About this Research Topic
Resilience describes the capacity of a system to withstand shocks through various forms of functional or structural adaptation without losing its capacity to operate. Where it doesn’t arise naturally, it can be designed into existing systems through the introduction of new technology, either in terms of material novelty, by implementing novel practices or by stimulating behavioral change. A primary obstacle to increasing the resilience of human-designed-and-operated systems is the notable effect of uncertainty on decision-making. The conflation of uncertainty, particularly that arising from the lack of information, with risk inhibits adoption of new technologies and ultimately influences prioritization among competing societal goals. Access to information is therefore a key factor in affecting human behavioral changes around resilience practices. AI that can increase the availability and certainty of information should be a magic bullet for affecting behavior, but must overcome a variety of obstacles, including satisfactory performance, accessibility, and integration into tools that promote engagement. Further design challenges reside in the determination of which scale AI systems should address when applied to the solution of societal ills and whether AI technology presents new opportunities for collective action.
This Research Topic invites both theoretical and empirical contributions to help analyze and understand which forms of resilience are desirable, how and to what extent AI influences a system’s resilience, illustrating the specific mechanisms or suggesting new applications where AI could yield an improvement in a system’s resilience. Contributions can be sector and domain-specific (e.g., sustainability management, health care), theoretical and cross-cutting (e.g., network topology analysis), empirical (e.g., the use of AI in mobility systems) or aspirational (e.g., engineering solutions to increase resilience of human interactions). More specifically, the Research Topic seeks to shed light on:
• AI applications to design for or enhance desirable forms of system resilience. This includes design options such as the use of AI for stress tests, forecasts, knowledge hubs, communication facilitators, as well as the extent to which AI tools can improve a system’s resilience (e.g., through time-to-failure analysis);
• Environmental factors influencing the optimal design of AI tools to enhance resilience, such as the size and frequency of shocks a system is exposed to;
• Information feedback loops between AI tools and human behavior, both individually and collectively for instance through social media affecting voting behavior;
• The brittleness of AI tools that affect their own resilience and the extent to which this interacts with a system’s resilience to which the tools are applied;
• Use cases of AI to enhance resilience in human networks through the provision of additional information, forecasting, scenario analysis and stress testing, such as its use for banking regulators and competition authorities.
Papers that analyze and discuss specific use cases are particularly welcome. As this article collection seeks to address policy implications, we welcome innovative considerations of possible policy solutions, including:
• Eco-system approaches for improvements in system resilience such as sustainability management, mobility networks, human social interactions and other broad societal goals as highlighted by the Sustainable Development Goals;
• The design of AI tools to improve their own resilience to failure;
• The use of AI to enhance resilience of policy making, including but not limited to its use in forecasting, stress testing, (strategic) knowledge sharing and communication;
• Use cases of AI in designing for resilience, including the study of a system’s characteristics and its environmental influence;
• Applications of AI in stimulating behavioral shifts of individuals or teams towards stronger cooperation, developing innovative solutions towards novel challenges or exploring and identifying a larger set of available options;
• Submissions that discuss the use of generative artificial intelligence, or other types of AI, for the use of systems-level evaluation, particularly in the context of evaluating the value and impact of systems change and transformation efforts. Uses of AI to assess resilience in the context of social impact measurement and value creation are also welcome.
We welcome original research papers from a broad range of disciplinary perspectives and encourage authors to submit papers using primary data and experiments, including applications of AI tools in human interactions, urban planning, policy interventions, mobility and sustainability management. Studies that offer design principles for AI tools to enhance resilience (“emergent engineering”) are highly welcomed. Authors are encouraged to elaborate on the policy implications of their findings.
This Research Topic invites both theoretical and empirical contributions to help analyze and understand which forms of resilience are desirable, how and to what extent AI influences a system’s resilience, illustrating the specific mechanisms or suggesting new applications where AI could yield an improvement in a system’s resilience. Contributions can be sector and domain-specific (e.g., sustainability management, health care), theoretical and cross-cutting (e.g., network topology analysis), empirical (e.g., the use of AI in mobility systems) or aspirational (e.g., engineering solutions to increase resilience of human interactions). More specifically, the Research Topic seeks to shed light on:
• AI applications to design for or enhance desirable forms of system resilience. This includes design options such as the use of AI for stress tests, forecasts, knowledge hubs, communication facilitators, as well as the extent to which AI tools can improve a system’s resilience (e.g., through time-to-failure analysis);
• Environmental factors influencing the optimal design of AI tools to enhance resilience, such as the size and frequency of shocks a system is exposed to;
• Information feedback loops between AI tools and human behavior, both individually and collectively for instance through social media affecting voting behavior;
• The brittleness of AI tools that affect their own resilience and the extent to which this interacts with a system’s resilience to which the tools are applied;
• Use cases of AI to enhance resilience in human networks through the provision of additional information, forecasting, scenario analysis and stress testing, such as its use for banking regulators and competition authorities.
Papers that analyze and discuss specific use cases are particularly welcome. As this article collection seeks to address policy implications, we welcome innovative considerations of possible policy solutions, including:
• Eco-system approaches for improvements in system resilience such as sustainability management, mobility networks, human social interactions and other broad societal goals as highlighted by the Sustainable Development Goals;
• The design of AI tools to improve their own resilience to failure;
• The use of AI to enhance resilience of policy making, including but not limited to its use in forecasting, stress testing, (strategic) knowledge sharing and communication;
• Use cases of AI in designing for resilience, including the study of a system’s characteristics and its environmental influence;
• Applications of AI in stimulating behavioral shifts of individuals or teams towards stronger cooperation, developing innovative solutions towards novel challenges or exploring and identifying a larger set of available options;
• Submissions that discuss the use of generative artificial intelligence, or other types of AI, for the use of systems-level evaluation, particularly in the context of evaluating the value and impact of systems change and transformation efforts. Uses of AI to assess resilience in the context of social impact measurement and value creation are also welcome.
We welcome original research papers from a broad range of disciplinary perspectives and encourage authors to submit papers using primary data and experiments, including applications of AI tools in human interactions, urban planning, policy interventions, mobility and sustainability management. Studies that offer design principles for AI tools to enhance resilience (“emergent engineering”) are highly welcomed. Authors are encouraged to elaborate on the policy implications of their findings.
Keywords: AI, Resilience, Systems
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
