Algorithmic Bias and Ethical Challenges in AI-Driven Business Decision-Making: A Comparative Perspective Between the U.S. and Africa/MENA

Artificial intelligence (AI) is becoming a powerful tool in business decision-making, offering companies new ways to enhance efficiency, improve predictions, and automate tasks. However, as AI becomes more deeply integrated into business operations, concerns about algorithmic bias and ethical challenges continue to grow. AI models, trained on historical data, can sometimes reinforce biases, leading to unintended consequences in areas such as hiring, financial decision-making, and risk assessment. The way these issues play out differs significantly between developed economies like the United States and emerging markets in Africa and the MENA region. This paper explores how algorithmic bias affects decision-making in these two different contexts, focusing on key differences in regulations, corporate governance, and the availability of reliable data. In the U.S., businesses operate within established legal frameworks, such as the Equal Credit Opportunity Act and AI ethics guidelines from institutions like the National Institute of Standards and Technology (NIST), which encourage accountability and responsible AI use. On the other hand, in Africa and the MENA region, businesses face additional challenges such as limited access to high-quality local data, evolving regulations, and a dependence on AI models developed in Western contexts, which may not always align with local realities. These factors can create inefficiencies, reinforce existing inequalities, and make fair decision-making more difficult. Through case studies from both regions, this paper highlights the risks associated with AI-driven bias and offers practical recommendations for businesses and policymakers. The findings suggest that strengthening regulations, promoting locally relevant AI solutions, and emphasizing ethical AI practices can help organizations make better and fairer decisions in the long run.