Human-centered AI Research Articles

Usability in human-robot collaborative workspaces

AbstractThis study explores the usability of human-robot collaboration in the previously under-researched field of forestry and agroforestry. The robotic platforms used were Boston Dynamics Spot and the Agile X Bunker, the latter equipped with a movable arm. The research was conducted in an experimental robotic test park, simulating real-world scenarios relevant to forestry and agriculture. The focus of this study is on the use of these robots as collaborative robots (cobots). Usability, as a central characteristic in human-computer interaction, was evaluated using the well-established System Usability Scale (SUS). The results demonstrate the potential of these robotic systems to enhance productivity and safety, while also underscoring the importance of user-centered design in the development of collaborative tools. A key finding of this work is that successful integration of AI-driven technologies in sectors such as forestry and agriculture requires a focus on human-centered AI which includes good usability, and accessibility, emphasizing the importance of the concept of universal access.

Reframing Deception for Human-Centered AI

Reframing Deception for Human-Centered AI

Human-Centered AI to Improve Operation Research, Automation, and Decision-Making Processes for HighTech Corporations

In the modern world, AI has spread to almost every field that is incorporated with technology. As the quality of AI systems continues to improve drastically, their usage in companies that rely heavily on technology and media increases almost every day. In this research paper, theresearcher will focus on how human-centered AI affects operations research, automation, and decision-making processes in high-tech companies. This paper will examine and discuss different use cases from world-renowned corporations.

Transitioning to Human‐Centered AI: A Systematic Review of Theories, Scenarios, and Hypotheses in Human‐AI Interactions

ABSTRACTThis study conducted a systematic review of human‐AI interaction (HAI)over the past decade for the implemented theories and scenarios, and the tested hypotheses to discover the changes in the current transition to human‐centered AI (HCAI). Moving from acceptance theories, Computers are social actors (CASA), anthropomorphism, and the integrative trust model are the most frequent theories. Augmentation scenarios of decision‐making, teamwork, and human‐AI collaborations are common in the latest HAI studies. Users' trust, acceptance, and intention to use an AI system are the main research targets in HAI studies. These trends show a clear transition toward HCAI. This paper also discusses opportunities tied to HAI studies based on the interconnections between the various theories, scenarios, and hypotheses.

어린이의 윤리적 가치 확립을 위한 행동 유도성 Human-Centered AI 교육 서비스 - 가상 세계에서의 성범죄를 중심으로

어린이의 윤리적 가치 확립을 위한 행동 유도성 Human-Centered AI 교육 서비스 - 가상 세계에서의 성범죄를 중심으로

Good decisions in an imperfect world: a human-focused approach to automated decision-making

ABSTRACT Legal rules are based on an imagined regulatory scene that contains presumptions about the reality a regulation addresses. Regarding automated decision-making (ADM), these include a belief in the ‘good human decision’ that is mirrored in the cautious approach in the GDPR. Yet the ‘good human decision’ defies psychological insight into human weaknesses in decision-making. Instead, it reflects a general unease about algorithmic decisions. Against this background I explore how algorithms become part of human relationships and whether the use of decision systems causes a conflict with human needs, values and the prevailing socio-legal framework. Inspired by the concept of Human-Centered AI, I then discuss how the law may address the apprehension towards decision systems. I outline a human-focused approach to regulating ADM that focuses on improving the practice of decision-making. The interaction between humans and machines is an essential part of the regulation. It must address socio-legal changes caused by decision systems both to integrate them into the existing value system and adapt the latter to changes brought forth by ADM. A human-focused approach thus connects the benefits of technology with human needs and societal values.

