Advancements in urban resilience are essential as cities face extreme events like floods, heat waves, or pandemics, alongside social and economic challenges. Building resilient urban systems depends on effectively integrating AI with the human decision-making process, ensuring adaptability and sustainability while involving experts and citizens. The proposed approach centers on human-machine interaction methodologies – Human-in-the-loop (HITL), Human-on-the-loop (HOTL), and Human-in-command (HIC) – to balance automation and human control in urban systems. These procedures ensure decisions are guided by data while remaining aligned with human ethics and societal values, maintaining human oversight and accountability. This MLU explores the coalescence of AI and human input within urban planning systems, emphasizing HITL, HOTL, and HIC. It highlights responsible AI participation through scenarios like smart city initiatives, showcasing citizen involvement, assurance of transparency, and application of AI to achieve resilient, inclusive urban environments, empowering human oversight and active citizenship.
T4R - Learning journey
Learning with Microlearning Units
Human in the Loop, Human on the Loop, Human in Command – Where Humans and Machines Work Together
FRAMEWORK:
ETHICS, INCLUSION, DEMOCRATIZATION
MODULE:
Inclusivity and accessibility by design
EQF 6
EID-205
| EID-200 | third loop |
|---|---|
| Human in the Loop, Human on the Loop, Human in Command – Where Humans and Machines Work Together | Learner knows the difference between human-in-the-loop (HITL), human-on-the-loop (HOTL), and human-on-command (HOC), particularly regarding the importance of human responsibility and feedback loops. Learner provides examples of HITL, HOTL, and HOC in the context of AI usage in urban planning. Learner can apply the mechanisms of HITL, HOTL, and HOC to citizen participation projects. |
