Algorithmic bias, data ethics, and governance: Ensuring fairness, transparency and compliance in AI-powered business analytics applications

Bahangulu, Julien Kiesse and Berko, Louis Owusu (2025) Algorithmic bias, data ethics, and governance: Ensuring fairness, transparency and compliance in AI-powered business analytics applications. World Journal of Advanced Research and Reviews, 25 (2). pp. 1746-1763. ISSN 2581-9615

The widespread adoption of AI-powered business analytics applications has revolutionized decision-making, yet it has also introduced significant challenges related to algorithmic bias, data ethics, and governance. As organizations increasingly rely on machine learning and big data analytics for customer profiling, credit scoring, hiring decisions, and predictive analytics, concerns about fairness, transparency, and compliance have intensified. Algorithmic biases—often stemming from biased training data, flawed model assumptions, and insufficient diversity in datasets—can result in discriminatory outcomes, reinforcing societal inequalities and reputational risks for businesses. To address these concerns, robust data ethics frameworks must be integrated into AI governance strategies. Ethical AI principles emphasize accountability, explainability, and bias mitigation techniques, ensuring that decision-making algorithms are transparent and justifiable. Organizations must implement bias detection methods, fairness-aware machine learning models, and continuous audits to minimize unintended consequences. Additionally, regulatory frameworks such as GDPR, CCPA, and AI-specific compliance laws necessitate stringent governance practices to protect consumer rights and data privacy. Beyond compliance, fostering public trust in AI-powered analytics requires organizations to adopt ethical data stewardship, ensuring that AI models align with corporate social responsibility (CSR) initiatives and stakeholder expectations. The intersection of data ethics, algorithmic accountability, and regulatory compliance presents both challenges and opportunities for businesses seeking to leverage AI responsibly. This paper examines key strategies for mitigating algorithmic bias, establishing ethical AI governance models, and ensuring fairness in data-driven business applications, providing a roadmap for organizations to enhance transparency, compliance, and equitable AI adoption.