Human-centered AI development in practice—insights from a multidisciplinary approach

Industry 5.0 will focus on human-centered digitalization and put people at the center of manufacturing. To achieve this, it is necessary to consider employees throughout the transformation process. For this purpose, a practical approach to human-centered AI integration based on the Design Science Research method is presented in this article. The main requirements of the approach were evaluated in interviews with ten pilot companies, especially SMEs. The practical procedure was then examined in more detail for three application examples. The results show that there is still a lack of a uniform approach regarding the involvement of employees. Information on AI projects often reach those affected only during implementation. Even though the interdisciplinary approach is understood as a feasible approach for holistic AI development and implementation, there is still a lack of practical guidelines for adopting this multi-perspective approach. This work provides an initial starting point for a practice-oriented approach to the introduction of human-centered AI.Practical Relevance: As a result of exponential digitalization and the associated in-depth use of partially to fully automated technologies such as AI, collaboration between humans and machines is becoming increasingly important. It is essential for companies to work according to human-centered approaches to consider motivation and satisfaction as well as aspects of data protection and personal protection for employees. The necessity for companies to adopt a human-centric and sustainable approach to digitalization will intensify in coming years. The work addresses the needs for operationalization of research findings for companies. Thus, the proposed guidelines can be understood as an approach to practical knowledge transfer from science to industry.

Travel counsellors’ perspectives on AI assistance

Research on AI in the context of travel counselling has mainly focused on increasing the efficiency and level of automation of online travel bookings. However, AI solutions can also be used in the context of offline travel counselling, i.e., face-to-face counselling at a travel agency service desk. In this specific application domain, AI has the potential to change personal interaction between counsellors and clients in different ways. In order to design AI solutions to enrich personal interaction, research on the specific requirements of counsellors, customers, and other relevant stakeholders in the context of AI-assisted offline travel counselling is needed. Human-centered design (HCD) is a promising approach to investigate and incorporate these perspectives on the course of the AI design process. In this paper, we present our approach of applying HCD elements to deepen the understanding of the context of use, to specify user requirements, and to find potential design solutions for a real-world use case of AI assistance in offline travel counselling. In addition, we highlight the key findings and insights regarding travel counsellors’ perspectives and requirements on possible AI adoption, showing that they are primarily open to AI-based innovations, but also have some concerns about the devaluation of the counselling profession. Further, we critically discuss additional design elements that would be needed for human-centered AI design in an ambitious sense.Practical Relevance: The findings are of high practical relevance as they demonstrate how HCD principles can be applied to real-life use cases, which aim to introduce human-AI interaction in work domains. At the same time, our findings provide valuable insights for practitioners into the specific work environment of offline travel counsellors.

Beyond "Taming Electric Scooters": Disentangling Understandings of Micromobility Naturalistic Riding

Electric(e)-scooters have emerged as a popular, ubiquitous, and first/last-mile micromobility transportation option within and across many cities worldwide. With the increasing situation-awareness and on-board computational capability, such intelligent micromobility has become a critical means of understanding the rider's interactions with other traffic constituents (called Rider-to-X Interactions, RXIs), such as pedestrians, cars, and other micromobility vehicles, as well as road environments, including curbs, road infrastructures, and traffic signs. How to interpret these complex, dynamic, and context-dependent RXIs, particularly for the rider-centric understandings across different data modalities --- such as visual, behavioral, and textual data --- is essential for enabling safer and more comfortable micromobility riding experience and the greater good of urban transportation networks. Under a naturalistic riding setting (i.e., without any unnatural constraint on rider's decision-making and maneuvering), we have designed, implemented, and evaluated a pilot Cross-modality E-scooter Naturalistic Riding Understanding System, namely CENRUS, from a human-centered AI perspective. We have conducted an extensive study with CENRUS in sensing, analyzing, and understanding the behavioral, visual, and textual annotation data of RXIs during naturalistic riding. We have also designed a novel, efficient, and usable disentanglement mechanism to conceptualize and understand the e-scooter naturalistic riding processes, and conducted extensive human-centered AI model studies. We have performed multiple downstream tasks enabled by the core model within CENRUS to derive the human-centered AI understandings and insights of complex RXIs, showcasing such downstream tasks as efficient information retrieval and scene understanding. CENRUS can serve as a foundational system for safe and easy-to-use micromobility rider assistance as well as accountable use of micromobility vehicles.

