Fairness | BitStage

Fair hiring by design

1Our principles

BitStage is built so candidates are evaluated on what they can do in a realistic rehearsal - and hiring teams can review the evidence behind every score. These principles guide product design, scoring architecture, and how we talk about AI on the platform.

Evidence over resumes

Candidates earn signal by doing the job in a short rehearsal - not by keywords, pedigree, or who they know.

Explainable AI

Every BitScore is accompanied by a breakdown and plain-language feedback on the behaviours and skills that contributed to the result.

Human-in-the-loop

BitStage does not make hiring decisions. Clients always review evidence and make the final call.

2What we do today

The commitments below are in place today across the BitStage platform. They are also reflected in our Privacy Policy and Security Policy.

No facial recognition

We do not perform facial recognition, faceprint matching, or voiceprinting for identification.

No protected-characteristic scoring

We do not detect or score on race, religion, gender identity, sexual orientation, disability, or other protected characteristics.

No score penalty for privacy choices

Choosing a more privacy-protective Consent Tier, such as Audio Only, does not affect score, application, or platform experience.

Separated scoring architecture

Identity data and behavioural scoring data are structurally separated so fairness analysis can run without accessing personal information.

Every score has a breakdown

BitScore outputs include plain-language feedback describing which behaviours and skills contributed to the result - so candidates and hiring teams can understand the evidence, not just the number.

3BiasAuditor

BiasAuditor is in development. We are building automated fairness audits as part of our roadmap - including pre-launch checks across demographic and accent patterns where lawful and feasible.

In development - no automated bias-detection claim yet

Until BiasAuditor is live, we do not claim any automated bias-detection capability. We are transparent about this limitation and will update this page when automated audits ship.

In the meantime, we periodically review models for disparate-impact patterns and make summaries available upon request, as described in Privacy Policy §13.

4Disputes and transparency

Candidates and Clients can dispute a BitScore or request an explanation of how an automated output was generated. BitStage will respond within thirty (30) days of a verified request.

Dispute a score: email info@bitstage.ai with the rehearsal or application reference.
Request audit summaries: fairness audit summaries are available upon request under the terms in our Privacy Policy.
Client responsibilities: Clients using BitStage in regulated jurisdictions remain responsible for applicable automated employment decision, notice, and bias audit laws in their region.

5Coming next

BiasAuditor automated pre-launch checks (in development).
Expanded fairness reporting and audit summaries as BiasAuditor ships.

6Contact

Questions about fairness, algorithmic transparency, or dispute handling?

Email info@bitstage.ai for algorithmic fairness and dispute requests, or visit the Trust Center for related policies.