Explainable AI: Bridging AI Systems and User Trust
Word count: 3000 words
Objectives to cover:
- Introduction: Overview of the role of Explainable AI in fostering trust in modern AI systems.
- Challenges in AI Trust: Examining the barriers to building user confidence in AI-powered technologies.
- Significance of Explainability: Highlighting the importance of transparency in enhancing AI system trustworthiness.
- Research Objectives: Defining the study’s goals, scope, and intended contributions to the field.
- Historical Context: Exploring the evolution of Explainable AI and its impact on trust development.
- Techniques in XAI: Overview of core methods like SHAP, LIME, and their practical applications.
- Empirical Insights: Presentation of data analysis, key findings, and real-world implications.
- Discussion and Ethics: Addressing the ethical considerations and practical challenges in XAI deployment.
- Conclusion: Summarizing findings and offering recommendations for advancing XAI and user trust.
Reference: IEEE style
The post Explainable AI: Bridging AI Systems and User Trust first appeared on Krita Infomatics.
The post Explainable AI: Bridging AI Systems and User Trust appeared first on Krita Infomatics.