## 5 Prospects and Challenges
### Challenges Ahead  
Despite the (potentially) revolutionary impact of autonomous recovery robots on post-shock emergency responses, it is crucial to acknowledge the significant challenges thatlie ahead, potentially casting a shadow over their development and deployment. Overall, these challenges include (but are not limited to) the following:  
1. The safety of deployment. Machine learning models, particularly deep neural networks, have showcased remarkable performance in controlled benchmarks; however, their safety and trustworthiness in real-world applications raise valid concerns, especially in safety-critical scenarios like post-hazard emergency responses [6]. The crux of the issue lies in the fact that minimizing a statistically motivated loss function during training does not guarantee optimal real-world performance. When these models encounter data during in situ deployment--data that significantly diverge from their training and testing datasets--their robustness against distribution shifts becomes compromised [138], exacerbated by the scarcity of hazard-related datasets. Furthermore, uncertainty compounds the challenge of ensuring model safety [139]. Inaccurate or false predictions can have severe unintended consequences during real-world hazard events. For instance, mischaracterizing the damage status of CISs using CNN-based image recognition may lead to significant disaster mismanagement; similarly, the uncertainty and dynamics surrounding the functionality of CISs during such periods can amplify the impact of unsafe actions of autonomous agents. These actions may trigger cascading failures whose consequences could, paradoxically, go substantially beyond the initial hazard [64,65]. Against this backdrop, in-depth research should be undertaken to institutionalize the certification of autonomous recovery robots [138]. It is also vital to develop a more interpretable and scalable DRL framework [140] to enable these robots to shun unsafe actions while still fulfilling the optimization objectives of post-shock emergency response campaigns [141,142];
2. Ethical decision-making. As AI penetrates modern societies, ethical questions arise regarding how AI-capable machines navigate complex decisions; for instance, autonomous vehicles, faced with unavoidable harm during crashes, must grapple with the difficult choice between running over pedestrians or sacrificing themselves and their passengers [143,144,145]. This principle of the \"greater good\" becomes even more intricate in the context of post-hazard emergency responses, or early warnings [146,147]. Recovery robots, deployed after disasters, face a delicate balancing act. Functionality gaps between supply and demand in hazard-impacted CISs, coupled with limited restoration resources, lead to critical decisions. These robots must prioritize certain neighbourhoods while \"sacrificing\" others [57]. Although well-intentioned, such decisions can evoke anxiety and discontent among local inhabitants, exacerbating societal inequality and sapping the long-term resilience of urban communities affected by hazards [148,149,150,151,152]. It is thus crucial for private and public sectors to collaborate to address such a thorny challenge by proposing an inclusive regulatory framework to guide recovery robots to select a strategy that reconciles widely recognized moral values with the self-interest of affected neighbourhoods.