From the inception of the project to the conclusion of the reporting period, significant work has been carried out.
WP 1 was dedicated to redefining task-based occupation modeling and was subdivided into several tasks. In Task 1.1 our researchers aimed to achieve the primary objective by developing a model that facilitates the examination of relationships between different data domains, encompassing tasks, occupations, and skills. This model laid the foundation for a survey, and a machine learning model was designed, ready for further training once survey data becomes available. Importantly, collaboration allowed us to gain a profound understanding of workforce challenges, particularly the transition from fixed work outcomes to dynamic work outcomes, carrying higher potential value. This effort entailed comprehensive research and analysis of portable skills in the labor market.
In Task 1.2 our team attended periodic meetings to understand the project's expectations from business partners. This included focusing on defining a survey structure and applying semantic analysis to streamline the survey by reducing free-text selections. The survey targeted both managers and employees, aiming to determine the most appropriate tasks for different occupations within companies.
Task 1.3 witnessed the successful finalization of survey questions. These activities within WP1 represent a highly collaborative effort to redefine occupation modeling and develop predictive models, leveraging advanced data analysis and machine learning techniques. These activities align seamlessly with the broader project's objectives of understanding and adapting to future workforce needs and the evolving labor market landscape.
Additionally, we've made remarkable progress in WP2.Task 2.1 We crafted a scientific survey tailored to assess machine learning suitability and employee skills, drawing data. The survey was designed to cover demographics, occupation-related questions, task characteristics, skills, and suitability for machine learning. We refined the survey structure. Task 2.2 involved our researchers working on understanding the ONET database and developing a proof of concept for the survey. Task 2.3 was focused on gender analysis of survey data and the development of a comprehensive model to investigate the impact of AI on gender.
Our team developed a robust gender analysis model. We initiated discussions on gender and AI, ensuring gender balance in skills, and collaborated closely with partners to refine survey questions. In Task 2.4 our team worked on creating training and education sets.
Our commitment also extended to WP3, in Task 3.1 we focused on experimenting with proposed methodologies for the Recommendation Portal, resulting in the development of a preliminary version of the portal. We collected data from various sources to refine and develop algorithms for mapping tasks and skills from O*NET into educational data.
Additionally, we also made significant progress in WP4. Task 4.1 focused on the organization of secondments and Task 4.2 dedicated to the organization of workshops, seminars, and dissemination events. Furthermore, in WP5, we have undertaken dissemination and communication activities, such as the creation of a project website and social media accounts.These channels have been utilized to share information about AI4LABOUR researchers, partner institutions, secondments, and project results, ensuring that researchers and the general public are well-informed.
