The Research problem
AI has the potential to increase efficiency, quality or precision, which can be used, for example, for better diagnosis and treatment of patients or resource-saving, high-quality production. Initiatives in regulating and norming AI on transnational, national and corporate level face privacy and the shortcomings in the trustworthiness of AI. Researchers emphasize ethical challenges that are related to AI usage. When it comes to concrete application scenarios, there are also challenges related to management accountability and the well-being of users at an operational level. In order to find context-sensitive approaches for responsible AI corresponding with the UN-Sustainable Development Goals there is a need to explore critical interfaces between those who develop and provide AI tools and those who adapt them. Context-sensitive insights can only be provided by global transdisciplinary research communities reflecting challenges of interfaces between individual experts, organizational domains and/or in global business.
The project team intends to integrate and validate their ongoing research on user-centred and context-sensitive AI applications in a global network emphasizing challenges of technology, organizational and individual development. To this end, the team starts with building a common ground on context-sensitive AI at work in monthly mealtime lectures. They establish a homepage with validated methods and research findings. They offer an online master class module on responsible AI from an transdisciplinary perspective and organize a winter school at RUB to intensify the discourse and enrich the content of the master class module. In a temporary mentorship program linked with up to three LabVisits, early career researchers can deepen their expertise in the field. Research on context-sensitive AI at work will made visible in up to three standing working groups on global conferences. In addition, the project team organizes a global young-faculty-industry symposium to disseminate research findings and strengthen global ties between researchers and practitioners.
The project team aims at establishing a global network for user-centred and context-sensitive AI applications. It will bundle, share, and further cross-validate already existing academic knowledge on how to reach responsible AI applications in a specific context and will condense this knowledge in an evaluation scheme for AI applications in line with the UN sustainable development goals. The team wants to make context-sensitive approaches of responsible AI visible and accessible to give an example for transdisciplinary partnerships for responsible AI industry applications and to foster academic-industry partnerships in this field. Since responsible AI application is a global challenge and requires deep knowledge of how AI tools are integrated in workflows and process designs, a strong network is needed. The partnership of six WUN universities and additional partners in research, teching and third mission on five continents is a good starting point for sustainend collaboration towards responsible AI applications around the globe.
Professor Dr Pattanasak Mongkolwat, Mahidol University
Professor Dr Joyce Nakatumba-Nabende, Makerere University
Professor Dr Shahid Bashir, Tecnológico de Monterrey
Professor Dr Emma Ruttkamp-Bloem, University of Pretoria
Professor Dr Tom Stoneham, University of York