Building Trust in Predictive Analytics: A Review of ML Explainability and Interpretability - Sajid, Shruthi; Arachchige, Jeewanie Jayasinghe; Bukhsh, Faiza Allah; Abhishta, Abhishta; Ahmed, Faizan (International Journal of Computing Sciences Research; Vol 9 (2025): Volume 9, 2025)
Purpose   – The purpose of the manuscript is to explore the previous literature to reveal the trust and interpretability of predictive analytical models that use ML /AI techniques.   Method – The methodology applied for the study is the guidelines of Kitchenham et al. (2007). Results – The results reveal that past research explicitly discussed the usage of predictive analytics. However, ML models are considered black boxes and suffer from transparency.  The study proposed a typical process to ensure that predictions made by AI/ML models can be interpreted and trusted. Conclusion – The literature review conducted predictive analytics and AI/ML techniques in business decision-making, highlighting their usage in industries.  The study reveals a significant gap exists in research on the explainability and interpretability of these ML models within a business context. Recommendations – Recommended the need for more research on transparency and interpretability of ML models by developing sector-specific explainability frameworks to bridge technical insights and business decisions.  Further, it is recommended to integrate ethical and regulatory considerations into explainability frameworks and study collaboration methods between AI/ML experts and business leaders to align ML models with business goals. Research Implications – The research highlights the significant gap in the literature explainability and interpretability of ML and AI models in the business context.  Therefore the research stresses the need for future investigations into improving model transparency and creating industry-specific and ethical frameworks that help organizations derive more meaningful, trusted, and interpretable insights from data-driven models. Practical Implications – It should focus on improving transparency, trust, and collaboration in using predictive analytics. By addressing explainability issues and incorporating ethical, regulatory, and industry-specific considerations, businesses can more effectively use the power of AI and ML to drive data-informed decisions. Social Implications – This study highlights the importance of ethical and regulatory concerns related to AI and ML, such as data privacy, and fairness.