Disparate Impact vs Disparate Treatment in Algorithms

Disparate treatment and disparate impact are distinct legal theories. Under Title VII, disparate treatment generally involves an employment decision motivated by a protected characteristic. Disparate impact concerns a facially neutral practice that causes a significant adverse effect on a protected group, even without discriminatory intent. A claim ordinarily identifies the practice causing the impact; the employer may then need to show it is job-related and consistent with business necessity, and a less discriminatory alternative may remain relevant. The precise standards depend on the statute and context.

Algorithms do not change the basic distinction. If a model directly uses race or age to disadvantage applicants, the facts may support a treatment theory. A model that omits protected attributes can still create disparate impact through correlated features or an uneven selection process. Intentional choice of a proxy to reproduce protected-class differences may be evidence of motive, but a proxy’s mere statistical correlation does not automatically establish disparate treatment. Courts and agencies assess the complete facts, law, and decision process.

The cases can also overlap. A model may be trained on biased historical decisions, selected using features that correlate with protected status, and applied in a way that has measurable adverse effects. Evidence may include feature-selection records, validation results, selection rates, communications, overrides, and the actual outcomes by group. Whether a disparity is legally actionable depends on causation, the applicable burden-shifting framework, defenses, and the jurisdiction.

For an AI review, separate questions about motive from questions about effects. Preserve why features and thresholds were chosen, identify the specific step causing a disparity, measure outcomes, and examine job relevance and alternatives. Avoid claiming that excluding race ends the analysis or that any statistical gap proves unlawful discrimination. A model audit informs legal review but does not replace it.