Beyond Abstract Compliance: Operationalising trust in AI as a moral relationship
Lameck Mbangula Amugongo, Tutaleni Asino, Nicola J Bidwell
TL;DR
The paper critiques prevailing trust frameworks that treat trust as a designable property and instead advocates for a relational, culturally grounded conception of trust in AI, rooted in African Ubuntu philosophy. It introduces four trust-by-design principles—Communitarianism, Respect for others, Integrity, and Design publicity—and shows how to operationalize them across the AI lifecycle using agile processes. Two use-cases, AI-enabled antibiotic prescribing in healthcare and AI tutoring in education, illustrate community co-design, collective data governance, participatory evaluation, and relational red-teaming to foster durable trust. By embedding relational ethics into design and governance, the work aims to produce context-sensitive, equitable AI systems and invites broader cross-cultural engagement in trustworthy AI discourse.
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.