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Individual privacy gets into AI tangle: Delhi HC ruling opens a debate

06 Jul 2026
2 min

Delhi High Court Ruling on "Right to be Forgotten"

The Delhi High Court's recent judgment asserts that the "right to be forgotten" is integral to an individual's fundamental right to privacy. This has introduced significant implications for artificial intelligence (AI) in terms of data privacy and regulatory frameworks.

Case Background

The case of "Laksh Singh Yadav versus Union of India and others" allows individuals to request the removal or masking of personal information from online judicial records if it harms their privacy, dignity, or reputation.

Challenges with AI Systems

  • AI systems present challenges in implementing this ruling, as it is technically difficult for AI to "unlearn" data once it has been incorporated into a model.
  • Data may be erased from systems, but the insights derived from that data can remain embedded within AI models.
  • Information in AI models is absorbed into complex systems and is not merely stored in databases, complicating data erasure.

Future Implications

  • Legal experts suggest focusing on preventing the reproduction, inference, or amplification of personal information in AI systems post-erasure requests.
  • AI companies are encouraged to implement output-level filters, maintain compliance records, and explore machine unlearning.

Technological and Legal Developments

  • Machine unlearning, which aims to diminish a model's reliance on specific data, is under development but is not yet fully reliable.
  • The ruling highlights potential gaps between existing data protection laws and broader constitutional privacy protections.
  • The Digital Personal Data Protection (DPDP) Act excludes certain publicly available information, raising concerns about adequacy in terms of privacy.

Balancing Act

Experts emphasize the need to balance privacy rights with freedom of expression, public accountability, research, and innovation.

Implementation and Enforcement Challenges

  • Enforcement is challenging as regulators may find it difficult to verify if a model was trained with personal data or if an erasure request is effectively implemented.
  • The ruling suggests a shift in privacy law focus from data deletion to defining digital memory limits.

Future Debate

The future discourse is expected to emphasize accountability rather than perfect deletion, with the real test being AI's ability to "forget" learned information.

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Output-level filters

Technical measures implemented in AI systems to prevent the reproduction, inference, or amplification of personal information after an erasure request has been made. These filters aim to control what information the AI system outputs, even if the underlying data is not perfectly deleted.

Digital Personal Data Protection (DPDP) Act

An Indian law enacted to protect the personal data of individuals, outlining the obligations of data fiduciaries and the rights of data principals regarding the processing of their personal information.

Machine Unlearning

A technique that allows for the removal of specific data from a trained AI model, addressing privacy concerns and the 'right to erasure' by ensuring that certain information can be forgotten by the AI.

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