Human-AI Collaboration Research Articles

Navigating the Ethical Landscape: Implementing Machine Learning in Smart Healthcare Informatics

The integration of Machine Learning (ML) into healthcare informatics holds immense promise, revolutionizing patient care and treatment strategies. However, as this technology advances, it brings forth ethical challenges crucial for careful navigation. ML offers unprecedented abilities to analyze vast healthcare data, leading to personalized medicine and improved outcomes. Yet, ethical concerns emerge, notably in privacy protection, algorithm bias, transparency, informed consent, and data quality. Transparency, explainability, and patient autonomy in decision-making processes are crucial to foster trust and accountability. Striking a balance between innovation and compliance, ensuring data quality, and promoting human-AI collaboration are essential. Addressing these challenges demands adherence to ethical frameworks, continuous monitoring, multidisciplinary governance, education, and regulatory compliance. To fully harness ML's potential in healthcare while upholding ethical standards, collaboration among stakeholders is imperative, ensuring patient welfare remains central amid technological advancements. Ethical considerations must be embedded at every stage of ML implementation to maintain an ethical, equitable, and patient-centered healthcare system.

Human-AI collaboration patterns in AI-assisted academic writing

ABSTRACT Artificial Intelligence (AI) has increasingly influenced higher education, notably in academic writing where AI-powered assisting tools offer both opportunities and challenges. Recently, the rapid growth of generative AI (GAI) has brought its impacts into sharper focus, yet the dynamics of its utilisation in academic writing remain largely unexplored. This paper focuses on examining the nature of human-AI interactions in academic writing, specifically investigating the strategies doctoral students employ when collaborating with a GAI-powered assisting tool. This study involves 626 recorded activities on how ten doctoral students interact with GAI-powered assisting tool during academic writing. AI-driven learning analytics approach was adopted for three layered analyses: (1) data pre-processing and analysis with quantitative content analysis, (2) sequence analysis with Hidden Markov Model (HMM) and hierarchical sequence clustering, and (3) pattern analysis with process mining. Findings indicate that doctoral students engaging in iterative, highly interactive processes with the GAI-powered assisting tool generally achieve better performance in the writing task. In contrast, those who use GAI merely as a supplementary information source, maintaining a linear writing approach, tend to get lower writing performance. This study points to the need for further investigations into human-AI collaboration in learning in higher education, with implications for tailored educational strategies and solutions.

Humans Supervising Artificial Intelligence – Investigation of Designs to Optimize Error Detection

ABSTRACT Artificial Intelligence (AI) fundamentally changes the way we work by introducing new capabilities. Human tasks shift towards a supervising role where the human confirms or disconfirms the presented decision. In this study, we utilise the signal detection theory to investigate and explain how the performance of human error detection is influenced by specific information design. We conducted two online experiments in the context of AI-supported information extraction and measured the ability of participants to validate the extracted information. In the first experiment, we investigated the mechanism of information provided prior to conducting the error detection task. In the second experiment, we manipulated the design of the presented information during the task and investigated its effect. Both manipulations significantly impacted the error detection performance of humans. Hence our study provides important insights for developing AI-based decision support systems and contributes to the theoretical understanding of human-AI collaboration.

Making sense of radiomics: insights on human–AI collaboration in medical interaction from an observational user study

Technologies based on “artificial intelligence” (AI) are transforming every part of our society, including healthcare and medical institutions. An example of this trend is the novel field in oncology and radiology called radiomics, which is the extracting and mining of large-scale quantitative features from medical imaging by machine-learning (ML) algorithms. This paper explores situated work with a radiomics software platform, QuantImage (v2), and interaction around it, in educationally framed hands-on trial sessions where pairs of novice users (physicians and medical radiology technicians) work on a radiomics task consisting of developing a predictive ML model with a co-present tutor. Informed by ethnomethodology and conversation analysis (EM/CA), the results show that learning about radiomics more generally and learning how to use this platform specifically are deeply intertwined. Common-sense knowledge (e.g., about meanings of colors) can interfere with the visual representation standards established in the professional domain. Participants' skills in using the platform and knowledge of radiomics are routinely displayed in the assessment of performance measures of the resulting ML models, in the monitoring of the platform's pace of operation for possible problems, and in the ascribing of independent actions (e.g., related to algorithms) to the platform. The findings are relevant to current discussions about the explainability of AI in medicine as well as issues of machinic agency.

