Defining human-AI teaming the human-centered way: a scoping review and network analysis

Berretta, Sophie; Tausch, Alina; Ontrup, Greta; Gilles, Björn; Peifer, Corinna; Kluge, Annette

doi:10.3389/frai.2023.1250725

Cited by 9 publications

(2 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The interface provides the PSE reporter with multiple probable event types and associated explanations for the ML classifier's prediction. This approach aligns with the principles of level 2 automation, where ML classifiers aids human decision-making rather than fully automating it [49]. This collaboration optimally combines human expertise with ML capabilities, potentially reducing cognitive workload and memorization of the taxonomy while also reducing the risks associated with overreliance on automation.…”

Section: Principal Findingsmentioning

confidence: 81%

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

Chen,

Cohen,

Wilson

et al. 2024

JMIR Hum Factors

View full text Add to dashboard Cite

Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.

show abstract

Section: Principal Findingsmentioning

confidence: 81%

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

Chen,

Cohen,

Wilson

et al. 2024

JMIR Hum Factors

View full text Add to dashboard Cite

show abstract

“…Second, HRM continues to work with AI application developers to make sure integrated AI usage is user-friendly, intuitive, explainable, and responsible. Third, study the human-AI interactive mechanisms that amplify human skills and develop guidelines for human-AI collaboration and integration (e.g., Budhwar et al, 2022 ; Berretta et al, 2023 ; Hu and Wu, 2023 ). These efforts to take a human-centric approach to learning and development can motivate employees to learn how to work with new technologies and be more willing to transform with the organization (e.g., Beichter and Kaiser, 2023 ).…”

Section: How Hrm Can Bring Humans and Machines Closer Together In The...mentioning

confidence: 99%

Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace

Fenwick,

Molnar,

Frangos

2024

Front. Artif. Intell.

View full text Add to dashboard Cite

The functions of human resource management (HRM) have changed radically in the past 20 years due to market and technological forces, becoming more cross-functional and data-driven. In the age of AI, the role of HRM professionals in organizations continues to evolve. Artificial intelligence (AI) is transforming many HRM functions and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. A growing body of evidence highlights the benefits AI brings to the field of HRM. Despite the increased interest in AI-HRM scholarship, focus on human-AI interaction at work and AI-based technologies for HRM is limited and fragmented. Moreover, the lack of human considerations in HRM tech design and deployment can hamper AI digital transformation efforts. This paper provides a contemporary and forward-looking perspective to the strategic and human-centric role HRM plays within organizations as AI becomes more integrated in the workplace. Spanning three distinct phases of AI-HRM integration (technocratic, integrated, and fully-embedded), it examines the technical, human, and ethical challenges at each phase and provides suggestions on how to overcome them using a human-centric approach. Our paper highlights the importance of the evolving role of HRM in the AI-driven organization and provides a roadmap on how to bring humans and machines closer together in the workplace.

show abstract

Navigating the landscape of concept-supported XAI: Challenges, innovations, and future directions

Shams Khoozani,

Sabri,

Seng

et al. 2024

Multimed Tools Appl

View full text Add to dashboard Cite

This comprehensive review of concept-supported interpretation methods in Explainable Artificial Intelligence (XAI) navigates the multifaceted landscape. As machine learning models become more complex, there is a greater need for interpretation methods that deconstruct their decision-making processes. Traditional interpretation techniques frequently emphasise lower-level attributes, resulting in a schism between complex algorithms and human cognition. To bridge this gap, our research focuses on concept-supported XAI, a new line of research in XAI that emphasises higher-level attributes or 'concepts' that are more aligned with end-user understanding and needs. We provide a thorough examination of over twenty-five seminal works, highlighting their respective strengths and weaknesses. A comprehensive list of available concept datasets, as opposed to training datasets, is presented, along with a discussion of sufficiency metrics and the importance of robust evaluation methods. In addition, we identify six key factors that influence the efficacy of concept-supported interpretation: network architecture, network settings, training protocols, concept datasets, the presence of confounding attributes, and standardised evaluation methodology. We also investigate the robustness of these concept-supported methods, emphasising their potential to significantly advance the field by addressing issues like misgeneralization, information overload, trustworthiness, effective human-AI communication, and ethical concerns. The paper concludes with an exploration of open challenges such as the development of automatic concept discovery methods, strategies for expert-AI integration, optimising primary and concept model settings, managing confounding attributes, and designing efficient evaluation processes.

show abstract

Defining human-AI teaming the human-centered way: a scoping review and network analysis

Cited by 9 publications

References 102 publications

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

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

Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace

Navigating the landscape of concept-supported XAI: Challenges, innovations, and future directions

Contact Info

Product

Resources

About