The Right to Be Forgotten in the Digital Age: Challenges and Omissions in the Digital Personal Data Protection Act, 2023
Bibliographic record
Abstract
The Right to Be Forgotten (RTBF) has emerged as a pivotal privacy right in the digital era, yet India’s Digital Personal Data Protection Act, 2023 (DPDPA), lacks explicit provisions for its implementation. This paper examines the DPDPA’s approach to data privacy, focusing on its omission of a robust RTBF framework, and identifies associated legal, technical, and enforcement challenges. Employing doctrinal legal research and comparative analysis with the EU’s GDPR, the study evaluates gaps in the DPDPA, particularly its vague data erasure provisions (Section 8) and reliance on judicial interpretation. It argues that the absence of clear RTBF mechanisms undermines individual autonomy and fails to address the needs of vulnerable groups seeking data removal. The paper proposes legislative amendments to incorporate explicit RTBF provisions, strengthen the Data Protection Board’s role, and enhance public awareness. By addressing these omissions, India can align its data protection framework with global standards, ensuring greater privacy rights in the digital age.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".