  This work investigates the potential of undermining both fairness and
detection performance in abusive language detection. In a dynamic and complex
digital world, it is crucial to investigate the vulnerabilities of these
detection models to adversarial fairness attacks to improve their fairness
robustness. We propose a simple yet effective framework FABLE that leverages
backdoor attacks as they allow targeted control over the fairness and detection
performance. FABLE explores three types of trigger designs (i.e., rare,
artificial, and natural triggers) and novel sampling strategies. Specifically,
the adversary can inject triggers into samples in the minority group with the
favored outcome (i.e., "non-abusive") and flip their labels to the unfavored
outcome, i.e., "abusive". Experiments on benchmark datasets demonstrate the
effectiveness of FABLE attacking fairness and utility in abusive language
detection.
