Published 01-10-2021
Keywords
- Explainable AI,
- Trust
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
Artificial Intelligence (AI) is transforming healthcare, finance, and law enforcement industries, driving efficiency and innovation while enabling data-driven decision-making. However, the increasing complexity of AI models often results in opaque decision-making processes, which undermine trust, accountability, and ethical adoption. Explainable AI (XAI) has emerged to address these concerns by making AI systems more interpretable and transparent, helping users understand how and why specific decisions are made. XAI bridges the gap between sophisticated algorithms and human understanding, employing techniques like feature importance analysis, model-agnostic approaches, interpretable models, and visualization tools to unravel AI’s decision logic. These methods ensure that critical applications—such as diagnosing diseases, approving financial loans, & detecting bias in law enforcement algorithms—are accurate but also fair and understandable. By providing clear, actionable insights, XAI empowers stakeholders, including non-technical users, to confidently make informed decisions. Despite its promise, implementing XAI poses significant challenges, including balancing interpretability with model accuracy, safeguarding sensitive data while maintaining transparency, and designing explanations that are accessible and meaningful to diverse audiences. Furthermore, achieving universal standards for explainability is complex due to variations in industry requirements and ethical considerations. This paper examines the foundations of XAI, exploring essential techniques, applications, and the challenges it must overcome to meet its potential. By enhancing the interpretability of AI models, XAI builds trust in AI systems, encouraging wider adoption & fostering accountability in critical sectors. As AI advances, explainability will be crucial for addressing ethical concerns, reducing bias, and ensuring compliance with regulatory frameworks, ultimately enabling more responsible and sustainable use of AI technologies.
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