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Where automation enters the hiring process

Different tools can affect which job ads you see, whether your application is eligible, and how it reaches a reviewer.

Where automation enters hiring

Automated decisions can happen before you apply, including when a platform chooses who will see a job ad.1

Before a person reviews the resume

Bias can enter earlier than the human review.

Data, rules, and automated filters can all affect who appears in the final group.

FIG 01
  1. Sourcing
    Who sees the role

    Ad delivery can affect who learns about the opening.

  2. Parsing
    What gets extracted

    A parser can miss or misread information in some layouts.

  3. Ranking
    Who gets prioritized

    Using gaps, schools, or titles as shortcuts can repeat past biases.

  4. Assessment
    Who advances

    Automated scoring can make a narrow definition of fit look objective.

Earlier filters matter. A human reviewer may only see the candidates who made it through every previous step.
Fig. 1 / Before the human reviewReviewing the final shortlist alone can miss bias introduced earlier.

One hiring process, several decision points

The Upturn report examines job advertising, sourcing, screening, assessment, and ranking. Automated decisions can affect each stage. Someone who never sees a job ad may never learn about the opportunity.1

Once you have an opportunity to apply, check that your file contains readable text and clear sections. That addresses a possible parsing problem, not the broader risks documented in the report.2

When the system changes who gets seen

A system that decides who sees a job ad or appears in a search can change who receives attention. Candidates left out may still find another route to the employer, but the initial decision can limit their opportunity.1

Common questions

Where does bias enter the hiring funnel?

Bias can enter at ad delivery, eligibility screening, ranking, assessment, and human review. An early decision can change who is available for every later stage.

Can a resume fix algorithmic bias?

No. Making a resume easier to read does not remove bias from the tools or decisions an employer uses.

What should candidates do?

Candidates cannot fix systemic bias. They can keep the file readable and make relevant experience explicit so avoidable parsing failures do not become another problem.

How this shows up in your report

01

Check the resume text

We review the text you submit and point out details that are missing or unclear. We cannot inspect the employer's advertising or screening tools.

02

Separate fact from folklore

We separate what public research documents from what private platforms do not disclose.

Sources and related research

Sources

  1. 01Help Wanted: An Examination of Hiring Algorithms, Equity, and Bias — Upturn (2018).
  2. 02Unsuccessful resume parse — Greenhouse Support (2026).

Related research

ATS MythsIndustryHuman vs. AlgorithmTrustHiring DiscriminationResearch

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