know what students know and can do, so that they can be prepared for education policies and interventions in teaching and learning. However, a score from the assessment may not be enough—educators need to know why students got low scores, how students engaged with the tasks and the assessment, and how students with different levels of skills worked through the assessment. Process data, combined with response data, reflect students’ test-taking processes and can provide educators such rich information, but manually labelling the complex data is hard to scale for large-scale assessments. From scratch, we leveraged machine learning techniques (including supervised, unsupervised, and active learning) and experimented with a general human-centred AI approach to help subject matter experts efficiently and effectively make sense of big data (including students’ interaction sequences with the digital assessment platform, such as response, timing, and tool use sequences) to provide process profiles, that is, a holistic view of students’ entire test-taking processes on the assessment, so that performance can be viewed in context. Process profiles may help identify different sources for low performance and help generate rich feedback to educators and policy makers. The released National Assessment of Educational Progress (NAEP) Grade 8 mathematics data were used to illustrate our proposed approach.

Analyzing Human-Centric Wireless Energy Harvesting for Sustainable and Resilient Energy Systems

Discussions around wireless communications and energy harvesting in the literature are elitist and techno-centric. There is a need for more studies on a human-centric approach that simplifies the concept and links energy harvesting with human empowerment and well-being. Also, studies so far on the implementation of wireless energy harvesting in rural settlements have been scanty despite the important role wireless communication plays in social interactions and financial transactions within rural communities. The challenge of little awareness of the human sustainability impact of WEH is impeding stakeholders’ advocacy and advancement of wireless energy harvesting (WEH) despite its real and perceived benefits. So far, research efforts as reported in the literature are less than proportionate to the potential of WEH. Apart from providing sustainable energy for wireless devices and networks, its generation of nonelectric renewable energy is a huge plus for ensuring access to affordable, reliable, sustainable, and modern energy for all as contained in the United Nations Sustainable Development Goal 7 (SDG 7). Given the astronomical growth in the use of wireless devices globally, more research studies are needed to create more awareness for massive WEH advocacy. This study uses a model-driven approach to simplify and highlight critical aspects of WEH such as wireless body area network, sources of non-electric renewable energy sources, green energy (environmental protection), intermittent nature of renewable wireless energy sources, and research directions. Also, the application of WEH for the sustenance of lives and livelihood in rural economies is examined using the rural community of Adum-Aiona in Nigeria as a case study. It is expected that the information provided will create more awareness and foster further discussions around the human-centric angle of WEH even as advances in technology continue to be made.

Applied artificial intelligence for global child health: Addressing biases and barriers.

Given the potential benefits of artificial intelligence and machine learning (AI/ML) within healthcare, it is critical to consider how these technologies can be deployed in pediatric research and practice. Currently, healthcare AI/ML has not yet adapted to the specific technical considerations related to pediatric data nor adequately addressed the specific vulnerabilities of children and young people (CYP) in relation to AI. While the greatest burden of disease in CYP is firmly concentrated in lower and middle-income countries (LMICs), existing applied pediatric AI/ML efforts are concentrated in a small number of high-income countries (HICs). In LMICs, use-cases remain primarily in the proof-of-concept stage. This narrative review identifies a number of intersecting challenges that pose barriers to effective AI/ML for CYP globally and explores the shifts needed to make progress across multiple domains. Child-specific technical considerations throughout the AI/ML lifecycle have been largely overlooked thus far, yet these can be critical to model effectiveness. Governance concerns are paramount, with suitable national and international frameworks and guidance required to enable the safe and responsible deployment of advanced technologies impacting the care of CYP and using their data. An ambitious vision for child health demands that the potential benefits of AI/Ml are realized universally through greater international collaboration, capacity building, strong oversight, and ultimately diffusing the AI/ML locus of power to empower researchers and clinicians globally. In order that AI/ML systems that do not exacerbate inequalities in pediatric care, teams researching and developing these technologies in LMICs must ensure that AI/ML research is inclusive of the needs and concerns of CYP and their caregivers. A broad, interdisciplinary, and human-centered approach to AI/ML is essential for developing tools for healthcare workers delivering care, such that the creation and deployment of ML is grounded in local systems, cultures, and clinical practice. Decisions to invest in developing and testing pediatric AI/ML in resource-constrained settings must always be part of a broader evaluation of the overall needs of a healthcare system, considering the critical building blocks underpinning effective, sustainable, and cost-efficient healthcare delivery for CYP.

