Search Results (4)

Human–AI collaboration (HAIC) increasingly mediates high-risk decisions in public and private sectors, yet many documented AI harms arise not only from model error but from breakdowns in joint human–AI work: miscalibrated reliance, impaired contestability, misallocated agency, and governance opacity. Conventional explainable AI (XAI) approaches, often delivered as static one-shot artifacts, are poorly matched to these sociotechnical dynamics. This paper is a position paper arguing that explainability should be reframed as a harm-mitigation infrastructure for HAIC: an interactive, iterative capability that supports ongoing sensemaking, safe handoffs of control, governance stakeholder roles and institutional accountability. We introduce co-explainers as a conceptual framework for interactive XAI, in which explanations are co-produced through structured dialogue, feedback, and governance-aware escalation (explain → feedback → update → govern). To ground this position, we synthesize prior harm taxonomies into six HAIC-oriented harm clusters and use them as heuristic design lenses to derive cluster-specific explainability requirements, including uncertainty communication, provenance and logging, contrastive “why/why-not” and counterfactual querying, role-sensitive justification, and recourse-oriented interaction protocols. We emphasize that co-explainers do not “mitigate” sociotechnical harms in isolation; rather, they provide an interface layer that makes harms more detectable, decisions more contestable, and accountability handoffs more operational under realistic constraints such as sealed models, dynamic updates, and value pluralism. We conclude with an agenda for evaluating co-explainers and aligning interactive XAI with governance frameworks in real-world HAIC deployments. Full article

Artificial intelligence (AI) technology, serving as an indispensable component within intelligent construction systems, has become a cornerstone for driving the digital and intelligent transformation of the construction industry. Although AI demonstrates autonomous decision-making capabilities in specific operational contexts, because of the dynamic and often unforeseeable nature of construction workflows, human–AI collaboration (HAIC) still dominates the operational paradigm. This study undertakes a systematic review of the prior research on human–AI collaboration in intelligent construction. Through a bibliometric search, scientometric analysis, and in-depth literature classification, 191 highly cited articles in the past five years, which are in the top 10% by citation count within the dataset (as of May 2025, based on Scopus, Google Scholar, and WOS), were screened, and four research streams were formed based on a co-citation analysis and clustering, namely, construction robotics, productivity and safety, intelligent algorithms and modelling, and factors related to construction workers. Finally, a three-dimensional knowledge framework covering the technical layer, application layer, and management layer was constructed. Through this comprehensive synthesis, the study developed a human–AI collaboration knowledge framework in the field of construction science that integrates technology, scenarios, and management dimensions, revealing the co-evolutionary path of artificial intelligence technology and industry digital transformation. Full article

As AI decision support systems play a growing role in high-stakes decision making, ensuring effective integration of human intuition with AI recommendations is essential. Despite advances in AI explainability, challenges persist in fostering appropriate reliance. This review explores AI decision support systems that enhance human intuition through the analysis of 84 studies addressing three questions: (1) What design strategies enable AI systems to support humans’ intuitive capabilities while maintaining decision-making autonomy? (2) How do AI presentation and interaction approaches influence trust calibration and reliance behaviors in human–AI collaboration? (3) What ethical and practical implications arise from integrating AI decision support systems into high-risk human decision making, particularly regarding trust calibration, skill degradation, and accountability across different domains? Our findings reveal four key design strategies: complementary role architectures that amplify rather than replace human judgment, adaptive user-centered designs tailoring AI support to individual decision-making styles, context-aware task allocation dynamically assigning responsibilities based on situational factors, and autonomous reliance calibration mechanisms empowering users’ control over AI dependence. We identified that visual presentations, interactive features, and uncertainty communication significantly influence trust calibration, with simple visual highlights proving more effective than complex presentation and interactive methods in preventing over-reliance. However, a concerning performance paradox emerges where human–AI combinations often underperform the best individual agent while surpassing human-only performance. The research demonstrates that successful AI integration in high-risk contexts requires domain-specific calibration, integrated sociotechnical design addressing trust calibration and skill preservation simultaneously, and proactive measures to maintain human agency and competencies essential for safety, accountability, and ethical responsibility. Full article

Even though Additive Manufacturing (AM) has become a critical enabler of manufacturing in various industries, its full potential in terms of process quality and productivity has not been achieved yet. The recent developments in Artificial Intelligence (AI) can help toward this goal, especially through Human–AI Collaboration (HAIC). However, existing approaches are focused on certain aspects of the problem, without comprehensively tackling the issue. This study proposes a holistic and AM-specific HAIC framework that combines the different components of human expertise, explainable AI, simulation-based forecasting, and variable-based process control into an integrated decision-making structure. The key findings include the identification of the most important variables that should be utilized, including their classification through the input of experts in terms of importance (utilizing the presented M-S metric), controllability, and the most suitable agent (human, AI, both) to effectively control each variable. Finally, the concept of the framework for effective HAIC in AM is analyzed, including the operational sequence of sensing, AI analysis, human evaluation, decision implementation, and feedback loops. Two complementary case studies are presented; the first provides a conceptual example, and the second one develops a quantitative scenario that allows the comparison of three decision pathways—AI-only, Human-only, and HAIC. Full article