What Is the Difference Between Disparate Impact and Disparate Treatment?
**Disparate Treatment** is intentional discrimination — treating candidates differently because of their protected characteristics (race, gender, age, etc.). Example: explicitly filtering out candidates over 50.
**Disparate Impact** is unintentional but systemic discrimination — using neutral criteria that disproportionately affect a protected group. Example: requiring a specific degree that fewer minority candidates hold, even though the degree is not necessary for job performance.
AI hiring bias audits primarily detect **disparate impact** because algorithms rarely discriminate intentionally but frequently produce disparate outcomes through:
- Training data reflecting historical hiring patterns
- Proxy variables that correlate with protected characteristics
- Feature selection that inadvertently encodes bias
The four-fifths rule is the primary test: if the selection rate for any group is less than 80% of the highest group rate, disparate impact may exist.
OnHirely detects both types through comprehensive statistical analysis.
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