Rapid development of artificial intelligence (AI) capabilities carries immense possibilities for tackling societal challenges from food insecurity to good governance across Africa. However, guiding the technology’s trajectory requires more research methodologies incorporating diverse expert insights alongside community participation. This analysis details the significance of transdisciplinary AI investigations and recommended practices, putting people at the center.
Monodisciplinary Limitations
Conventional academic AI research frequently occurs in siloed technology or engineering departments focused narrowly on computer science techniques 1. While pushing technical boundaries, such endeavors overlook integrating applied perspectives from domain experts in agriculture, healthcare, education, etc., or social scientists anticipating impacts on marginalized groups.
Even multidisciplinary initiatives in several fields lack consistent frameworks facilitating knowledge co-production across boundaries 2. Research questions target publishing papers rather than informing policy and practice. These shortcomings result in AI solutions misaligned with nuanced realities of implementation contexts or excluding underserved populations.
The Transdisciplinary Approach
In contrast, transdisciplinarity structures collaborative inquiry spanning stakeholders within and beyond academia focused on real-world priorities are increasingly rampant. Scientists engage domain experts, civil society, and communities in defining research problems, developing methodologies, interpreting data, and applying findings in practice.
Transdisciplinarity has been explained as a reflective research process that addresses societal questions by integrating different scientific and extra-scientific insights 3. This emphasis on contextual applicability and participatory processes ensures research rigorously accounts for social dynamics and people’s aspirations.
Key Practices for Meaningful Inclusion
Successfully leveraging more transdisciplinary AI investigations to catalyze African development requires employing more frameworks, incentives, and capabilities supporting meaningful engagement. Policy mechanisms play a pivotal role, advocating for funding to be directed more towards participatory problem-focused research networks rather than merely prioritizing publication volume. This shift fosters a more inclusive and impactful approach to addressing societal challenges.
Creating more dialogue platforms is also essential, providing spaces where technologists, government representatives, and community leaders can come together to discover and align mutual priorities. Structured ethics anticipation is another crucial element, enabling collaborative speculation on potential risks from AI applications. Simultaneously, investing in capacity building ensures that trainees from diverse disciplines and geographical locations can collaborate, contributing to the joint creation of new knowledge. Implementing recursive participative evaluation further solidifies sustained relationships and accountability between researchers and the citizens impacted by AI advancements.
In effect, transdisciplinarity recognizes technoscience as deeply embedded within society. Open, inclusive collaboration emerges as a vital strategy, explicitly targeting questions of justice, sustainability, and democracy to steer AI development away from extractive outcomes.
Synthesizing Perspectives for Positive Futures
Across Africa, nascent examples showcase the transformative potential of transdisciplinary AI research in advancing the public good. In Rwanda, teams of young engineers and medical professionals collaborated to codesign drones that expand access to vaccine cold chains, leveraging insights from community health workers 4. Meanwhile, Makerere University in Uganda partnered with farmers and regional officials to create AI services that match cultivation advice to hyperlocal climate impacts based on meteorology insights 5.
These cases exemplify participatory goal-setting and knowledge integration, resulting in accessible innovations. Governments can purposefully harness the benefits of transdisciplinary approaches by incentivizing more such research through funding schemes and actively involving more stakeholders in defining national AI strategies.
Governing AI development pathways toward equitable progress relies on illuminating, through inclusive research, where technologies interface helpfully or harmfully with Africa’s sociocultural realities and developmental needs. Transdisciplinary methodologies, leveraging collaborative spaces between citizens, experts, and policymakers, enable precisely such enlightening discoveries. Forward-looking African nations investing in convening and amplifying these multifaceted conversations will make the most prudent technology decisions, advancing sustainable futures for all their peoples.