Insights on algorithmic bias, AEDT compliance, and the evolving regulatory landscape.
A non-legal-jargon walkthrough of the law's requirements. What counts as an AEDT, who needs an audit, what the audit must include, and what happens if you don't comply.
An explanation of the statistical foundation behind adverse impact analysis, with worked examples. Where the rule comes from, how to calculate it, and what it means when you fail.
A preview of Colorado's requirements and what you need to do to prepare. How it differs from NYC LL144, and why you can't just copy your existing compliance program.
Transparency isn't optional in independent auditing. We explain why our methodology is public, what it includes, and how it differs from vendors who keep their approach proprietary.
An accessible explanation of how aggregate data can mask department-level disparities—and why every serious audit needs stratified analysis.
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Principal Auditor & Founder
Ph.D. in Sociology from The New School with specialization in computational social science and algorithmic bias. Published researcher at JSC, IEEE, and SSRN. Tyler writes about the intersection of AI, employment law, and statistical methodology.
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