Building Trust with Explainable AI

Objectives to cover:

• Introduction: Overview of AI adoption and the need for customer trust through explainable systems.
• Importance of Explainability: Significance of transparency and clarity in AI decision-making processes.
• Theoretical Framework: Relationship between explainability, transparency, and customer trust.
• Literature Review: Insights from prior studies on XAI and its role in trust-building.
• Case Studies: Examples of successful XAI implementations and lessons learned.
• Research Methodology: Design, data collection, and metrics for evaluating trust and explainability.
• Empirical Findings: Analysis of how XAI features influence customer trust across industries.
• Discussion: Implications, limitations, and opportunities for enhancing XAI approaches.
• Conclusion: Summary of findings and actionable recommendations for AI product developers.

Reference:  IEEE style