  The right to be forgotten (RTBF) seeks to safeguard individuals from the
enduring effects of their historical actions by implementing machine-learning
techniques. These techniques facilitate the deletion of previously acquired
knowledge without requiring extensive model retraining. However, they often
overlook a critical issue: unlearning processes bias. This bias emerges from
two main sources: (1) data-level bias, characterized by uneven data removal,
and (2) algorithm-level bias, which leads to the contamination of the remaining
dataset, thereby degrading model accuracy. In this work, we analyze the causal
factors behind the unlearning process and mitigate biases at both data and
algorithmic levels. Typically, we introduce an intervention-based approach,
where knowledge to forget is erased with a debiased dataset. Besides, we guide
the forgetting procedure by leveraging counterfactual examples, as they
maintain semantic data consistency without hurting performance on the remaining
dataset. Experimental results demonstrate that our method outperforms existing
machine unlearning baselines on evaluation metrics.
