Explainability of AI

In the context of the Digital Personal Data Protection Act, 2023 (DPDP Act), Explainability of AI refers to the ability of organizations to understand how AI systems process data and generate outputs, particularly when those outputs may affect individuals. Explainable AI helps organizations document AI processing activities, improve transparency, identify risks, and establish accountability when artificial intelligence systems use personal data.

AI systems can process large volumes of personal data to generate predictions, recommendations, classifications, or automated outcomes. When organizations cannot understand how these systems operate, it becomes difficult to identify inaccurate data usage, unintended bias, privacy risks, or inappropriate processing practices. Explainability helps organizations maintain oversight by understanding data inputs, processing logic, model behavior, and factors influencing AI-generated outputs.

The DPDP Act does not specifically mandate AI explainability. However, organizations using AI systems that process personal data must ensure compliance with relevant privacy obligations, including transparency through notices, lawful processing, purpose limitation, and protection of Data Principal rights. Explainability can support responsible AI governance by improving accountability and enabling organizations to assess whether AI processing aligns with privacy expectations.

In practice, gaps emerge when:

• Organizations cannot explain how AI systems process personal data.
• AI decisions affecting individuals cannot be reviewed or understood.
• Data used to train AI models lacks proper governance documentation.
• AI outputs are accepted without validation or human oversight.
• Privacy teams lack visibility into AI systems deployed across the organization.

Organizations improve AI governance by maintaining AI inventories, documenting data flows, assessing privacy risks, implementing human oversight, and establishing accountability processes. Within Privy, capabilities such as data discovery, data classification, data mapping, privacy assessments, and governance workflows help organizations understand personal data usage across AI-enabled environments.