The ethical role of Native-Centric algorithm decolonizing algorithmic bias in Artificial Intelligence

AI algorithms can analyze vast amounts of data quickly and accurately, leading to insights and predictions that can drive decision-making and improve processes. As AI continues to advance, it has the potential to revolutionize world history, industries and transform the way we live and work. However, the economic nuances of African societies have led to algorithmic bias. Historically, algorithmic bias arises when an AI system produces systematically discriminatory outcomes due to flawed assumptions or biases in the training data used during machine learning. Therefore, there is a need for the development of algorithms that are tailored to the specific needs and characteristics of African populations. Additionally, there is a need for greater transparency and accountability in the development and deployment of AI systems to ensure that they do not perpetuate biases or discrimination in credit scoring. By addressing these challenges, Africa can harness the full potential of algorithm driven solutions. By incorporating African native-centric algorithms into artificial intelligence (AI) systems, the aim is to decolonize ethical issues that have often been overlooked in the mainstream of AI development for credit scoring.  The research adopts a philosophical method of analysis. The integration of African native-centric algorithms can help address biases and promote more equitable outcomes in AI applications. By incorporating local perspectives and values into algorithm design, policymakers can ensure that AI technologies are more culturally sensitive and responsive to the needs of diverse populations. This approach can also help build trust and acceptance of AI systems among communities in Nigeria and beyond. However, the incorporation of African native-centric algorithms in the development of AI algorithms is challenged by cultural individualism. User-centric interface design in the management of data and computations is necessary to train and accommodate cultural values. Despite these challenges, the study concludes that incorporating African worldviews in AI algorithms has the potential to enhance effectiveness and the development of indigenous knowledge in the global AI race.