Participatory Visual Methods and Artificial Intelligence-Driven Analysis for Sustainable Consumption Insights

Sustainable consumption is crucial for mitigating global sustainability challenges. Understanding consumer behaviors and motivations, particularly in developing regions, is essential for designing effective interventions. This study pioneers an innovative methodology integrating participatory visual methods (photovoice) and artificial intelligence analysis to investigate food waste perceptions in an emerging economy context. Twenty-six university students participated in the study, documenting their lived experiences and perspectives on household food waste through photographs and narratives. The key results included 32% of participants expressing shock at the extent of food waste in their daily lives, while 28% showed relative indifference. AI-powered (Artificial Intelligence) computer vision and natural language processing were used to efficiently analyze the large visual and textual dataset. The mixed methods approach generated nuanced, situated insights into consumer attitudes, behaviors, and socio-cultural drivers of wastage. The key themes included low waste consciousness, aesthetic and convenience motivations, social norms, and infrastructural limitations. The participatory process proved effective for raising critical consciousness and uncovering consumption practice dynamics. AI analysis enabled rapid knowledge discovery from the qualitative data while mitigating researcher bias. This innovative integration of participatory methodologies and computational analytics advances sustainable consumption research by empowering marginalized voices and generating contextual insights from unstructured data. With further development, such human-centered AI approaches can transform the study and governance of sustainable consumption.

What you say vs what you do: Utilizing positive emotional expressions to relay AI teammate intent within human–AI teams

With the expansive growth of AI’s capabilities in recent years, researchers have been tasked with developing and improving human-centered AI collaborations, necessitating the creation of human–AI teams (HATs). However, the differences in communication styles between humans and AI often prevent human teammates from fully understanding the intent and needs of AI teammates. One core difference is that humans naturally leverage a positive emotional tone during communication to convey their confidence or lack thereof to convey doubt in their ability to complete a task. Yet, this communication strategy must be explicitly designed in order for an AI teammate to be human-centered. In this mixed-methods study, 45 participants completed a study examining how human teammates interpret the behaviors of their AI teammates when they express different positive emotions via specific words/phrases. Quantitative results show that, based on corresponding behaviors, AI teammates were able to use displays of emotion to increase trust in AI teammates and the positive mood of the human teammate. Additionally, our qualitative findings indicate that participants preferred their AI teammates to increase the intensity of their displayed emotions to help reduce the perceived risk of their AI teammate’s behavior. When taken in sum, these findings describe the relevance of AI teammates expressing intensities of emotion while performing various behavioral decisions as a continued means to provide social support to the wider team and better task performance.

Ethical Considerations in Human-Centered AI: Advancing Oncology Chatbots through Large Language Models.

The integration of chatbots in oncology underscores the pressing need for human-centered AI that addresses patient and family concerns with empathy and precision. Human-centered AI emphasizes ethical principles, empathy, and user-centric approaches, ensuring technology aligns with human values and needs. This review critically examines the ethical implications of employing Large Language Models (LLMs) like GPT-3 and GPT-4 in oncology chatbots. It examines how these models replicate human-like language patterns, impacting the design of ethical AI systems. The paper identifies key strategies for ethically developing oncology chatbots, focusing on potential biases arising from extensive datasets and neural networks. Specific datasets, such as those sourced from predominantly Western medical literature and patient interactions, may introduce biases by over-representing certain demographic groups. Moreover, the training methodologies of LLMs, including fine-tuning processes, can exacerbate these biases, leading to outputs that may disproportionately favor affluent or Western populations while neglecting marginalized communities. By providing examples of biased outputs in oncology chatbots, the review highlights the ethical challenges LLMs present and the need for mitigation strategies. The study emphasizes integrating human-centric values into AI to mitigate these biases, ultimately advocating for the development of oncology chatbots that are aligned with ethical principles and capable of serving diverse patient populations equitably.

The European commitment to human-centered technology: the integral role of HCI in the EU AI Act’s success

Abstract The evolution of AI is set to profoundly reshape the future. The European Union, recognizing this impending prominence, has enacted the AI Act, regulating market access for AI-based systems. A salient feature of the Act is to guard democratic and humanistic values by focusing regulation on transparency, explainability, and the human ability to understand and control AI systems. Hereby, the EU AI Act does not merely specify technological requirements for AI systems. The EU issues a democratic call for human-centered AI systems and, in turn, an interdisciplinary research agenda for human-centered innovation in AI development. Without robust methods to assess AI systems and their effect on individuals and society, the EU AI Act may lead to repeating the mistakes of the General Data Protection Regulation of the EU and to rushed, chaotic, ad-hoc, and ambiguous implementation, causing more confusion than lending guidance. Moreover, determined research activities in Human-AI interaction will be pivotal for both regulatory compliance and the advancement of AI in a manner that is both ethical and effective. Such an approach will ensure that AI development aligns with human values and needs, fostering a technology landscape that is innovative, responsible, and an integral part of our society.