THE IMPACT OF ARTIFICIAL INTELLIGENCE IN DECISION MAKING: A COMPREHENSIVE REVIEW

Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize decision making across various domains. This research article explores the impact of AI in decision making and its implications for individuals, organizations, and society. The article begins by providing an overview of AI and its key components, such as machine learning and natural language processing. It then discusses the role of AI in enhancing decision-making processes by automating tasks, augmenting human capabilities, and providing data-driven insights. The article highlights the benefits of AI in improving decision accuracy, efficiency, and scalability, while also acknowledging the challenges and risks associated with its implementation. These challenges include ethical considerations, biases in AI algorithms, and potential job displacement. The article further explores the importance of transparency, accountability, and interpretability in AI decision-making systems. Additionally, it discusses the role of human-AI collaboration and the need for interdisciplinary approaches to ensure the responsible and ethical deployment of AI in decision making. Drawing on case studies and empirical research, the article provides concrete examples of how AI is transforming decision making in various fields, such as finance, healthcare, and transportation. Finally, the article concludes by discussing future directions and recommendations for policymakers, organizations, and individuals to harness the full potential of AI in decision making while addressing its ethical, social, and economic implications. KEYWORDS: Artificial Intelligence, Decision Making, Machine Learning, Automation, Augmentation, Ethical Considerations, Bias, Transparency, Human-AI Collaboration, Responsible AI.

ADVISE: Accelerating the Creation of Evidence Syntheses for Global Development Using Natural Language Processing-Supported Human-Artificial Intelligence Collaboration

Abstract When designing evidence-based policies and programs, decision-makers must distill key information from a vast and rapidly growing literature base. Identifying relevant literature from raw search results is time and resource intensive, and is often done by manual screening. In this study, we develop an artificial intelligence (AI) agent based on a bidirectional encoder representations from transformers (BERT) model and incorporate it into a human team designing an evidence synthesis product for global development. We explore the effectiveness of the human–AI hybrid team in accelerating the evidence synthesis process. To further improve team efficiency, we enhance the human–AI hybrid team through active learning (AL). Specifically, we explore different sampling strategies, including random sampling, least confidence (LC) sampling, and highest priority (HP) sampling, to study their influence on the collaborative screening process. Results show that incorporating the BERT-based AI agent into the human team can reduce the human screening effort, i.e., the number of documents that humans need to screen, by 68.5% compared to the case of no AI assistance and by 16.8% compared to the industry-standard case of using a frequency-based language model and support vector machine-based classifier for identifying 80% of all relevant documents. When we apply the HP sampling strategy, the human screening effort can be reduced even more: by 78.3% for identifying 80% of all relevant documents compared to no AI assistance. We apply the AL-enhanced human–AI hybrid teaming workflow in the design process of three evidence gap maps for U.S. Agency for International Development and find it to be highly effective. These findings demonstrate how AI can accelerate the development of evidence synthesis products and promote timely evidence-based decision-making in global development.

The AI Ghostwriter Effect: When Users do not Perceive Ownership of AI-Generated Text but Self-Declare as Authors

Human-AI interaction in text production increases complexity in authorship. In two empirical studies (n1 = 30 & n2 = 96), we investigate authorship and ownership in human-AI collaboration for personalized language generation. We show an AI Ghostwriter Effect : Users do not consider themselves the owners and authors of AI-generated text but refrain from publicly declaring AI authorship. Personalization of AI-generated texts did not impact the AI Ghostwriter Effect , and higher levels of participants’ influence on texts increased their sense of ownership. Participants were more likely to attribute ownership to supposedly human ghostwriters than AI ghostwriters, resulting in a higher ownership-authorship discrepancy for human ghostwriters. Rationalizations for authorship in AI ghostwriters and human ghostwriters were similar. We discuss how our findings relate to psychological ownership and human-AI interaction to lay the foundations for adapting authorship frameworks and user interfaces in AI in text-generation tasks.

