Shagun Sarraf
Papers
1
Total Citations
3
H-Index
1
About
Shagun Sarraf is a researcher at the forefront of eXplainable Artificial Intelligence (XAI), a critical field dedicated to making AI systems transparent, interpretable, and trustworthy. Her most notable work, "A Study of eXplainable Artificial Intelligence: A Systematic Literature Review of the Applications" (2023), provides a comprehensive synthesis of how XAI techniques are being deployed across diverse domains—from healthcare to finance—offering researchers and practitioners a clear roadmap of current methodologies and gaps. Though early in her career, this systematic review has already garnered 3 citations, signaling its value as a foundational reference for those navigating the rapidly evolving XAI landscape. Sarraf’s contribution lies in her ability to distill complex, fragmented research into actionable insights, helping bridge the gap between theoretical explainability and real-world application. Her work underscores a commitment to responsible AI, ensuring that as machine learning models grow more powerful, they remain understandable and accountable. For students and researchers entering the field of AI ethics and transparency, Sarraf’s review serves as an essential starting point—a testament to her role in shaping the discourse around human-centered artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1