Language-based machine perception: linguistic perspectives on the compilation of captioning datasets

Abstract Over the last decade, a plethora of training datasets have been compiled for use in language-based machine perception and in human-centered AI, alongside research regarding their compilation methods. From a primarily linguistic perspective, we add to these studies in two ways. First, we provide an overview of sixty-six training datasets used in automatic image, video, and audio captioning, examining their compilation methods with a metadata analysis. Second, we delve into the annotation process of crowdsourced datasets with an interest in understanding the linguistic factors that affect the form and content of the captions, such as contextualization and perspectivation. With a qualitative content analysis, we examine annotator instructions with a selection of eleven datasets. Drawing from various theoretical frameworks that help assess the effectiveness of the instructions, we discuss the visual and textual presentation of the instructions, as well as the perspective-guidance that is an essential part of the language instructions. While our analysis indicates that some standards in the formulation of instructions seem to have formed in the field, we also identified various reoccurring issues potentially hindering readability and comprehensibility of the instructions, and therefore, caption quality. To enhance readability, we emphasize the importance of text structure, organization of the information, consistent use of typographical cues, and clarity of language use. Last, engaging with previous research, we assess the compilation of both web-sourced and crowdsourced captioning datasets from various perspectives, discussing factors affecting the diversity of the datasets.

Towards a Human-Centered Innovation in Digital Technologies and Artificial Intelligence: The Contributions of the Pontificate of Pope Francis

ABSTRACT This paper investigates the contributions of Pope Francis toward human-centered AI and digital innovation. It draws from his numerous dialogues with experts in technology, medicine, science, ethics, law, philosophy, and theology. The papal engagements are based on the rich patrimony of Catholic Social Teaching that is being updated in the wake of the digital revolutions. The paper explores the Magisterium of Francis with the question of technological progress and inherent dangers for human society. It underscores some fundamental anthropological and ethical themes that the Pontiff considered necessary in making emerging technologies beneficial.

Fostering social-emotional learning through human-centered use of generative AI in business research education: an insider case study

PurposeThis exploratory study innovates the pedagogy of undergraduate business research courses by integrating Generative Artificial Intelligence (GAI) tools, guided by human-centered artificial intelligence, social-emotional learning, and authenticity principles.Design/methodology/approachAn insider case study approach was employed to examine an undergraduate business research course where 72 students utilized GAI for coursework. Thematic analysis was applied to their meta-reflective journals.FindingsStudents leverage GAI tools as brainstorming partners, co-writers, and co-readers, enhancing research efficiency and comprehension. They exhibit authenticity and human-centered AI principles in their GAI engagement. GAI integration imparts relevant AI skills to students.Research limitations/implicationsFuture research could explore how teams collectively interact with GAI tools.Practical implicationsIncorporating meta-reflections can promote responsible GAI usage and develop students' self-awareness, critical thinking, and ethical engagement.Social implicationsOpen discussions about social perceptions and emotional responses surrounding GAI use are necessary. Educators can foster a learning environment that nurtures students' holistic development, preparing them for technological challenges while preserving human learning and growth.Originality/valueThis study fills a gap in exploring the delivery and outcomes of AI-integrated undergraduate education, prioritizing student perspectives over the prevalent focus on educators' viewpoints. Additionally, it examines the teaching and application of AI for undergraduate research, diverging from current studies that primarily focus on research applications for academics.

