Editorial: Augmented Intelligence for Enabling Knowledge-Driven Decision Making
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Abstract
More flexible and cooperative decision-making processes are required as a result of society's digital transformation. The role of augmented intelligence, that is the synergistic fusion of artificial intelligence and human judgment in facilitating knowledge-driven strategies across various domains is examined in this thematic issue. The integration of business intelligence and software engineering, which forms the foundation for creating intelligent, scalable, and explicable systems, is essential to this investigation. The six chosen papers in this issue show how machine learning techniques can be used to mine and model both structured (such as health records indicators) and unstructured (such as product reviews, e-sports discourse, and social media text from X) data. Applications in political sentiment analysis, geopolitical opinion monitoring, risk communication related to weather, e-commerce consumer feedback, gaming community analytics, and mapping malnutrition for public health intervention are all covered in these papers. From explainability and interface design to data preprocessing and model deployment, software engineering is essential to coordinating these intelligent pipelines and guaranteeing that AI outputs are not only accurate but also practically sound. The pieces in this issue collectively demonstrate how Augmented Intelligence can transform decision-making in a rapidly changing digital society when enabled by domain-aware data pipelines and structured engineering frameworks.
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