Towards a safety learning culture for the shipping industry : a white paper

Within the framework of the EU-funded SAFEMODE project, a series of confidential, in-depth interviews of seafarers and investigators was carried out to ascertain the current status of Safety Culture in the shipping industry, and to recommend possible avenues for improvement. The interview script covered practices in incident and accident investigation and reporting, the Human Element, the factors that keep the ship safe, the role of the Safety Management System, Just Culture and Safety Learning. The seafarers’ and investigators’ interviews were complemented by small focus groups with unions, education and safety bodies. Participants were open and genuine in providing their opinions, as anonymity was preserved. The general consensus among interviewees was that seafarers are the ones who keep ships safe at sea, which is a good omen for Safety Culture in the shipping industry. The originally intended ‘destination’ for the shipping industry was to be Just Culture, but the interviews quickly revealed that Safety Learning, already evident in some parts of the industry, appeared a more pragmatic and attainable destination, one that could add safety improvements and shore up Safety Cultur

Learning for Air Traffic Management: guidelines for future AI systems

The SESAR-funded Modern ATM via Human / Automation Learning Optimisation (MAHALO) project recently completed two years of technical work exploring the human performance impacts of AI and Machine Learning (ML), as applied to enroute ATC conflict detection and resolution (CD&R). It first developed a hybrid ML CD&R capability, along with a realtime simulation platform and experimental User Interface. After a series of development trials, the project culminated in a pair of field studies (i.e., human-in-the-loop trials) across two EU countries, with a total of 35 operational air traffic controllers. In each of these two field studies, controller behaviour was first captured in a pre-test phase, and used to train the ML system. Subsequent main experiment trials then experimentally manipulated within controllers both Conformance (as either a personalised-, group average-, or optimized model) and Transparency (as ether a baseline vector depiction, an enhanced graphical diagram, or a diagram-plus-text presentation). The proposed paper presents guidelines on the design and implementation of ML systems in Air Traffic Control, derived from the results and lesson learned from the Simulations, as well as the qualitative feedback received from the controllers themselves.Control & Simulatio