Evaluating Human Interpretability of AI-Driven Data Science Models in Non-Technical Contexts

1. Introduction 1.1 Background of the Study 1.2 Research Problem 1.3 Objectives of the Study 1.4 Significance of the Study 2. Literature Review 2.1 AI and Data Science Models 2.2 Interpretability in Machine Learning 2.3 Human Interpretability Challenges 2.4 Contexts for Non-Technical Users 3. Theoretical Framework 3.1 Models of Interpretability 3.2 Cognitive Load Theory 3.3 Usability and User Experience 4. Methodology 4.1 Research Design 4.2 Data Collection Methods 4.3 Sampling Techniques 4.4 Analysis Approach 5. Case Studies 5.1 Case Study Selection 5.2 Evaluation Metrics 5.3 Results of Case Studies 6. Evaluation Criteria 6.1 Criteria for Human Interpretability 6.2 Comparison with Technical Contexts 6.3 Tools and Techniques for Evaluation 7. Results and Discussion 7.1 Interpretation of Findings 7.2 Implications for AI Models 7.3 Limitations of the Study 8. Conclusion 8.1 Summary of Key Findings 8.2 Recommendations for Practice 8.3 Directions for Future Research