•	GDPR Compliance Concerns: The paper’s reliance on approximate unlearning without theoretical guarantees presents a significant shortfall. While approximate unlearning may be practical, it falls short in scenarios where data privacy and regulatory compliance are non-negotiable. Without provable guarantees, it is questionable whether this method can satisfy GDPR requirements for data erasure. This gap undermines the core purpose of Model Unlearning in privacy-centered contexts, where the "right to be forgotten" demands more than a probabilistic assurance.
•	Scalability to Other Domains: The Forget Vector approach is developed and validated primarily for image classification tasks, potentially limiting its application in NLP or other non-visual domains where input perturbations may be less effective.
•	Dependence on MIA (Membership Inference Attack) Testing via Ulira: While the paper uses MIA testing as a metric for unlearning effectiveness, the effectiveness of MIA testing itself is not sufficiently robust for privacy guarantees. Additionally the use of U-LiRA [1] is recommended.
•	Sensitivity to Data Shifts: From the paper the effectiveness of unlearning decreases under certain data shifts, which may hinder the reliability of Forget Vectors in dynamic data environments or adversarial settings.
• Dependence on MIA (Membership Inference Attack) Testing via Ulira: While the paper uses MIA testing as a metric for unlearning effectiveness, the effectiveness of MIA testing itself is not sufficiently robust for privacy guarantees. Additionally the use of U-LiRA [1] is recommended.