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  • In this paper, we discuss how universities can become more essential players in the digital innovation and artificial intelligence (DI&AI) ecosystem and increase their capacity to support the “responsible” development and use of these technologies. The four sections of Part I explore the different ways in which universities can change the future of DI&AI and how DI&AI might transform the world of universities. Concrete examples of innovative and inspiring academic practices related to various challenges and opportunities explored in the paper are highlighted throughout. In section 1, we recognize that academics in the social and human sciences (SHS) have started to develop knowledge, tools and methodologies around the concept of responsible DI&AI. However, these have yet to be integrated in organizations and policy, which struggle to anticipate the societal impact of producing and using cutting-edge DI&AI systems. Collaboration between SHS scientists, their Science, Technology, Engineering and Mathematics (STEM) colleagues and non-academic actors in the DI&AI ecosystem is not yet commonplace. We explore some of the impediments to this collaboration, while stressing its increasing importance in the face of growing public mistrust of organizations operating DI&AI and collecting and using personal data. Universities have not yet adopted changes required to capitalize on their status as trust brokers and engage with civil society and other stakeholders on issues of responsible innovation.

  • In this paper, we discuss how universities can become more essential players in the digital innovation and artificial intelligence (DI&AI) ecosystem and increase their capacity to support the “responsible” development and use of these technologies. The four sections of Part I explore the different ways in which universities can change the future of DI&AI and how DI&AI might transform the world of universities. Concrete examples of innovative and inspiring academic practices related to various challenges and opportunities explored in the paper are highlighted throughout. In section 1, we recognize that academics in the social and human sciences (SHS) have started to develop knowledge, tools and methodologies around the concept of responsible DI&AI. However, these have yet to be integrated in organizations and policy, which struggle to anticipate the societal impact of producing and using cutting-edge DI&AI systems. Collaboration between SHS scientists, their Science, Technology, Engineering and Mathematics (STEM) colleagues and non-academic actors in the DI&AI ecosystem is not yet commonplace. We explore some of the impediments to this collaboration, while stressing its increasing importance in the face of growing public mistrust of organizations operating DI&AI and collecting and using personal data. Universities have not yet adopted changes required to capitalize on their status as trust brokers and engage with civil society and other stakeholders on issues of responsible innovation.

Dernière mise à jour depuis la base de données : 22/07/2025 05:00 (EDT)

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2. Planification