Periodic Reporting for period 1 - AI4LABOUR (Reshaping labour force participation with Artificial Intelligence)
Periodo di rendicontazione: 2021-11-01 al 2023-10-31
The overall objective of the AI4LABOUR project is to design a recommendation/planning portal with AI techniques for the benefit of individuals, companies, policymakers, and educational institutions from Europe and the global for helping their decision processes to reduce the possible negative AI-based impacts on the labour force.
Given that studies on future of work, the possibility of automation of 30 percent of global working hours by 2030, its important to take into account that AI can take on the monotonous and repetitive aspects of works that are done by humans when some jobs will be automated. This may ensure focusing on more strategic or more analytical works. To offer a solution to this problem, AI4LABOUR aims to predict which kind of new occupations will appear in near future and which kind of skills will be needed to get these new occupations. Shortly, in the context of this project, required training to obtain these required skills would also be designed. To achieve this goal, an innovative skill-based modeling and skill development methodology armed with AI techniques will be designed for the labor force.
Given that studies on future of work, the possibility of automation of 30 percent of global working hours by 2030, its important to take into account that AI can take on the monotonous and repetitive aspects of works that are done by humans when some jobs will be automated. This may ensure focusing on more strategic or more analytical works. To offer a solution to this problem, AI4LABOUR aims to predict which kind of new occupations will appear in near future and which kind of skills will be needed to get these new occupations. Shortly, in the context of this project, required training to obtain these required skills would also be designed. To achieve this goal, an innovative skill-based modeling and skill development methodology armed with AI techniques will be designed for the labor force.
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.
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.
The completion of WPs 3, 4, 5, and 6 is well within our anticipated timeline, and we're making progress towards achieving the project's objectives. In Task 3.2 the testing phase of the recommendation portal is currently underway. In Task 3.3 we are gearing up for the activation of the portal in full operational capacities. This means that soon, the Recommendation Portal will be ready to provide valuable insights and recommendations based on the data.
WP4 is advancing steadily, with Task 4.1 focused on organizing secondments. In Task 4.2 the organization of workshops and seminars is an essential component of our knowledge-sharing efforts, facilitating the dissemination of our research findings to a wider audience. Moreover, Task 4.3 centers on the planning and organization of the Final Conference.Task 4.4 dedicated to organizing Round Tables for Stakeholders.
Additionally, Task 5.3 focusing on the exploitation strategy, is a critical component of our project's long-term impact. We are diligently working on strategies to ensure that the knowledge and tools developed in the project applied and leveraged beyond its conclusion.
In WP6, Task 6.2 which deals with project sustainability, is actively preparing for the continuation of our project's efforts even after its formal completion. Lastly, Task 6.3 involves meticulous project reporting, ensuring that our project's progress and achievements are well-documented and transparent to our partners and stakeholders.
In conclusion, we are on the way of our achievements so far and are determined to continue our outstanding work in the coming phases of the project.
The primary objective of the Digitizing European Industry strategy is to enable companies of all sizes and across all sectors in Europe to harness digital innovations. The scoping paper underscores the importance of supporting both generic AI and domain-specific research areas.
In this context, legislative initiatives at the EU level are emerging. While well-intentioned, there is a concern that early-stage regulations may inadvertently stifle the future growth of AI and limit its positive impact on the labor market and industry.
This project is aligned with these EU strategies. Its goal is to predict the emergence of new occupations in the near future and identify the corresponding skills required for these roles. We aim to enhance cooperation and knowledge transfer across sectors and disciplines, thus increasing the research and innovation capacity of participating organizations. The project fosters innovative collaboration through synergies, networking activities, workshops, seminars, summer schools, and a final conference, facilitating knowledge sharing.
WP4 is advancing steadily, with Task 4.1 focused on organizing secondments. In Task 4.2 the organization of workshops and seminars is an essential component of our knowledge-sharing efforts, facilitating the dissemination of our research findings to a wider audience. Moreover, Task 4.3 centers on the planning and organization of the Final Conference.Task 4.4 dedicated to organizing Round Tables for Stakeholders.
Additionally, Task 5.3 focusing on the exploitation strategy, is a critical component of our project's long-term impact. We are diligently working on strategies to ensure that the knowledge and tools developed in the project applied and leveraged beyond its conclusion.
In WP6, Task 6.2 which deals with project sustainability, is actively preparing for the continuation of our project's efforts even after its formal completion. Lastly, Task 6.3 involves meticulous project reporting, ensuring that our project's progress and achievements are well-documented and transparent to our partners and stakeholders.
In conclusion, we are on the way of our achievements so far and are determined to continue our outstanding work in the coming phases of the project.
The primary objective of the Digitizing European Industry strategy is to enable companies of all sizes and across all sectors in Europe to harness digital innovations. The scoping paper underscores the importance of supporting both generic AI and domain-specific research areas.
In this context, legislative initiatives at the EU level are emerging. While well-intentioned, there is a concern that early-stage regulations may inadvertently stifle the future growth of AI and limit its positive impact on the labor market and industry.
This project is aligned with these EU strategies. Its goal is to predict the emergence of new occupations in the near future and identify the corresponding skills required for these roles. We aim to enhance cooperation and knowledge transfer across sectors and disciplines, thus increasing the research and innovation capacity of participating organizations. The project fosters innovative collaboration through synergies, networking activities, workshops, seminars, summer schools, and a final conference, facilitating knowledge sharing.