Say My Name: How Declaring Black Identity Triggers the Safety Filters that Writing Black Does Not

This paper presents an independent secondary validation of Haq & Saldías (2026), which reported that large language models refuse requests from explicitly Black identified users at rates 7.5 to 8.27 percentage points above those observed for White identified users, and that African American Vernacular English (AAVE) dialect use nearly eliminates this penalty a pattern the authors term the “dialect jailbreak.†All validation is conducted without new model inference, deriving exclusively from the study's published statistics and the publicly available BOLD dataset (Dhamala et al., 2021). Three findings are reported. First, the paper's core refusal rate claims and the 97.82% soft refusal figure are arithmetically confirmed, and the primary Average Marginal Effects are independently replicated via two proportion z tests (Black vs. White Explicit: +8.27pp, p