AI Transparency
How Our AI Works — No Black Boxes
We believe you deserve to know how decisions about your career are made. Here is a complete breakdown of our AI systems.
AI Transparency & Compliance
ZhiYin is committed to responsible AI use in recruitment. This page explains how our AI systems work, what safeguards are in place, and your rights.
How We Use AI
ZhiYin uses artificial intelligence to enhance — not replace — the recruitment process. Our AI systems assist with the following:
- Job-Candidate Matching: Analyzing skills, experience, and preferences to suggest relevant job opportunities
- Resume Analysis: Extracting skills, scoring qualifications, and suggesting improvements to help candidates strengthen their profiles
- Interview Preparation: Generating practice questions and providing feedback based on career tracks and job roles
- Salary Insights: Providing market salary estimates based on role, location, and experience level
What AI Does NOT Do
We have clear boundaries on how AI is used. Our AI systems do NOT:
- Make automated hiring decisions — all hiring decisions are made by human employers
- Perform biometric analysis, facial recognition, or physical appearance assessment
- Conduct emotion detection or sentiment analysis of candidates
- Score candidates based on protected characteristics such as age, gender, ethnicity, or disability
Human Oversight Guarantee
Every AI-generated suggestion, score, or recommendation on ZhiYin is advisory only. Employers make all hiring decisions independently. AI match scores are presented alongside full candidate profiles so employers can evaluate candidates fully. Candidates always see the complete job listing, not just AI-filtered results. Our team regularly reviews AI outputs to ensure quality and fairness.
Data Used for AI Processing
Understanding what data feeds our AI models and what is explicitly excluded:
Data Used by AI
- Resume text, skills, and work experience (as provided by candidates)
- Job descriptions, requirements, and preferences (as posted by employers)
- Career track and industry information for interview preparation
Data Never Used by AI
- Personal contact information (email, phone, address)
- Photos, profile pictures, or any visual appearance data
- Protected characteristics (age, gender, ethnicity, disability status, religion)
Right to Explanation
Candidates have the right to request an explanation of any AI-generated score or recommendation that affects them. To request an explanation, contact our Data Protection Officer at privacy@supply-mates.com. We will provide a clear, understandable explanation of the factors that contributed to the AI output within 30 days of your request.
Opt-Out Rights
Candidates can request to opt out of AI processing at any time by emailing privacy@supply-mates.com. Once your request is processed, your profile will be excluded from our AI matching algorithms and you will not receive AI-generated recommendations; you can still browse and apply to jobs manually. Opting out will not affect your account status or access to the platform.
Bias Prevention
We take active steps to prevent bias in our AI systems:
- Regular bias audits: We conduct periodic audits of our matching and scoring algorithms to detect disparate impact across demographic groups
- Protected attribute exclusion: Our AI models do not receive or process protected characteristics such as age, gender, ethnicity, or disability status
- Diverse training evaluation: We evaluate AI outputs across diverse candidate profiles to ensure consistent and fair treatment
EU AI Act Classification
Under the EU AI Act (Regulation 2024/1689), AI systems used in recruitment and employment are classified as "high-risk" (Annex III, point 4). We are building our practices toward the high-risk AI requirements — including a risk-management system, data governance and quality, transparency to users, human oversight, and technical documentation. Where the Act requires registration in the EU AI database (Article 49), we will complete registration before operating within its scope.
Algorithm Registry
Alongside this policy, we publish a registry of every algorithm in production use on ZhiYin — its purpose, transparency level, and latest bias-audit date — together with the matching factors and bias controls behind our recommendations.
View the algorithm registryContact Our Data Protection Officer
If you have questions about our AI systems, want to request an explanation of an AI decision, or wish to exercise your opt-out rights, please contact our Data Protection Officer.
Email: privacy@supply-mates.com
How Job Matching Works
We match candidates to jobs using a weighted multi-factor system. Every match score is explainable.
Skills Overlap
How closely your skills and certifications overlap with what each role requires.
Experience Relevance
How relevant your work history, roles, and years of experience are to the position.
Location & Salary Fit
Whether the job's location, remote options, and salary range fit your stated preferences.
