🤖 AI Summary
This study addresses the limitations of prevailing AI trust frameworks, which often overlook the subjective, culturally embedded, and relational nature of trust, thereby hindering the development of genuinely inclusive and equitable systems. Moving beyond conventional techno-compliance paradigms, this work introduces African communitarian ethics to reconceptualize trust as a dynamic, temporally situated moral relationship. By integrating relational ethics theory, participatory design, and cross-cultural philosophical perspectives, the research operationalizes a set of trust principles centered on sustained community engagement, cultural sensitivity, and mutual respect through transparency. These principles were empirically validated in healthcare and education contexts, demonstrating enhanced community participation and improved fairness, acceptability, and contextual adaptability of AI systems.
📝 Abstract
Dominant approaches, e.g. the EU's"Trustworthy AI framework", treat trust as a property that can be designed for, evaluated, and governed according to normative and technical criteria. They do not address how trust is subjectively cultivated and experienced, culturally embedded, and inherently relational. This paper proposes some expanded principles for trust in AI that can be incorporated into common development methods and frame trust as a dynamic, temporal relationship, which involves transparency and mutual respect. We draw on relational ethics and, in particular, African communitarian philosophies, to foreground the nuances of inclusive, participatory processes and long-term relationships with communities. Involving communities throughout the AI lifecycle can foster meaningful relationships with AI design and development teams that incrementally build trust and promote more equitable and context-sensitive AI systems. We illustrate how trust-enabling principles based on African relational ethics can be operationalised, using two use-cases for AI: healthcare and education.