Advancements in software engineering using AI

The integration of Artificial Intelligence (AI) into the space of software engineering marks a transformative period that reshapes traditional development processes and propels the industry into a new era of innovation. This exploration delves into the multifaceted impact of AI, from its roots in early symbolic AI to the contemporary dominance of machine learning and deep learning. AI’s applications span various domains, but its significance in software engineering lies in its ability to enhance efficiency, improve software quality, and introduce novel approaches to problem-solving. From automating routine tasks to streamlining complex development workflows, AI acts as a virtual collaborator, allowing human developers to focus on higher-order thinking and creativity. This study introduces the application of AI in software engineering. reverse-engineering, and development environments. Moreover, ethical considerations, challenges, and future trends, including explainable AI, reinforcement learning, and human-AI collaboration, are presented.

Differences in student-AI interaction process on a drawing task: Focusing on students’ attitude towards AI and the level of drawing skills

Recent advances and applications of artificial intelligence (AI) have increased the opportunities for students to interact with AI in their learning tasks. Although various fields of scholarly research have investigated human-AI collaboration, the underlying processes of how students collaborate with AI in a student-AI teaming scenario have been scarcely investigated. To develop effective AI applications in education, it is necessary to understand differences in the student-AI interaction (SAI) process depending on students' characteristics. The present study attempts to fill this gap by exploring the differences in the SAI process amongst students with varying drawing proficiencies and attitudes towards AI in performing a public advertisement drawing task. Based on the empirical evidence obtained from the think-aloud protocols of 20 Korean undergraduate students, the study first conducted a lag sequential analysis to identify statistically significant linear patterns of each group and then chronologically incorporated them into the SAI duration via coded activity alignment series to distinguish the overall SAI process of each group. The study revealed the distinctive differences in SAI processes of students with different attitudes towards AI and drawing skills. To better facilitate student-AI teams for learning, a range of implications of educational AI development and instructional design is discussed. Implications for practice or policy: Educational AI should not be limited to performing a specific task and solving well-defined problems. It should be designed with a holistic view of the end-to-end student-AI process, interconnected to different learning activities in the learning process. Educational AI should be capable of increasing students’ metacognition and emotional engagement. An educational AI system architect team inclusive of diverse stakeholders should be formed to collaboratively design the AI system.

C-XAI: A conceptual framework for designing XAI tools that support trust calibration

Recent advancements in machine learning have spurred an increased integration of AI in critical sectors such as healthcare and criminal justice. The ethical and legal concerns surrounding fully autonomous AI highlight the importance of combining human oversight with AI to elevate decision-making quality. However, trust calibration errors in human-AI collaboration, encompassing instances of over-trust or under-trust in AI recommendations, pose challenges to overall performance. Addressing trust calibration in the design process is essential, and eXplainable AI (XAI) emerges as a valuable tool by providing transparent AI explanations. This paper introduces Calibrated-XAI (C-XAI), a participatory design framework specifically crafted to tackle both technical and human factors in the creation of XAI interfaces geared towards trust calibration in Human-AI collaboration. The primary objective of the C-XAI framework is to assist designers of XAI interfaces in minimising trust calibration errors at the design level. This is achieved through the adoption of a participatory design approach, which includes providing templates, guidance, and involving diverse stakeholders in the design process. The efficacy of C-XAI is evaluated through a two-stage evaluation study, demonstrating its potential to aid designers in constructing user interfaces with trust calibration in mind. Through this work, we aspire to offer systematic guidance to practitioners, fostering a responsible approach to eXplainable AI at the user interface level.

A Machine Learning Approach with Human-AI Collaboration for Automated Classification of Patient Safety Event Reports: Algorithm Development and Validation Study.

