Automation and Liability in ATM [GEN-LIABILITY]
Classroom Course
A wave of new systems based on automation is rapidly changing the Air Traffic Management. Consequently, the work of the air traffic controllers all over Europe is changing as well. Do these changes affect attribution of legal liabilities? How?
Answering these questions is the purpose of the Automation and Liability in ATM course. In short, it aims to ensure that all actors and stakeholders concerned by the introduction of new systems based on automation correctly address and understand the complex link between automation and liability.
The course stems from the forefront of the research on automation and liability in socio-technical systems. It proposes a new systemic approach to liability, in which we consider liability as a dynamic element of the system.
In this perspective, liability is not static; rather its allocation depends on the distribution of tasks and functions within the system. Consequently, liability allocation can also be set by design.
In other words, along with technical and operational aspects of the technology, liability can be taken into account during the design process of the system. This allows ensuring that the resulting liability allocation scheme is acceptable for all the actors and stakeholders involved.
The Legal Case is the concrete method this course offers to apply this approach of liability by design. As the other more famous Safety and Human Performance Cases, with whom it can combine, the Legal Case intends to make sure that liability implications of a new technology are opportunely taken into account and addressed during the design process.
The course couples theoretical explanations with the analysis of specific case studies and group exercises. This facilitates showing how the introduction of systems based on different levels of automation changes operations, and consequently liability. The case studies addressed concern most innovative technologies. These include, for example:
- Remote Tower and Remote Tower Centres
- New generation of Airborne Collision Avoidance Systems (ACAS
X)
- Use of drone and the introduction of Unmanned Traffic
Management (UTM)
-Artificial Intelligence and Deep Learning.
Answering these questions is the purpose of the Automation and Liability in ATM course. In short, it aims to ensure that all actors and stakeholders concerned by the introduction of new systems based on automation correctly address and understand the complex link between automation and liability.
The course stems from the forefront of the research on automation and liability in socio-technical systems. It proposes a new systemic approach to liability, in which we consider liability as a dynamic element of the system.
In this perspective, liability is not static; rather its allocation depends on the distribution of tasks and functions within the system. Consequently, liability allocation can also be set by design.
In other words, along with technical and operational aspects of the technology, liability can be taken into account during the design process of the system. This allows ensuring that the resulting liability allocation scheme is acceptable for all the actors and stakeholders involved.
The Legal Case is the concrete method this course offers to apply this approach of liability by design. As the other more famous Safety and Human Performance Cases, with whom it can combine, the Legal Case intends to make sure that liability implications of a new technology are opportunely taken into account and addressed during the design process.
The course couples theoretical explanations with the analysis of specific case studies and group exercises. This facilitates showing how the introduction of systems based on different levels of automation changes operations, and consequently liability. The case studies addressed concern most innovative technologies. These include, for example:
- Remote Tower and Remote Tower Centres
- New generation of Airborne Collision Avoidance Systems (ACAS
X)
- Use of drone and the introduction of Unmanned Traffic
Management (UTM)
-Artificial Intelligence and Deep Learning.
Giuseppe Contissa
Presenter
Paola Lanzi
Presenter
Matteo SOTTILE
Course Manager
Damiano Taurino
Presenter
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