The relationship between a human being and an AI system has to be considered as a collaborative process between two agents during the performance of an activity. When there is a collaboration between two people, a fundamental characteristic of that collaboration is that there is co-supervision, with each agent supervising the actions of the other. Such supervision ensures that the activity achieves its objectives, but it also means that responsibility for the consequences of the activity is shared. If there is no co-supervision, neither collaborator can be held co-responsible for the actions of the other. When the collaboration is between a person and an AI system, co-supervision is also necessary to ensure that the objectives of the activity are achieved, but this also means that there is co-responsibility for the consequences of the activities. Therefore, if each agent's responsibility for the consequences of the activity depends on the effectiveness and efficiency of the supervision that that agent performs over the other agent's actions, it will be necessary to take into account the way in which that supervision is carried out and the factors on which it depends. In the case of the human supervision of the actions of an AI system, there is a wealth of psychological research that can help us to establish cognitive and non-cognitive boundaries and their relationship to the responsibility of humans collaborating with AI systems. There is also psychological research on how an external observer supervises and evaluates human actions. This research can be used to programme AI systems in such a way that the boundaries of responsibility for AI systems can be established. In this article, we will describe some examples of how such research on the task of supervising the actions of another agent can be used to establish lines of shared responsibility between a human being and an AI system. The article will conclude by proposing that we should develop a methodology for assessing responsibility based on the results of the collaboration between a human being and an AI agent during the performance of one common activity.

Air Traffic Controllers’ (ATCos) mental workload is likely to remain the specific greatest functional limitation on the capacity of the Air Traffic Management (ATM) system. Developing computational models to monitor mental workload and task complexity is essential for enabling ATCOs and ATM systems to adapt to varying task demands. Most methodologies have computed task complexity based on basic parameters such as air-traffic density; however, literature research has shown that it also depends on many other factors. In this paper, we present a study in which we explored the possibility of predicting task complexity and performance through mental workload measurements of participants performing an ATM task in an air-traffic control simulator. Our findings suggest that mental workload measurements better predict poor performance and high task complexity peaks than other established factors. This underscores their potential for research into how different ATM factors affect task complexity. Understanding the role and the weight of these factors in the overall task complexity confronted by ATCos constitutes one of the biggest challenges currently faced by the ATM sphere and would significantly contribute to the safety of our sky.

Four experiments examined the role of meaning frequency (dominance) and associative strength (measured by associative norms) in the processing of ambiguous words in isolation. Participants made lexical decisions to targets words that were associates of the more frequent (dominant) or less frequent (subordinate) meaning of a homograph prime. The first two experiments investigated the role of associative strength at long SOAs (Stimulus Onset Asynchrony) (750 ms.), showing that meaning is facilitated by the targets' associative strength and not by their dominance. The last two experiments traced the role associative strength at short SOAs (250 ms), showing that the manipulation of the associative strength has no effect in the semantic priming. The conclusions are: on the one hand, semantic priming for homographs is due to associative strength manipulations at long SOAs. On the other hand, the manipulation of the associative strength has no effect when automatic processes (short SOAs) are engaged for homographs. A través de cuatro experimentos, se examinó el papel de la frecuencia del significado (dominancia) y de la fuerza asociativa (medida mediante normas asociativas) en el procesamiento de palabras ambiguas aisladas. Los participantes tomaron decisiones léxicas acerca de palabras meta que eran las asociadas del significado más frecuente (dominante) o menos frecuente (subordinado) de un homógrafo primo. Los primeros dos experimentos investigaron el papel de la fuerza asociativa en las SOAs (asincronía del comienzo del estímulo) largas (750 ms), demostrando que el significado se ve facilitado por la fuerza asociativa de la meta y no por su dominancia. Los segundos experimentos trazaron el papel de la fuerza asociativa en las SOAs cortas (250 ms), demostrando que la manipulación de la fuerza asociativa no influye en la ceba semántica. Se concluye que: (a) la ceba semántica para homográficos se debe a las manipulaciones de la fuerza asociativa en las SOAs largas y (b) la manipulación de la fuerza asociativa no tiene ningún efecto cuando los procesos automáticos (SOAs cortas) están ocupados para los homógrafos.