ADAPT Consortium

ADAPT is an international and interdisciplinary research and development consortium exploring and researching the potential and real use of machine learning in vocational training. Operating in the intersection of welfare coordination and research, ADAPT can facilitate and facilitate tests and processes that not only enable an efficient implementation of state-of-the-art algorithmic technology in welfare services, but also safeguard democratic and ethical principles that should be intrinsic when working with and for human beings.

Machine learning has been recognized as an increasingly important tool in welfare services. For vocational training, this might mean providing new opportunities for learners to acquire practical skills and knowledge, creating new systems of recommendations and counseling, as well as bringing new statistical insights. However, this novel use of data also presents several risks that must be carefully considered, be it bias algorithms, bad quality data or low data-literacy within the organization.

The ADAPT consortium is committed to helping welfare organizations make the best possible use of AI in vocational training while at the same time mitigating organizational, social, and political pitfalls and risks. ADAPT combines a hands-on applied approach, close collaboration with social welfare providers and clients, and advanced expertise in computer science and information technology. This enables the consortium to facilitate data-oriented projects, tests and designs at small and large scale while simultaneously adding a critical perspective in order to safeguard the rights of users and citizens, as well as general democratic principles.

The ADAPT consortium consists of:

ADAPT has worked together as a consortium since 2019 in projects funded by actors such as the European Social Fund (ESF) and VINNOVA (Sweden). As a research and development collaboration, ADAPT offers expertise in AI technology and implementation, data-literacy workshops and testing and prototyping of data-driven systems in both research projects and organizational development.