AI-Driven Predictive Analytics in Monitoring and Evaluation: Opportunities and Ethical Dilemmas

Abstract

The integration of Artificial Intelligence (AI) into Monitoring and Evaluation (M&E) frameworks represents a significant transformation in how organizations assess program effectiveness and impact. This mixed-methods study examined the opportunities and ethical challenges associated with AI-driven predictive analytics in M&E through systematic literature review, case study analysis, and stakeholder consultation. The systematic review analyzed 89 peer-reviewed articles from 2015-2024, while three detailed case studies from Kenya, Colombia, and Southeast Asia provided implementation insights. Semi-structured interviews with 24 M&E practitioners, technology specialists, and ethics experts informed the analysis. Results demonstrated substantial improvements in program targeting (60% increase in effectiveness), resource allocation (30% cost reduction), and predictive accuracy (85-92% across contexts). However, significant ethical challenges emerged, including algorithmic bias affecting 67% of implementations, data privacy concerns in 78% of cases, and accountability gaps in 85% of current implementations. The study concludes with evidence-based recommendations for responsible AI integration in M&E, emphasizing phased implementation, robust governance frameworks, and continuous stakeholder engagement to maximize benefits while addressing ethical concerns.