Contents 2


● Although the need to avoid ethnic and gender discrimination and biases in Data Science projects is mentioned (guaranteeing the representation of all groups in the data), there is no mention of the need to design applications adapted to different cultural sensitivities and values (culture-aware systems) or the need to enhance the representation of all groups in the research, design and development of AI applications (through mechanisms such as community-based development methodologies, citizen science, etc). We emphasize the multi-cultural strength of Europe and the opportunity that this represents for AI, as well as the synergies that can exist to build a more cohesive Europe if an AI sensitive to people and to these cultures is developed. This is only possible with the strengthening of pan-european research involving both the public and private sector.

● The White Paper does speak of the need to promote women’s vocations in AI technologies, but not to encourage female entrepreneurship in AI. In the computer industry only 1-2% of new companies receiving venture capital funds are run by women, even though women-led companies get a 200% return on investment. This is the area where gender biases in the technology industry are most apparent. This is a missed opportunity to develop a more inclusive and innovative AI.