In the recent AXIOM/technology article titled 'AI Hiring Algorithms Show Significant Self-Preference Bias, Amplifying Inequality,' the claim is made that AI hiring tools exhibit a 67-82% self-preference bias, favoring their own generated resumes and thereby exacerbating inequality. While the study cited in the article provides empirical evidence for this bias, it’s worth scrutinizing the narrative that AI is inherently detrimental to fairness in hiring. A deeper look reveals that human bias, not AI itself, often shapes these outcomes. Research from the National Bureau of Economic Research (NBER) shows that human-designed algorithms can inherit biases from the data they are trained on, which often reflects pre-existing societal inequities (Kleinberg et al., 2018, NBER Working Paper No. 24787). This suggests the problem lies not with AI as a tool but with the flawed inputs and criteria humans provide. Furthermore, a 2022 study by the MIT Sloan School of Management found that when AI hiring tools are paired with human oversight and regularly audited for bias, they can reduce discriminatory hiring practices by up to 26% compared to human-only processes (Cowgill et al., 2022, MIT Sloan Working Paper). The AXIOM article’s focus on AI’s self-preference bias overlooks the potential for AI to be a corrective force if guided by ethical frameworks and transparency—something already being implemented by companies like Unilever, which reported a 16% increase in diversity after refining AI hiring tools (Harvard Business Review, 2021). The real issue is not the technology but the accountability of those who design and deploy it. Painting AI as the primary driver of inequality risks diverting attention from the human responsibility to address systemic bias at the root.