Forbes has published an opinion piece arguing that algorithmic discrimination in hiring is not a novel harm created by AI tools but a continuation of biases long embedded in recruitment practice.

The piece, titled "AI Hiring Bias: The Workplace Problem AI Didn't Create," repositions the accountability conversation by suggesting AI inherits pre-existing flaws rather than generating new ones. For HR leaders weighing procurement of screening, ranking, or assessment tools, that framing carries immediate practical weight.

What does the inherited-bias argument change for employers?

It relocates where compliance and ethics scrutiny must land. Employers cannot treat algorithmic hiring as a clean technical layer separate from their own judgment. The same scrutiny applied to interviewer training, panel composition, and promotion reviews now extends to model selection, training data provenance, and ongoing audit cadence.

The framing cuts both ways. Vendors may use the argument to deflect responsibility for opaque model behavior, while employers may use it to defer hard questions about the tools they license. Neither outcome serves candidates, compliance teams, or the regulators who will eventually review those decisions.

Why does the AI layer still demand separate attention?

AI tools process applicants at volume and consistency that human screeners cannot match, which means a flawed process compounds faster once automated. A bias present in one out of a hundred interviewer decisions scales very differently when multiplied across tens of thousands of model inferences per hiring cycle.

The headline and excerpt reviewed here do not cite specific survey data, sample sizes, or named enforcement actions, so quantitative claims about prevalence remain unverified. HR teams should still require documented bias testing, model card disclosures, and ongoing post-deployment monitoring before signing procurement contracts.

What should HR leaders ask before adopting AI hiring tools?

• What population generated the training data, and how were labels assigned?
• How often does the vendor retest for disparate impact across protected categories?
• What appeal process exists when a candidate is filtered out by the model?
• Can the employer audit the model independently, or is that capability restricted?

The skeptical caveat applies here. The argument that AI did not invent bias does not absolve employers of the duty to test, disclose, and remedy harm in any system they choose to deploy. Outsourcing screening to a vendor does not outsource liability.

What the headline leaves unsaid

The Forbes headline commits to a thesis without, in the excerpt available, naming regulators, active lawsuits, or specific enforcement statistics that would ground the argument. Readers seeking jurisdictional specifics or numerical evidence will need the full text or independent verification from primary sources such as the EEOC or state civil rights agencies.

For HR teams, the practical implication is straightforward. Any AI-assisted hiring decision still carries the same legal exposure as a human one, plus additional questions about vendor liability, audit transparency, and data protection. The Forbes reframe offers a useful corrective to AI-doom narratives, but it does not narrow the employer obligation to scrutinize the tools now sitting inside the hiring funnel.