Toward Trustworthy and Responsible Autonomous Drones in Future Smart Cities

Autonomous drones are reaching a level of maturity when they can be deployed in cities to support tasks ranging from medicine or food delivery to environmental monitoring. These operations rely on powerful AI models integrated into the drones. Ensuring these models are robust is essential for operating in cities as any errors in the decisions of the autonomous drones can cause damage to the citizens or the urban infrastructure. We contribute a research vision for trustworthy city-scale deployments of autonomous drones. We highlight current key requirements and challenges that have to be fulfilled for achieving city-scale autonomous drone deployments. In addition, we also analyze the complexity of using XAI methods to monitor drone behavior. We demonstrate this by inducing changes in AI model behavior using data poisoning attacks. Our results demonstrate that XAI methods are sensitive enough to detect the possibility of a data attack, but a combination of multiple XAI methods is better to improve the robustness of the estimation. Our results also suggest that currently, the reaction time to counter an attack in city-scale deployment is large due to the complexity of the XAI analysis.