Adverse events refer to incidents with potential or actual harm to patients in hospitals. These events are typically documented through patient safety event (PSE) reports, which consist of detailed narratives providing contextual information on the occurrences. Accurate classification of PSE reports is crucial for patient safety monitoring. However, this process faces challenges due to inconsistencies in classifications and the sheer volume of reports. Recent advancements in text representation, particularly contextual text representation derived from transformer-based language models, offer a promising solution for more precise PSE report classification. Integrating the machine learning (ML) classifier necessitates a balance between human expertise and artificial intelligence (AI). Central to this integration is the concept of explainability, which is crucial for building trust and ensuring effective human-AI collaboration. This study aims to investigate the efficacy of ML classifiers trained using contextual text representation in automatically classifying PSE reports. Furthermore, the study presents an interface that integrates the ML classifier with the explainability technique to facilitate human-AI collaboration for PSE report classification. This study used a data set of 861 PSE reports from a large academic hospital's maternity units in the Southeastern United States. Various ML classifiers were trained with both static and contextual text representations of PSE reports. The trained ML classifiers were evaluated with multiclass classification metrics and the confusion matrix. The local interpretable model-agnostic explanations (LIME) technique was used to provide the rationale for the ML classifier's predictions. An interface that integrates the ML classifier with the LIME technique was designed for incident reporting systems. The top-performing classifier using contextual representation was able to obtain an accuracy of 75.4% (95/126) compared to an accuracy of 66.7% (84/126) by the top-performing classifier trained using static text representation. A PSE reporting interface has been designed to facilitate human-AI collaboration in PSE report classification. In this design, the ML classifier recommends the top 2 most probable event types, along with the explanations for the prediction, enabling PSE reporters and patient safety analysts to choose the most suitable one. The LIME technique showed that the classifier occasionally relies on arbitrary words for classification, emphasizing the necessity of human oversight. This study demonstrates that training ML classifiers with contextual text representations can significantly enhance the accuracy of PSE report classification. The interface designed in this study lays the foundation for human-AI collaboration in the classification of PSE reports. The insights gained from this research enhance the decision-making process in PSE report classification, enabling hospitals to more efficiently identify potential risks and hazards and enabling patient safety analysts to take timely actions to prevent patient harm.

Examining the potential and pitfalls of ChatGPT in science and engineering problem-solving

The study explores the capabilities of OpenAI's ChatGPT in solving different types of physics problems. ChatGPT (with GPT-4) was queried to solve a total of 40 problems from a college-level engineering physics course. These problems ranged from well-specified problems, where all data required for solving the problem was provided, to under-specified, real-world problems where not all necessary data were given. Our findings show that ChatGPT could successfully solve 62.5% of the well-specified problems, but its accuracy drops to 8.3% for under-specified problems. Analysis of the model's incorrect solutions revealed three distinct failure modes: (1) failure to construct accurate models of the physical world, (2) failure to make reasonable assumptions about missing data, and (3) calculation errors. The study offers implications for how to leverage LLM-augmented instructional materials to enhance STEM education. The insights also contribute to the broader discourse on AI's strengths and limitations, serving both educators aiming to leverage the technology and researchers investigating human-AI collaboration frameworks for problem-solving and decision-making.

Generative Artificial Intelligence [GAI]: Enhancing Future Marketing Strategies with Emotional Intelligence [EI], and Social Skills?

The convergence of Generative Artificial Intelligence (GAI), Emotional Intelligence (EI), and social skills represents a transformative force in the realm of marketing strategy. The purpose of the study is to explore the intricate synergy between GAI, which leverages machine learning and natural language processing, and the deeply human qualities of EI and social skills. It investigates in general how this alliance has the potential to revolutionize future marketing practices by creating more personalized, emotionally resonant, and ethically responsible strategies.The foundation of this research lies in addressing critical questions facing the marketing industry today. First, the need for enhanced personalization. Second, the creation of emotionally intelligent content. Third, ethical considerations and cross-cultural adaptability. Fourth, long-term effectiveness. Fifth, the dynamic interplay between human marketers and AI.Through an interdisciplinary approach, encompassing fields such as AI, psychology, ethics, and marketing, this study examines in general the state of the art in GAI and EI integration, shedding light on how AI systems can be imbued with the capacity to perceive, understand, and respond to human emotions effectively. It delves into the ethical considerations that must underpin the development and deployment of emotionally intelligent marketing strategies, safeguarding consumer privacy and trust.Furthermore, this research investigates in general the cross-cultural adaptability of GAI-driven marketing strategies infused with EI, recognizing the importance of acknowledging cultural nuances in emotional expression and social norms. It evaluates the long-term efficacy of emotionally intelligent marketing, exploring whether such strategies foster enduring customer loyalty and brand affinity.Finally, this study navigates the evolving landscape of human-AI collaboration in marketing, defining the optimal roles of human marketers and AI systems in strategy development and execution.The goal of this research is to provide practical insights, ethical guidelines, and innovative strategies that enable marketers to leverage the complete potential of GAI in tandem with EI and social skills. The study envisions a future where marketing practices create genuine connections with consumers, encourage meaningful interactions, and drive sustainable business growth in a time marked by technological advancement and changing consumer expectations.