A human-centred approach to symbiotic AI: Questioning the ethical and conceptual foundation

This paper advocates for a constructivist approach to symbiosis to restore human-centredness in the governance of Symbiotic Artificial Intelligence (SAI). Challenging rigid, deterministic foundational methods warns against the risk of divorcing ethics from mere adherence to moral principles. Instead, it calls for a shift towards a distributed, contextual, relational, and dialectical structure to embody human-centredness. Through an analysis of the SAI landscape and its interplay between social and technological factors, the paper argues for a reconceptualisation of theoretical foundation and human responsibility within the socio-technical perspective. Chapter 2 delves into foundational issues of SAI, questioning the application of biological categories and proposing patterns of SAI based on definitions of intelligent life. Chapter 3 explores the potential of a constructivist approach, emphasising flexibility and context awareness, and presents a framework for understanding and evaluating SAI systems, components of an evolving methodology.

Striking the Balance: Embracing Technology While Upholding Humanistic Principles in Neurorehabilitation

Background The rapid advancement of technology-focused strategies in neurorehabilitation has brought optimism to individuals with neurological disorders, caregivers, and physicians while reshaping medical practice and training. Objectives We critically examine the implications of technology in neurorehabilitation, drawing on discussions from the 2021 and 2024 World Congress for NeuroRehabilitation. While acknowledging the value of technology, it highlights inherent limitations and ethical concerns, particularly regarding the potential overshadowing of humanistic approaches. The integration of technologies such as robotics, artificial intelligence, neuromodulation, and brain-computer interfaces enriches neurorehabilitation by offering interdisciplinary solutions. However, ethical considerations arise regarding the balance between compensation for deficits, accessibility of technologies, and their alignment with fundamental principles of care. Additionally, the pitfalls of relying solely on neuroimaging data are discussed, stressing the necessity for a more comprehensive understanding of individual variability and clinical skills in rehabilitation. Results From a clinical perspective, the article advocates for realistic solutions that prioritize individual needs, quality of life, and social inclusion over technological allure. It underscores the importance of modesty and honesty in responding to expectations while emphasizing the uniqueness of each individual’s experience. Moreover, it argues for the preservation of human-centric approaches alongside technological advancements, recognizing the invaluable role of clinical observation and human interaction in rehabilitation. Conclusion Ultimately, the article calls for a balanced attitude that integrates both scientific and humanistic perspectives in neurorehabilitation. It highlights the symbiotic relationship between the sciences and humanities, advocating for philosophical questioning to guide the ethical implementation of new technologies and foster interdisciplinary dialogue.

Effect of Human Interaction Modalities on the Usability and Performance of a Robotic Cyber-Physical System for Construction Progress Monitoring

The construction industry has been slow to adopt advanced technologies like Cyber-Physical Systems (CPS), underscoring the necessity for a human-centered approach to their development. This study aims to understand the significance and impact of human-CPS interaction modalities on human perceptions of CPS, specifically in the use case of construction progress monitoring. A mixed-method approach comprising quantitative observations and qualitative interviews was employed to evaluate the system’s usability and performance with different human-CPS interaction modalities. The findings reveal variable levels of usability and performance across different human-CPS interaction modalities. Users expressed a preference for the three-dimensional immersive environment compared to a two-dimensional setting due to a better sense of presence. However, they experienced heightened cognitive load when utilizing novel interaction methods such as hand gestures and voice commands. Interestingly, despite this increased cognitive demand, users exhibited comparable performance in the assigned task, irrespective of their familiarity with the interaction modality. The results underscore the importance of human-centered design in CPS development. It contributes empirical evidence crucial for understanding and improving human-CPS interactions, thereby advancing the implementation of CPS in construction progress monitoring.