Ravi’s Story: A Missed Opportunity

Ravi, a first-generation college graduate from a small town, applied for a data analyst role at a fast-growing tech startup. He had stellar grades, a portfolio of real-world projects, and excellent recommendations.

But he never made it past the first round.

Later, he learned that the company’s resume screening tool used a model that favored candidates from certain “feeder schools” and flagged nontraditional education paths as “low relevance.”

Bias Insight:

• Ravi’s résumé wasn’t weak—it just didn’t fit the AI’s narrow view of success.

• The model replicated elite hiring patterns from past data.

• It failed to recognize diverse pathways into tech.

Why It Matters

When hiring algorithms filter based on prestige or past hiring decisions, they risk reinforcing exclusion—locking out capable people from historically underrepresented backgrounds.

FairFrame AI builds transparency into these systems. We help organizations audit resume filters and retrain models using fairness criteria so they don’t just mirror the past—they build for a more inclusive future.

A FairFrame Future

If we want a diverse, innovative workforce, we must rethink how we assess merit. AI should expand opportunity—not shrink it.

FairFrame AI is committed to identifying and correcting hidden bias in hiring platforms—because talent comes in many forms, and equity starts with access.