Critical Issues for the Uptake of AI in Science - THEME 1: R&D agenda setting, technology assessment, foresight and science advice

Diversity in AI research – There is a need to ensure the gender, ethnic and cultural diversity of the AI workforce, in the interest of equity and to improve the quality of research and other outcomes. [...] Ethical data use – The use of big data and AI complicates present-day notions of consent and of human research participants, as well as the ways in which data is collected and used. [...] Advantage – Much of the data required for the development of scientific AI will not fall within the scope of open data initiatives, for example data held by the private sector. [...] Data infrastructures – The development of AI for science will require harmonization of practices and the development of communities of practice. [...] Protection and use of digital data – Text and data mining risk infringing copyright through the creation of unauthorized copies, and may violate the terms and conditions of websites and databases.