AI in Hiring and Employee Evaluation: 'Human Review' Is Now a Legal Requirement, Not a Design Choice

CV screening, candidate scoring, performance evaluation and employee monitoring are among the enterprise areas where AI is spreading fastest. They are also where regulators are paying the most attention. In 2026 the shared message of the rules became clear: in decisions that affect people, AI's role must be explainable and there must be real human review.

US: four regimes in force, Colorado tightens

• In force: New York City's bias-audit rule (Local Law 144), California's automated-decision-system regulations, Illinois' amended Human Rights Act and Texas' Responsible AI Governance Act.
• Colorado's SB 26-189, signed on May 14, 2026, requires notice to individuals, an adverse-action and human-review process, and keeping records for at least three years for employment decisions materially influenced by AI.
• Colorado's proposed implementing rules would, from January 1, 2027, require explaining AI-driven decisions, tracing data sources and a meaningful human review with authority to override an adverse outcome.
• A key detail: in Colorado, involving a human does not by itself take a tool out of scope; California, by contrast, exempts organizations that conduct meaningful human review from certain obligations.

The EU and Türkiye

The EU AI Act treats AI systems used in recruitment, application filtering, candidate evaluation, task allocation, employee monitoring and promotion or termination decisions as high-risk. Organizations using them are expected to provide human oversight, keep logs and inform affected people; a deferral of these obligations is still being debated in the EU. In Türkiye, Article 11 of the KVKK gives individuals the right to object to an adverse outcome resulting from their data being analyzed exclusively by automated systems.

The common thread: four requirements

• Notice: the candidate or employee should know AI was used in the decision.
• Explainability: it should be possible to show what the AI recommended and based on which data.
• Real human review: the reviewer should have the authority and information to change the AI's result.
• Records: which model, which data and which human decision should be demonstrable years later.

The remot angle

Most of these rules come down to one question: how did AI contribute to this decision, and can you prove it? remot logs every model call, the data used and the result in hiring or evaluation processes, adds human-approval steps to the flow and masks personal data before it reaches a model. That way the decision process stays traceable and the records needed for an audit already exist.