Human-AI Collaboration: Understanding User Trust in ChatGPT Conversations

This research paper delves into the critical dimension of Human-AI Collaboration, with a specific focus on unraveling the intricacies of user trust in ChatGPT conversations. In an era marked by increasing AI integration into various aspects of human life, understanding and fostering user trust in conversational AI systems like ChatGPT is essential for effective collaboration. The study employs a comprehensive approach, investigating metrics for trust measurement, analyzing user experiences, and exploring the factors that influence trust. By examining the evolving impact of trust on collaboration and conducting comparative analyses with other conversational AI models, the research aims to provide valuable insights. Ultimately, the paper not only contributes to a nuanced understanding of user trust in ChatGPT conversations but also offers practical recommendations for developers and stakeholders to enhance the collaborative potential of AI systems in real-world applications. Keywords: Human-AI Collaboration, ChatGPT Conversations, Conversational AI, Trust Metrics, User Trust.

Human-AI Collaboration in Healthcare: A Review of User Perspectives and Challenges

Artificial intelligence (AI) has rapidly become a transformative force across various sectors, including healthcare, virtual support, and security. The convergence of AI and Human-Computer Interaction (HCI) has led to the development of interactive intelligent systems, fostering user engagement. This paper presents a comprehensive review of the intersection of AI and HCI, focusing on user perspectives and challenges in the context of healthcare. Keywords: Artificial intelligence, Human-Computer Interaction, Healthcare, User Perspectives, Challenges

Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications

Transparency in AI models is essential for promoting human–AI collaboration and ensuring regulatory compliance. However, interpreting these models is a complex process influenced by various methods and datasets. This study presents a comprehensive overview of foundational interpretation techniques, meticulously referencing the original authors and emphasizing their pivotal contributions. Recognizing the seminal work of these pioneers is imperative for contextualizing the evolutionary trajectory of interpretation in the field of AI. Furthermore, this research offers a retrospective analysis of interpretation techniques, critically evaluating their inherent strengths and limitations. We categorize these techniques into model-based, representation-based, post hoc, and hybrid methods, delving into their diverse applications. Furthermore, we analyze publication trends over time to see how the adoption of advanced computational methods within various categories of interpretation techniques has shaped the development of AI interpretability over time. This analysis highlights a notable preference shift towards data-driven approaches in the field. Moreover, we consider crucial factors such as the suitability of these techniques for generating local or global insights and their compatibility with different data types, including images, text, and tabular data. This structured categorization serves as a guide for practitioners navigating the landscape of interpretation techniques in AI. In summary, this review not only synthesizes various interpretation techniques but also acknowledges the contributions of their original authors. By emphasizing the origins of these techniques, we aim to enhance AI model explainability and underscore the importance of recognizing biases, uncertainties, and limitations inherent in the methods and datasets. This approach promotes the ethical and practical use of interpretation insights, empowering AI practitioners, researchers, and professionals to make informed decisions when selecting techniques for responsible AI implementation in real-world scenarios.

