Explainable AI in Data Science - Enhancing Model Interpretability and Transparency
Praveen Thunki- Mohan Raparthi
- Sarath Babu Dodda
Published 08-04-2021
Keywords
- Explainable AI,
- Model Interpretability,
- Transparency,
- Data Science,
- Decision-making
- Bias Mitigation,
- Regulatory Compliance ...More
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
Explainable Artificial Intelligence (AI) is gaining prominence as a critical component of data science, particularly in contexts where decision-making is complex and impacts are significant. This paper explores the role of explainable AI in enhancing model interpretability and transparency, essential for building trust and understanding in AI systems. We discuss various methods and techniques used to achieve explainability, including model-agnostic approaches, post-hoc explanations, and interpretable models. Through a comprehensive review, we highlight the importance of explainable AI in facilitating human understanding, error detection, bias mitigation, and regulatory compliance. By improving the transparency of AI models, organizations can make better-informed decisions and enhance the adoption of AI technologies across diverse domains.
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