Anastasia Kordoni’s research while affiliated with Lancaster University and other places

Autonomous systems, such as drones and rescue robots, are increasingly used during emergencies. They deliver services and provide situational awareness that facilitate emergency management and response. To do so, they need to interact and cooperate with humans in their environment. Human behaviour is uncertain and complex, so it can be difficult to reason about it formally. In this paper, we propose IDEA: an adaptive software architecture that enables cooperation between humans and autonomous systems, by leveraging in the social identity approach. This approach establishes that group membership drives human behaviour. Identity and group membership are crucial during emergencies, as they influence cooperation among survivors. IDEA systems infer the social identity of surrounding humans, thereby establishing their group membership. By reasoning about groups, we limit the number of cooperation strategies the system needs to explore. IDEA systems select a strategy from the equilibrium analysis of game-theoretic models, that represent interactions between group members and the IDEA system. We demonstrate our approach using a search-and-rescue scenario, in which an IDEA rescue robot optimises evacuation by collaborating with survivors. Using an empirically validated agent-based model, we show that the deployment of the IDEA system can reduce median evacuation time by $13.6\%$ .

Digital visual data afford psychologists with exciting research possibilities. It becomes possible to see real-life interactions in real time and to be able to analyze this behavior in a fine-grained and systematic manner. However, the fact that faces (and other personally identifying physical characteristics) are captured as part of these data sets means that this kind of data is at the highest level of sensitivity by default. When this is combined with the possibility of automatic collection and processing, then the sensitivity risks are compounded. Here we explore the ethical challenges that face psychologists wishing to take advantage of digital visual data. Specifically, we discuss ethical considerations around data acquisition, data analysis, data storage, and data sharing. We begin by considering the challenges of securing visual data from both public space security systems and social media sources. We then explore the dangers of bias and discrimination in automatic data processing, as well as the dangers to human analysts. We set out the ethical requirements for secure data storage, the dangers of “function creep,” and the challenges of the right of the individual to withdraw from databases. Finally, we consider the tensions that exist between sensitive visual data that require extra protections and the recent open science movement, which advocates data transparency and sharing. We conclude by offering a practical route map for tackling these complex ethical issues in the form of a Privacy and Data Protection Impact Assessment template for researchers.

As autonomous systems increasingly become part of our lives, it is crucial to foster trust between humans and these systems, to ensure positive outcomes and mitigate harmful ones.

Autonomous systems, such as drones, are critical for emergency mitigation, management, and recovery. They provide situational awareness and deliver communication services which effectively guide emergency responders’ decision making. This combination of technology and people comprises a socio-technical system. Yet, focusing on the use of drone technology as a solely operational tool, underplays its potential to enhance coordination between the different agents involved in mass emergencies, both human and non-human. This paper proposes a new methodological approach that capitalizes on social identity principles to enable this coordination in an evacuation operation. In the proposed approach, an adaptive drone uses sensor data to infer the group membership of the survivors it encounters during the operation. A corpus of 200 interactions of survivors’ talk during real-life emergencies was computationally classified as being indicative of a shared identity or personal/no identity. This classification model, then, informed a game-theoretic model of human-robot interactions. Bayesian Nash Equilibrium analysis determined the predicted behavior for the human agent and the strategy that the drone needs to adopt to help with survivor evacuation. Using linguistic and synthetic data, we show that the identity-adaptive architecture outperformed two non-adaptive architectures in the number of successful evacuations. The identity-adaptive drone can infer which victims are likely to be helped by survivors and where help from emergency teams is needed. This facilitates effective coordination and adaptive performance. This study shows decision-making can be an emergent capacity that arises from the interactions of both human and non-human agents in a socio-technical system.

As autonomous systems are becoming part of our daily lives, ensuring their trustworthiness is crucial. There are a number of techniques for demonstrating trustworthiness. Common to all these techniques is the need to articulate specifications. In this paper, we take a broad view of specification, concentrating on top-level requirements including but not limited to functionality, safety, security and other non-functional properties. The main contribution of this article is a set of high-level intellectual challenges for the autonomous systems community related to specifying for trustworthiness. We also describe unique specification challenges concerning a number of application domains for autonomous systems.