Explainable AI (XAI): Bridging The Gap Between AI And Human Understanding

Artificial Intelligence (AI) is transforming industries, but its “black box” nature raises concerns about trust, fairness, and accountability. Explainable AI (XAI) aims to make AI models transparent, helping users understand why and how decisions are made.

Why Does XAI Matter?

1. Trust & Transparency – Users need confidence in AI-driven decisions, especially in healthcare, finance, and legal sectors.
2. Bias Detection – XAI helps identify and mitigate biases in machine learning models, ensuring fairness.
3. Regulatory Compliance – Laws like GDPR require AI models to provide explanations for decisions affecting individuals.

How XAI Works

XAI techniques include:

• Feature Importance Analysis – Identifying key factors influencing AI decisions.
• Model-Agnostic Methods – Tools like LIME (Local Interpretable Model-agnostic Explanations) explain black-box models.
• Rule-Based AI – Systems using decision trees or expert rules for clear logic.

The Future of XAI

As AI adoption grows, interpretable models will become the norm, ensuring ethical and responsible AI. Investing in XAI enhances user trust, regulatory compliance, and better decision-making.

By:
Dr.S.Balakrishnan,
Professor and Head,
Department of CSE,
AVIT

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