What Gets Audited

An algorithmic hiring audit typically covers:

• Resume screening and matching algorithms

• Candidate ranking and scoring models

• Video interview and assessment AI

• Skills and personality inference systems

• Automated rejection logic

• Decision-support features embedded in ATS platforms

• Third-party vendor AI integrations

We audit system behavior, not marketing claims.

Why Algorithmic Audits Are Necessary

Most organizations assume compliance because they purchased reputable tools. In practice, risk emerges at the system level, not the product level.

Common audit findings include:

• Models trained on biased historical data

• Black-box decision logic no one internally understands

• Automation without documented human oversight

• Inconsistent application of evaluation criteria

• No clear accountability for outcomes

• No ability to explain or defend decisions

Without independent audits, these failures remain invisible.

The Real Risk

Algorithmic hiring systems fail in ways that are:

• silent (no obvious error signals)

• scalable (small flaws multiply at volume)

• distributed (no single owner)

• difficult to reverse once deployed

By the time risk becomes visible, it usually arrives through:

• legal challenge

• regulatory inquiry

• internal investigation

• public exposure

Audits exist to prevent that moment.

What an Audit Produces

A defensible algorithmic hiring audit produces:

• documented system risk profile

• bias and impact analysis

• governance and accountability gaps

• cybersecurity and data risk review

• workforce and labor compliance exposure

• regulatory defensibility assessment

• practical remediation roadmap

Not just findings.
Operational consequences.