Search Results (5)

The advent of Artificial Intelligence in the cockpit and the air traffic control centre in the coming decade could mark a step-change improvement in aviation safety, or else could usher in a flush of ‘AI-induced’ accidents. Given that contemporary AI has well-known weaknesses, from data biases and edge or corner effects, to outright ‘hallucinations’, in the mid-term AI will almost certainly be partnered with human expertise, its outputs monitored and tempered by human judgement. This is already enshrined in the EU Act on AI, with adherence to principles of human agency and oversight required in safety-critical domains such as aviation. However, such sound policies and principles are unlikely to be enough. Human interactions with current automation in the cockpit or air traffic control tower require extensive requirements, methods, and validations to ensure a robust (accident-free) partnership. Since AI will inevitably push the boundaries of traditional human-automation interaction, there is a need to revisit Human Factors to meet the challenges of future human-AI interaction design. This paper briefly reviews the types of AI and ‘Intelligent Agents’ along with their associated levels of AI autonomy being considered for future aviation applications. It then reviews the evolution of Human Factors to identify the critical areas where Human Factors can aid future human-AI teaming performance and safety, to generate a detailed requirements set organised for Human AI Teaming design. The resultant requirements set comprises eight Human Factors areas, from Human-Centred Design to Organisational Readiness, and 165 detailed requirements, and has been applied to three AI-based Intelligent Agent prototypes (two cockpit, one air traffic control tower). These early applications suggest that the new requirements set is scalable to different design maturity levels and different levels of AI autonomy, and acceptable as an approach to Human-AI Teaming design teams. Full article

Artificial intelligence is developing at a rapid pace, with examples of machine learning already being used in aviation to improve efficiency. In the coming decade, it is likely that intelligent assistants (IAs) will be deployed to assist aviation personnel in the cockpit, the air traffic control center, and in airports. This will be a game-changer and may herald the way forward for single-pilot operations and AI-based air traffic management. Yet in aviation there is a core underlying tenet that ‘people create safety’ and keep the skies and passengers safe, based on a robust industry-wide safety culture. Introducing IAs into aviation might therefore undermine aviation’s hard-won track record in this area. Three experts in safety culture and human-AI teaming used a validated safety culture tool to explore the potential impacts of introducing IAs into aviation. The results suggest that there are indeed potential negative outcomes, but also possible safety affordances wherein AI could strengthen safety culture. Safeguards and mitigations are suggested for the key risk owners in aviation organizations, from CEOs to middle managers, to safety departments and frontline staff. Such safeguards will help ensure safety remains a priority across the industry. Full article

Human factors (HF) in aviation and maritime safety occurrences are not always systematically analysed and reported in a way that makes the extraction of trends and comparisons possible in support of effective safety management and feedback for design. As a way forward, a taxonomy and data repository were designed for the systematic collection and assessment of human factors in aviation and maritime incidents and accidents, called SHIELD (Safety Human Incident and Error Learning Database). The HF taxonomy uses four layers: The top layer addresses the sharp end where acts of human operators contribute to a safety occurrence; the next layer concerns preconditions that affect human performance; the third layer describes decisions or policies of operations leaders that affect the practices or conditions of operations; and the bottom layer concerns influences from decisions, policies or methods adopted at an organisational level. The paper presents the full details, guidance and examples for the effective use of the HF taxonomy. The taxonomy has been effectively used by maritime and aviation stakeholders, as follows from questionnaire evaluation scores and feedback. It was found to offer an intuitive and well-documented framework to classify HF in safety occurrences. Full article

A safety management system (SMS) is the overall set of procedures, documentation, and knowledge systems as well as the processes using them, which are employed within an organisation to control and improve its safety performance. Safety management systems are often observed as being bureaucratic, distinct from actual operations, and being too much focused on the prevention of deviations from procedures rather than on the effective support of safety in the real operational context. The soft parts of advancing safety in organisations, such as the multitude of interrelations and the informal aspects in an organisation that influence safety, are often only considered to a limited extent. As a way forward, this paper presents two coupled approaches. Firstly, a generic tool for assessing the maturity of safety management of aviation organisations is presented, which accounts for recent insights in effectively incorporating human factors. This assessment tool provides insight into the strong and weak topics of an organisation’s SMS. Secondly, an overview is given of a range of approaches that aim to improve the safety of aviation organisations by strengthening relevant organisational processes and structures, with a focus on human factors. The relations of these approaches with SMS are discussed, and the links with topics of the SMS maturity assessment tool are highlighted. Full article

Aviation is a highly inter-connected system. This means that a problem in one area may cause effects in other countries or parts of the Air Transport System (ATS). Examples range from local air traffic disruptions to the 2010 volcanic ash crisis. Agility, like resilience, refers to the ability to cope with dynamics and complexity in a flexible manner, by adjusting and adapting performance and the organization of work to fit changing demands. The aim of this work is to help ATS organizations with increasing their agility in the face of crises and challenges. To this end, this article presents the Agile Response Capability (ARC) guidance material. ARC was developed from a literature study and a number of case studies that combined past event analysis, interviews, focus groups, workshops, questionnaires, and exercise observation methodologies. ARC aims to help aviation organizations to set up, run, and evaluate exercises promoting agility to handle disturbances and crises, and to enable structured pro-active and retrospective analysis of scenarios and actual events. The elements and steps of the ARC approach are illustrated and exemplified with data from three case studies. The ARC methodology facilitates more agile and resilient ways of responding to the fundamental and novel surprises that have become almost commonplace in the past decade, and are likely to continue to do so. Full article