Building Trust in Predictive Analytics: A Review of ML Explainability and Interpretability
Shruthi Sajid, Jeewanie Jayasinghe Arachchige, Faiza Allah Bukhsh, Abhishta Abhishta, Faizan Ahmed · International Journal of Computing Sciences Research · 2025
Purpose–The purpose of the manuscript is to explorethe previous literature to reveal the trust and interpretability of predictive analytical models that use ML /AI techniques. Method–The methodology applied for the studyis 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 sufferfrom 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 usagein 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 ontransparency and interpretability ofML modelsbydeveloping sector-specific explainability frameworks to bridge technical insights and business decisions. Further,it is recommendedto 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 significantgap in the literature explainability and interpretabilityofML and AI models in the business context. Therefore the research stresses the need for futureinvestigations into improving model transparency and creating industry-specific and ethical frameworksthathelp organizations derive more meaningful, trusted, and interpretable insights from data-driven models. Practical Implications–It should focus on improving transparency, trust, and collaboration in usingpredictive 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–Thisstudy highlightsthe importance of ethical and regulatory concerns related to AI and ML, such as data privacy, and fairness. Keywords–explainability, interpretability, machine learning, predictive analytics, trust