Deploying Explainable AI (XAI) faces challenges such as balancing accuracy with transparency, addressing linguistic and socio-economic diversity in India, and managing trade-offs between transparency, security, and performance.

AI is increasingly impacting critical sectors like governance, healthcare, finance, policing, and education, making transparency and accountability essential as decisions directly affect human lives.

While the European Union's GDPR grants a right to explanation for automated decisions, India currently lacks similar legal safeguards in its laws, including the Digital Personal Data Protection Act of 2023.

High-profile cases like Apple Card bias against women, racial bias in the COMPAS algorithm, and errors in India’s Aadhaar-linked welfare schemes highlight the risks of non-transparent AI systems.

Explainability in AI is crucial to uphold legal principles such as due process, natural justice, and non-discrimination, enabling individuals to contest or seek remedies for biased decisions.

India has a unique opportunity to lead with a rights-based, inclusive AI framework that balances innovation with democratic values to build public trust.

The main issue with many AI systems is the prevalence of 'black box' algorithms that are complex and opaque, making it difficult to understand, audit, or challenge their decisions.

Explainable AI (XAI) seeks to improve transparency through techniques like model-specific methods and tools such as LIME and SHAP, which interpret complex models.

Full transparency raises concerns about exposing proprietary algorithms and vulnerabilities, especially in surveillance and fraud detection systems.

Effective solutions include sector-wide collaboration, mandatory Algorithmic Impact Assessments, public audits, transparency registers, redress mechanisms, and increasing AI literacy among citizens.

Defining the target audience for explanations—whether experts, regulators, or the public—is complex in India’s diverse context and requires balanced regulations to prevent oversimplification or opacity.

The 2015 incident involving Amazon’s AI recruiting tool, which perpetuated gender bias and was discontinued, underscores the importance of embedding fairness and ethical considerations in AI development.