AI Bibliography
AI Bibliography |
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Preece, A., Braines, D., Cerutti, F., & Pham, T. (2019). Explainable ai for intelligence augmentation in multi-domain operations. arXiv preprint arXiv:1910.07563. |
| Resource type: Journal Article BibTeX citation key: Preece2019 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Computer Science, Data Sciences, Decision Theory, Engineering, General, Military Science Subcategories: Augmented cognition, Big data, Decision making, Deep learning, Edge AI, Human decisionmaking, Human factors engineering, JADC2, Machine learning, Mosaic warfare, Psychology of human-AI interaction, Strategy Creators: Braines, Cerutti, Pham, Preece Publisher: Collection: arXiv preprint arXiv:1910.07563 |
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| Abstract |
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Central to the concept of multi-domain operations (MDO) is the utilization of an intelligence, surveillance, and reconnaissance (ISR) network consisting of overlapping systems of remote and autonomous sensors, and human intelligence, distributed among multiple partners. Realising this concept requires advancement in both artificial intelligence (AI) for improved distributed data analytics and intelligence augmentation (IA) for improved human-machine cognition. The contribution of this paper is threefold: (1) we map the coalition situational understanding (CSU) concept to MDO ISR requirements, paying particular attention to the need for assured and explainable AI to allow robust human-machine decision-making where assets are distributed among multiple partners; (2) we present illustrative vignettes for AI and IA in MDO ISR, including human-machine teaming, dense urban terrain analysis, and enhanced asset interoperability; (3) we appraise the state-of-the-art in explainable AI in relation to the vignettes with a focus on human-machine collaboration to achieve more rapid and agile coalition decision-making. The union of these three elements is intended to show the potential value of a CSU approach in the context of MDO ISR, grounded in three distinct use cases, highlighting how the need for explainability in the multi-partner coalition setting is key.
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