Legal
AI Fairness Statement
Last Updated: April 28, 2026
Commitment
Bifalabs is committed to building AI-assisted hiring and workforce readiness tools that are explainable, reviewable, job-related, and designed to reduce rather than amplify unfair outcomes. Bifalabs recognizes that AI systems used in employment contexts can affect opportunity, mobility, and trust. The platform is therefore deployed with governance, oversight, documentation, and human accountability.
Design Principles
Bifalabs aims to design AI features around job-related criteria, consistent assessment workflows, transparent candidate notices, role-based access, explainable summaries, audit logs, and appropriate human review. AI outputs should be treated as signals that help users organize evidence, not as final judgments about a candidate's worth, identity, protected characteristics, or future potential.
Bias and Validation
Bifalabs tests models and assessment workflows for performance, reliability, and potential bias across relevant populations where sufficient data and lawful analysis are available. Customers should also validate that their job requirements, assessment criteria, thresholds, and decision processes are lawful and job-related.
Sensitive Attributes
Bifalabs does not intentionally use protected characteristics such as race, color, religion, sex, national origin, age, disability, genetic information, or other protected status as decision criteria. Some systems may process video, audio, language, or identity data for security and integrity, but those signals are limited to legitimate assessment purposes and governed carefully.
Transparency
Bifalabs provides customers with reasonable documentation about AI-assisted features, including intended use, limitations, relevant data categories, output types, and recommended human review. Candidates receive clear notices where AI, automated scoring, proctoring, or significant profiling is used.
Continuous Improvement
Bifalabs monitors model performance, user feedback, adverse events, complaints, technical failures, and changing legal requirements. Fairness is an ongoing governance process, not a one-time statement.
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