How Riskiy Works · Step 2
Riskiy separates raw data, extracted features, and learned risk factors so observations stay neutral and auditable — never conclusions about people.
Riskiy keeps three things distinct everywhere: raw data, extracted features, and learned risk factors. Features are neutral observations — tenure, prior industry experience, schedule fit, certification match — that recruiters can review and correct.
What improving it meansImproving fairness means describing job-related facts, not labeling people.
How Riskiy supports itExtracted features are neutral observations kept separate from any score or rule; the product never says a type of person is bad.
What improving it meansImproving accuracy means letting recruiters fix what parsing got wrong.
How Riskiy supports itFeature corrections are saved, marked as recruiter-corrected, and recalculate neutral tenure and experience context.
What improving it meansImproving compliance means never collecting or inferring protected traits or demographic proxies.
How Riskiy supports itFeatures exclude race, gender, age, religion, disability, and similar attributes by design, including name- and neighborhood-based proxies.
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