These factors combine under a fixed, published two-sided formula: a candidate-fit score (skills 40%, experience 20%, verified evidence 25%, recent activity 15%) and a job-appeal score (salary fit 30%, location 25%, employer quality 25%, salary transparency 10%, freshness 10%), joined by harmonic mean so one-sided matches rank low, optionally blended with semantic text similarity. The score is always shown alongside the full job listing so you can judge for yourself.
Bias Prevention Controls
We actively monitor and prevent discriminatory outcomes in our AI systems.
4/5ths Rule Compliance (EEOC)
Selection rates for any protected group should reach at least 80% of the rate for the highest group — the fairness target we design our matching toward.
Quarterly Bias Audits
We are building a recurring internal review process to measure disparate impact across gender, age, ethnicity, and disability status, and aim to publish results as the program matures.
Diverse Training Data
We use general-purpose language models rather than training our own, and design our prompts and ranking signals to reduce cultural bias across regions and industries.
Human Review for Edge Cases
When AI confidence is low or outcomes appear anomalous, human reviewers intervene before any decision is finalized.
Algorithm Registry
All AI algorithms used by ZhiYin are registered and documented, compliant with China CAC and EU AI Act requirements.
Registered Algorithms
7 algorithms documented in our transparency registry, designed to align with CAC and EU AI Act requirements
| ID | Algorithm |
|---|---|
| ALG-001 | Job-Candidate Matching (two-sided reciprocal) Rank candidate–job pairs with a deterministic, fixed-weight TWO-SIDED formula: a candidateFit score (skills 40% via bilingual EN-ZH lexicon, experience 20%, verified evidence 25% — mock-interview/proctored-assessment/course/resume signals weighted by published predictive validity — and activity recency 15%) and a jobAppeal score (salary fit 30%, currency-gated; location 25%; employer quality 25% from reply rate, offer-acceptance rate and candidate satisfaction; salary transparency 10%; job freshness 10%), combined by harmonic mean so one-sided matches rank low, optionally blended 60/40 with multilingual text-embedding similarity. Feed ordering additionally applies aggregate-level congestion balancing (over-applied jobs demoted up to 12%, under-exposed jobs boosted up to 6%). The existing candidate-intent adjustment includes NOT_LOOKING at 0.85x. responsivenessScore is collected but not scored. Candidate-declared directionStatus, employmentContext, and searchUrgency are candidate-only planning context: they are never employer-visible and never ranking inputs. Birth date, marital status, hukou, other protected characteristics, and accommodation data are also excluded. Every score ships with a per-component breakdown and matched/missing-skill evidence; no LLM participates in scoring. Legal interpretation should be reviewed by qualified US and China counsel. |
| ALG-002 | Resume Analysis Extract and score skills, experience, and qualifications from resumes |
| ALG-003 | Cover Letter Generation Generate personalized cover letters based on candidate profile and job description |
| ALG-004 | Interview Question Generation Generate relevant interview questions based on job role and career track |
| ALG-005 | Salary Insights Provide salary range estimates based on role, location, and experience level |
| ALG-006 | AI Team Composition (team-compose-v1) Compose 3 ranked contractor teams from an employer brief by scoring candidates on skill overlap, rate fit, verification, availability, and past rating, then generating per-member rationale via LLM. |
| ALG-007 | Job Search Ranking Order public job-search results with a deterministic blend when a text query is present: text relevance (Postgres full-text rank or weighted keyword hits with bilingual alias and Chinese word-segmentation expansion) 55%, freshness (30-day exponential decay) 25%, and aggregate engagement (recency-weighted views and application conversion, computed daily, no per-user profiling) 20%. Employer responsiveness tier remains the primary ordering; chronically unresponsive employers rank below all others. No LLM participates; no individual user behavior is used. |
Privacy & Compliance
Our AI systems are designed to align with employment-discrimination laws and AI-governance regulations in the markets we serve.
EEOC / NYC LL144
We design our automated decision tools to support bias review in line with emerging US automated-employment-decision-tool rules.
EU AI Act
Recruitment AI is treated as high-risk under the EU AI Act, and we are building the Article 9 bias-monitoring and human-oversight practices it calls for.
CAC Algorithm Registry
Our recommendation and matching algorithms are designed to support the algorithm-registration requirements of the Cyberspace Administration of China.
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