Beyond Abstract Compliance: Operationalising trust in AI as a moral relationship
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866912862655479808 |
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| author | Amugongo, Lameck Mbangula Asino, Tutaleni Bidwell, Nicola J |
| author_facet | Amugongo, Lameck Mbangula Asino, Tutaleni Bidwell, Nicola J |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_22769 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Abstract Compliance: Operationalising trust in AI as a moral relationship Amugongo, Lameck Mbangula Asino, Tutaleni Bidwell, Nicola J Computers and Society Artificial Intelligence 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. |
| title | Beyond Abstract Compliance: Operationalising trust in AI as a moral relationship |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2601.22769 |