# Enriching Moral Perspectives on AI: Concepts of Trust amongst Africans

arXiv: https://arxiv.org/abs/2508.14116 · HTML: https://mlsanity.com/paper/2508.14116/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 96 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale](https://mlsanity.com/paper/2211.03759/) (2022)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- [Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning](https://mlsanity.com/paper/2202.05338/) (2022)
- [WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?](https://mlsanity.com/paper/2305.06415/) (2023)
- [Narratives and Counternarratives on Data Sharing in Africa](https://mlsanity.com/paper/2103.01168/) (2021)
- [Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects](https://mlsanity.com/paper/2206.06738/) (2022)
- [Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2212.10693/) (2022)

## Built on by (most cited first, 1 of 1)

- [Beyond Abstract Compliance: Operationalising trust in AI as a moral relationship](https://mlsanity.com/paper/2601.22769/) (2026)

## Abstract (arXiv)

The trustworthiness of AI is considered essential to the adoption and application of AI systems. However, the meaning of trust varies across industry, research and policy spaces. Studies suggest that professionals who develop and use AI regard an AI system as trustworthy based on their personal experiences and social relations at work. Studies about trust in AI and the constructs that aim to operationalise trust in AI (e.g., consistency, reliability, explainability and accountability). However, the majority of existing studies about trust in AI are situated in Western, Educated, Industrialised, Rich and Democratic (WEIRD) societies. The few studies about trust and AI in Africa do not include the views of people who develop, study or use AI in their work. In this study, we surveyed 157 people with professional and/or educational interests in AI from 25 African countries, to explore how they conceptualised trust in AI. Most respondents had links with workshops about trust and AI in Africa in Namibia and Ghana. Respondents' educational background, transnational mobility, and country of origin influenced their concerns about AI systems. These factors also affected their levels of distrust in certain AI applications and their emphasis on specific principles designed to foster trust. Respondents often expressed that their values are guided by the communities in which they grew up and emphasised communal relations over individual freedoms. They described trust in many ways, including applying nuances of Afro-relationalism to constructs in international discourse, such as reliability and reliance. Thus, our exploratory study motivates more empirical research about the ways trust is practically enacted and experienced in African social realities of AI design, use and governance.