Navigating algorithm bias in AI: ensuring fairness and trust in Africa.

Frontiers in research metrics and analytics Pub Date : 2024-10-24 eCollection Date: 2024-01-01 DOI:10.3389/frma.2024.1486600
Notice Pasipamire, Abton Muroyiwa
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Abstract

This article presents a perspective on the impact of algorithmic bias on information fairness and trust in artificial intelligence (AI) systems within the African context. The author's personal experiences and observations, combined with relevant literature, formed the basis of this article. The authors demonstrate why algorithm bias poses a substantial challenge in Africa, particularly regarding fairness and the integrity of AI applications. This perspective underscores the urgent need to address biases that compromise the fairness of information dissemination and undermine public trust. The authors advocate for the implementation of strategies that promote inclusivity, enhance cultural sensitivity, and actively engage local communities in the development of AI systems. By prioritizing ethical practices and transparency, stakeholders can mitigate the risks associated with bias, thereby fostering trust and ensuring equitable access to technology. Additionally, the article explores the potential consequences of inaction, including exacerbated social disparities, diminished confidence in public institutions, and economic stagnation. Ultimately, this work argues for a collaborative approach to AI that positions Africa as a leader in responsible development, ensuring that technology serves as a catalyst for sustainable development and social justice.

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引导人工智能中的算法偏见:确保非洲的公平与信任。
本文从非洲的角度阐述了算法偏见对信息公平性和人工智能(AI)系统信任度的影响。作者的个人经历和观察,结合相关文献,构成了本文的基础。作者说明了为什么算法偏见在非洲构成了巨大挑战,特别是在人工智能应用的公平性和完整性方面。这一观点强调了解决偏见的迫切需要,因为偏见会损害信息传播的公平性,破坏公众信任。作者主张实施促进包容性的战略,提高文化敏感性,并让当地社区积极参与人工智能系统的开发。通过优先考虑道德实践和透明度,利益相关者可以降低与偏见相关的风险,从而促进信任并确保公平获取技术。此外,文章还探讨了不作为的潜在后果,包括加剧社会差距、降低对公共机构的信心以及经济停滞。归根结底,这项工作主张对人工智能采取合作的方法,将非洲定位为负责任发展的领导者,确保技术成为可持续发展和社会正义的催化剂。
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CiteScore
3.50
自引率
0.00%
发文量
0
审稿时长
14 weeks
期刊最新文献
Navigating algorithm bias in AI: ensuring fairness and trust in Africa. The ethics of knowledge sharing: a feminist examination of intellectual property rights and open-source materials in gender transformative methodologies. Complexity and phase transitions in citation networks: insights from artificial intelligence research. Designing measures of complex collaborations with participatory, evidence-centered design. Patent data-driven analysis of literature associations with changing innovation trends.
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