Objective: The challenge of keeping humans in meaningful control of highly automated systems is growing as these systems become more common in high-risk domains like aviation, the military, and emergency services. One suggested method to ensure human control and responsibility is to apply the principles of meaningful human control. This study aimed to explore the applicability of meaningful human control through a case study involving operators interacting with a prototype interface designed for controlling multiple unmanned aerial vehicles fighting forest fires.  Method: A simulated scenario was created and implemented through a prototype interface for human-swarm interaction. Empirical data included screen- and audio recordings of participants engaging with the simulated scenario through the prototype. The Joint Control Framework was used to transcribe and analyze the interaction.  Results: The results indicate that the level of meaningful human control in the interaction between the operator and the unmanned aerial vehicle system is dynamic and context-dependent. It varies based on the type of task, the operator’s level of cognitive control, the level of interaction with the swarm, and the system’s level of autonomy. It is realized through the joint actions performed by the operator and the system.  Conclusion: For meaningful human control to be applicable, it needs to be operationalized as a situated and contextual measure, rather than a binary concept. Future measures of meaningful human control, whether subjective or objective, should reflect this approach.

The thesis has aimed to study the emergence of a new weapons technology, also known as ‘killer robots’ or lethal autonomous weapon system. It seeks to answer what factors drive the development and deployment of this weapon system without ‘meaningful human control’, a component that allows the decision to kill to be delegated to machines. The research question focuses on seeking the motivations to develop and deploy LAWS, as well as the consequences this would have on military conduct and conflict characteristics.The incentives they bring up and the way of adopting them has been studied by synthesizing antinomic democratic peace theory and adoption capacity theory respectively. The findings of this qualitative content analysis lead to two major conclusions. (1) That LAWS present severe risk avoidance and costs reduction potential for the user. These factors have a more prevalent pull on democracies than autocracies, since they stand to benefit from LAWS’ specific capabilities more in comparison. (2) That their adoption is aided by low financial intensity needed to adopt it, due to the high commercial profitability and applicability of AI technology, and the ease of a spillover to military sphere. Their adoption is hindered by high organizational capital needed to implement the drastic changes LAWS bring. All of this leads to the prediction that LAWS are likely to proliferate further, at a medium speed, and potentially upset the balance of power.