Deep learning in computed tomography pulmonary angiography imaging: A dual-pronged approach for pulmonary embolism detection

The increasing reliance on Computed Tomography Pulmonary Angiography (CTPA) for Pulmonary Embolism (PE) diagnosis presents challenges and a pressing need for improved diagnostic solutions. The primary objective of this study is to leverage deep learning techniques to enhance the Computer Assisted Diagnosis (CAD) of PE. With this aim, we propose a classifier-guided detection approach that effectively leverages the classifier’s probabilistic inference to direct the detection predictions, marking a novel contribution in the domain of automated PE diagnosis. Our classification system includes an Attention-Guided Convolutional Neural Network (AG-CNN) that uses local context by employing an attention mechanism. This approach emulates a human expert's attention by looking at both global appearances and local lesion regions before making a decision. The classifier demonstrates robust performance on the FUMPE dataset, achieving an AUROC of 0.927, sensitivity of 0.862, specificity of 0.879, and an F1-score of 0.805 with the Inception-v3 backbone architecture. Moreover, AG-CNN outperforms the baseline DenseNet-121 model, achieving an 8.1% AUROC gain. While previous research has mostly focused on finding PE in the main arteries, our use of cutting-edge object detection models and ensembling techniques greatly improves the accuracy of detecting small embolisms in the peripheral arteries. Finally, our proposed classifier-guided detection approach further refines the detection metrics, contributing new state-of-the-art to the community: mAP50, sensitivity, and F1-score of 0.846, 0.901, and 0.779, respectively, outperforming the former benchmark with a significant 3.7% improvement in mAP50. Our research aims to elevate PE patient care by integrating AI solutions into clinical workflows, highlighting the potential of human-AI collaboration in medical diagnostics.

Democratizing Knowledge Creation Through Human-AI Collaboration in Academic Peer Review

In the rapidly evolving landscape of academic research, artificial intelligence (AI) is poised to revolutionize traditional academic peer review processes and knowledge evaluation systems. We believe that the growing collaboration between humans and AI will disrupt how academics assess scholarly manuscripts and disseminate published works in a way that facilitates the closing of gaps among diverse scholars as well as competing scholarly traditions. Such human-AI collaboration is not a distant reality but is unfolding before us, in part, through the development, application, and actual use of AI, including language learning models (LLMs). This opinion piece focuses on the academic peer review process. It offers preliminary ideas on how human-AI collaboration will likely change the peer review process, highlights the benefits, identifies possible bottlenecks, and underscores the potential for democratizing academic culture worldwide.

Comprehending AI's Role in Literature and Arts from a Transhumanist Perspective

This research paper embarks on a journey into the transhumanistic intersection between human intelligence and Artificial Intelligence (AI) in the context of producing literature and arts. It argues that human-AI collaboration is a catalytic process for expansion and innovation, with AI emerging as a potential creative partner. This exploration delves into how AI can serve as an extension of human articulation, enhancing artistic capabilities and broadening the horizons of artistic expression. The study underscores the pivotal role of AI in artistic domains by tracing its historical development in creative fields, examining collaborations between human artists and AI systems, and exploring real-world applications to illuminate the potential advantages and challenges of AI-aided creativity. The paper further delves into the ethical and philosophical considerations surrounding authorship, authenticity, and human intervention in AI-generated elements, as well as their societal and cultural implications, offering a glimpse into how AI applications are reshaping contemporary society. Moreover, the research probes into how AI-generated art disrupts traditional aesthetic art forms, reshapes artistic norms, and fosters versatile interpretations. By scrutinizing the potential impacts and risks associated with AI in literature and arts, this research paper aims to shed light on the symbiotic power of AI as an extension of human intelligence in the creative spectrum within the framework of Transhumanism.

AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow.

Large Language Models (LLMs) are powerful but also raise significant security concerns, particularly regarding the harm they can cause, such as generating fake news that manipulates public opinion on social media and providing responses to unethical activities. Traditional red teaming approaches for identifying AI vulnerabilities rely on manual prompt construction and expertise. This paper introduces AdversaFlow, a novel visual analytics system designed to enhance LLM security against adversarial attacks through human-AI collaboration. AdversaFlow involves adversarial training between a target model and a red model, featuring unique multi-level adversarial flow and fluctuation path visualizations. These features provide insights into adversarial dynamics and LLM robustness, enabling experts to identify and mitigate vulnerabilities effectively. We present quantitative evaluations and case studies validating our system's utility and offering insights for future AI security solutions. Our method can enhance LLM security, supporting downstream scenarios like social media regulation by enabling more effective detection, monitoring, and mitigation of harmful content and behaviors.