Appendix C. Sources, Methodological Notes, and Legal Caveats
How to read the sources
This book is a practical handbook, not an academic review and not legal advice. The sources below are used as methodological support for careful wording: structured interviews, criteria-based assessment, human review, privacy, and non-discrimination.
Methodological sources
| Topic | Source / direction | How it is used in the book |
|---|---|---|
| Validity of selection methods | Schmidt & Hunter, 1998; research on validity and practical usefulness of selection methods | Supports the idea that assessment methods differ in reliability and should be tied to the role’s tasks. |
| Structured interviews | McDaniel et al., 1994; Campion et al., 1997; Google re:Work materials on structured interviews | Supports predefined criteria, consistent questions, and comparable candidate evaluation. |
| Assessment fairness and non-discrimination | EEOC materials, EU Equal Treatment Directive 2000/78/EC as a general context | Supports the ban on sensitive assumptions and the requirement for work-related criteria. |
| Personal data | GDPR as a well-known example of data regulation; local company policies | Supports the principle of data minimization, consent, storage periods, and access restrictions. |
| AI governance | NIST approaches to AI-risk management and human-verification practices | Supports the rule that AI can assist but must not make sensitive decisions without human review. |
HarmonyATS reference materials
| Topic | Where to look |
|---|---|
| Consent for personal-data processing | HarmonyATS documentation / personal data and consent section |
| Resume-source report | HarmonyATS / reports / resume sources |
| Rejection report | HarmonyATS / reports / rejection reasons |
| Candidate movement | HarmonyATS / reports / candidate movement |
| Detailed funnel | HarmonyATS / reports / detailed funnel |
| Time-to-hire | HarmonyATS / reports / time to hire |
| SLA report | HarmonyATS / reports / SLA |
| AI Studio and AI-Matching | HarmonyATS / AI Studio and AI-Matching |
Caveats
- Check the applicable labor, anti-discrimination, and privacy regulation in your jurisdiction.
- External feedback to a candidate depends on company policy, note quality, and legal context.
- The economic models in the book are management hypotheses, not a financial forecast or an ROI guarantee.
- AI outputs must not be used without checking facts, privacy, tone, and wording admissibility.
