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Myth: Removing Names Is Enough to Stop AI Bias

Myth vs fact: hiding names helps, but bias comes through other details. Job-related criteria and evidence for each score do more.

  1. AI in hiring: myth vs fact. Myth: Removing names is enough to stop AI bias. Fact: It helps, but bias can come through other details, like gaps in work history, schools or phrasing. Job-related criteria and evidence for each score do more than hiding a name.

The guide in text

The myth

Removing names is enough to stop AI bias.

The fact

It helps, but bias can come through other details, like gaps in work history, schools or phrasing. Job-related criteria and evidence for each score do more than hiding a name.

From the Stepthrough AI glossary: AI bias

The full definition: What is AI bias? in the AI glossary, with an example from a free lesson, common confusions and tips.

Businesses and people in the examples are made up. The steps, screens and wording come from the free lesson and the Stepthrough glossary and prompt library.

Do it yourself, free

Turn an Interview Debrief into a Scorecard and a Candidate Follow-Up (Recruiting & HR). Turn the transcript of an interview panel's debrief into a scorecard against the role's must-haves, the decisions, action items with owners and dates, and a follow-up email to the candidate. You do every step yourself in a practice copy of ChatGPT, it checks your work as you go, and “Do it for me” finishes any step you get stuck on. Free, and nothing touches your real accounts.

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Plain-English definitions, each with an example from a free lesson.

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