AI-based automated speech therapy tools for persons with speech sound disorder: a systematic literature review

ABSTRACT In response to the limited availability of speech therapy services, researchers are developing AI-based automated speech therapy tools for individuals with speech sound disorder (SSD). However, their effectiveness/efficacy compared to conventional speech therapy remains unclear, and no guidelines exist for designing these tools or their required automation levels compared to therapy by speech-language pathologists (SLPs). Moreover, AI applications raise concerns about job displacement, biased algorithms, and privacy issues. This systematic review aims to provide comprehensive insights into AI-based automated speech therapy, focusing on (i) types of SSD addressed; (ii) AI techniques used; (iii) autonomy levels achieved; (iv) delivery modes; and (v) effectiveness/efficacy of these tools. PRISMA guidelines were applied across five databases for studies published between January 2007 and February 2022. Twenty-four studies that met the inclusion and exclusion criteria were included. Results suggest that articulation disorders are the most frequently addressed SSD. Various AI techniques were applied, ranging from traditional automatic speech recognition (ASR) to advanced methods. Most studies proposed fully automated tools, often overlooking the role of other stakeholders. Computer-based and gamified applications were the most common intervention modes. The results suggest the potential of AI-based automated speech therapy tools for individuals with SSD; however, only a few studies have compared their effectiveness/efficacy with conventional speech therapy. Further research is needed to develop speech corpora for under-represented languages, apply a Human-Centered AI approach, conduct usability studies on intervention modes, and perform more rigorous effectiveness studies.

From explainable to interactive AI: A literature review on current trends in human-AI interaction

AI systems are increasingly being adopted across various domains and application areas. With this surge, there is a growing research focus and societal concern for actively involving humans in developing, operating, and adopting these systems. Despite this concern, most existing literature on AI and Human–Computer Interaction (HCI) primarily focuses on explaining how AI systems operate and, at times, allowing users to contest AI decisions. Existing studies often overlook more impactful forms of user interaction with AI systems, such as giving users agency beyond contestability and enabling them to adapt and even co-design the AI’s internal mechanics. In this survey, we aim to bridge this gap by reviewing the state-of-the-art in Human-Centered AI literature, the domain where AI and HCI studies converge, extending past Explainable and Contestable AI, delving into the Interactive AI and beyond. Our analysis contributes to shaping the trajectory of future Interactive AI design and advocates for a more user-centric approach that provides users with greater agency, fostering not only their understanding of AI’s workings but also their active engagement in its development and evolution.

Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis.

Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment. This work focuses on the decision making of sepsis, an acute life-threatening systematic infection that requires an early diagnosis with high uncertainty from the clinician. Our aim is to explore the design requirements for AI systems that can support clinical experts in making better decisions for the early diagnosis of sepsis. The study begins with a formative study investigating why clinical experts abandon an existing AI-powered Sepsis predictive module in their electrical health record (EHR) system. We argue that a human-centered AI system needs to support human experts in the intermediate stages of a medical decision-making process (e.g., generating hypotheses or gathering data), instead of focusing only on the final decision. Therefore, we build SepsisLab based on a state-of-the-art AI algorithm and extend it to predict the future projection of sepsis development, visualize the prediction uncertainty, and propose actionable suggestions (i.e., which additional laboratory tests can be collected) to reduce such uncertainty. Through heuristic evaluation with six clinicians using our prototype system, we demonstrate that SepsisLab enables a promising human-AI collaboration paradigm for the future of AI-assisted sepsis diagnosis and other high-stakes medical decision making.

A Human-Centered AI Framework for Efficient Labelling of ECGs From Drug Safety Trials.

Drug safety trials require substantial ECG labelling like, in thorough QT studies, measurements of the QT interval, whose prolongation is a biomarker of proarrhythmic risk. The traditional method of manually measuring the QT interval is time-consuming and error-prone. Studies have demonstrated the potential of deep learning (DL)-based methods to automate this task but expert validation of these computerized measurements remains of paramount importance, particularly for abnormal ECG recordings. In this paper, we propose a highly automated framework that combines such a DL-based QT estimator with human expertise. The framework consists of 3 key components: (1) automated QT measurement with uncertainty quantification (2) expert review of a few DL-based measurements, mostly those with high model uncertainty and (3) recalibration of the unreviewed measurements based on the expert-validated data. We assess its effectiveness on 3 drug safety trials and show that it can significantly reduce effort required for ECG labelling-in our experiments only 10% of the data were reviewed per trial-while maintaining high levels of QT accuracy. Our study thus demonstrates the possibility of productive human-machine collaboration in ECG analysis without any compromise on the reliability of subsequent clinical interpretations.