DCODE has significantly advanced digital design research by training 15 PhD students to become leaders in academia, industry, and civil society. Through world-class graduate programs, interdisciplinary supervision, summer and winter schools, and collaborations in prototeams, ESRs gained expertise in research methods, ethics, leadership, and communication. By engaging with industry-leading organizations, they developed transferable skills and practical knowledge applicable across sectors.
The project has made substantial progress beyond the state of the art, particularly in redefining digital design and AI research. ESRs developed innovative frameworks addressing major societal challenges: an inclusive digital futures framework that moves beyond traditional algorithmic interaction; a decentralized interaction model that rethinks human-machine interfaces; and a data valuation approach integrating feminist, indigenous, and sustainability perspectives to foster societal equity. Research also explored democratic governance in the digital age, emphasizing the balance between technological speed and the stability of democratic processes. Ethics was redefined as an ongoing process rather than a fixed problem, influencing new methodologies for responsible design.
DCODE has sought to shape public discourse by engaging with a variety of audiences including industry, communities of interest and practice, and digital activist groups. The DCODE Conversations podcast (with over 200 listens) and the open-access book Rethink Design: A Vocabulary for Designing with AI (downloaded over 1800 times) have extended research accessibility to broader audiences.
The project's impact aligns with EU priorities on human-centric AI, emphasizing fundamental rights and sustainable digital futures. By integrating social and technical perspectives, it will contribute to the development of ethical AI systems. The high employability of ESRs, with several securing positions before graduation, reflects the success of the training program.
DCODE has championed inclusivity, ensuring intersectional perspectives in AI research. Sensitivity training, discussions on postcoloniality, and studies on marginalized communities’ experiences with AI have addressed structural inequalities in technological systems. Open science principles have been upheld, with research findings made openly accessible through Zenodo.