PERSPECTIVE · AI, DATA STANDARDS & SKILLS-BASED HIRING
Better Hiring Needs
Evidence People Can Understand.
Connect clear requirements, portable records, and human context.
AI and data standards can support a more useful hiring conversation when the work is clear, the evidence has context, and people can question the interpretation.
Originally published September 21, 2023; revised September 13, 2026. This article presents Gobekli’s approach and intended product workflows. See current product availability.
Both sides are trying to explain something complex.
The employer describes the work
A job description needs to explain responsibilities, conditions, and what preparation matters.
The applicant describes experience
An application needs to show contribution, context, and the basis for relevant claims.
If either account is vague, a fast comparison can still be a poor comparison. Our starting point is to improve the information on both sides before deciding what AI should do with it.
Three contributions. Three different limits.
| Component | Useful role | What it does not settle |
|---|---|---|
| Data standards | Support agreed structures for expressing and exchanging information. | Whether a requirement is relevant or a record is sufficient for the job. |
| AI assistance | Help structure accounts, surface possible connections, and organize questions. | Whether an inference is correct or a person should be hired. |
| Human review | Examine evidence, question assumptions, and take responsibility for decisions. | Fairness cannot be assumed simply because a person is involved. |
Begin with a requirement someone can explain.
Before requesting evidence, the hiring team clarifies the actual task and conditions. Which preparation is needed at entry? What can be learned with support? What examples would help evaluate it?
A vague requirement
“Excellent project-management skills.”
A reviewable question
“Can you describe coordinating a delivery with changing dependencies, including how you communicated a delay?”
The second version gives the applicant a clearer way to respond. Reviewers still need agreed criteria for interpreting the response and assessing any remaining requirements.
A skill label starts a question.
Experience helps answer it.
Keep the task, contribution, source, and uncertainty connected.
Follow one claim into a better conversation.
Consider an illustrative applicant who coordinated a community event. Their experience might be relevant to a delivery-coordination role, but the connection needs examination.
The claim
“I coordinated the event’s suppliers and schedule.”
The context
What was the scale, what changed, and what responsibility did the applicant hold?
The supporting material
An authorized work example, attributed feedback, or a specific account of the actions taken.
The review question
How does that experience relate to the new role—and what is different?
AI could help organize this account or suggest a possible connection. The applicant checks its accuracy; the reviewer examines the underlying example. Neither the label nor the AI suggestion proves readiness.
People may bring relevant experience from employment, independent work, education, volunteering, or other settings they choose to discuss. The assessment still needs to relate to the work.
What standards make possible.
HR Open Standards develops standards for exchanging HR information. Shared structures can reduce the need for every system to interpret a different format. Actual interoperability still depends on the standards and versions each participant implements.
W3C’s Verifiable Credentials Data Model 2.0 describes issuer claims, holders, and verifiers, with mechanisms for securing credentials against tampering. A verifier must still determine whether the issuer and claims are suitable for the intended purpose.
For hiring, the practical distinction is simple: a check on a record is not a complete assessment of a person. A course-completion claim and evidence of applying that learning answer different questions.
What AI should help people do.
Clarify the opportunity
Draft questions about responsibilities, unclear criteria, and preparation needs for employer review.
Describe the experience
Help an applicant explain their actions and identify relevant supporting context.
Organize possible connections
Show which example may address a requirement while preserving the source and uncertainty.
Prepare a useful review
Summarize what is supplied and what still needs clarification, with access to the underlying material.
These are proposed uses to evaluate, not guaranteed model performance. AI-generated text should remain reviewable and should not be presented as an issued credential or a verified fact.
Keep interpretation visible.
- Show the employer-confirmed requirement beside the relevant example.
- Distinguish source records, self-description, and AI inference.
- Make missing or partial information visible without treating it as automatic disqualification.
- Give applicants a route to clarify information and challenge errors.
- Keep reviewers accountable for the criteria and the decision.
Our intended approach avoids reducing an applicant to one unexplained score. A structured view should help reviewers understand a connection and its limits, rather than obscure them behind an appearance of precision.
Fair access needs more than a different filter.
Recognizing a wider range of experience is useful only if people can participate in the process. Employers should examine application accessibility, the effort requested, available support, and whether acceptable evidence can come from different relevant settings.
A standardized field can contain an unnecessary requirement. A human reviewer can repeat an unsupported assumption. An AI summary can miss context. Each part needs evaluation rather than an automatic claim of fairness.
This article describes design principles, not an assessment that a specific hiring process meets legal requirements or produces equitable outcomes.
Where TalentPass and TalentSync fit.
TalentPass: explain the experience
The intended individual workflow helps a person prepare and review selected professional context for a purpose.
TalentSync: clarify the opportunity
The intended recruiter workflow connects employer-confirmed requirements with relevant applicant information for review.
Pythia and Profiles
Guided reflection and purpose-specific views help make accounts and requirements understandable.
Passport Pages
Defined exchanges connect selected people and records with appropriate recipients.
These workflows are at different stages of development. This page does not promise general availability, complete ATS integration, automatic credential verification, or automated hiring decisions.
Let the next experience improve the next question.
After hiring, an agreed handoff can connect relevant experience and support needs with onboarding. Later work may reveal that a requirement was unclear, a capability could be learned, or an assumption needs revision.
That is the Human Intelligence Layer’s broader ambition: connecting records, human explanation, and action across a continuing relationship. Any evaluation of hiring criteria needs appropriate evidence and methods; an isolated result does not establish predictive validity.
Start with the part of hiring you need to understand better.
Improve the evidence request
Connect applicant examples with useful review questions.
Connect the next step
Carry appropriate context and support into the first assignment.
Questions about AI, standards, and hiring
What do data standards contribute to skills-based hiring?
They support consistent ways to express and exchange information. They do not determine whether a requirement is relevant or whether a candidate meets it.
Is a verified credential proof of job readiness?
Not by itself. A credential expresses particular issuer claims, and verification checks specified properties. Reviewers still need to assess relevance, trust, and any further evidence needed.
Can AI identify transferable experience?
AI may suggest possible connections between an experience and a requirement. Those suggestions need review against the actual task, source information, and context.
Does removing degree requirements guarantee fairness?
No. Criteria, access, evidence requests, review practices, and opportunities to correct errors all need attention. Outcomes require evaluation.
Does Gobekli automatically rank or reject applicants?
This article describes an intended approach centered on reviewable context and accountable human decisions, not automatic ranking or rejection. Check current availability for actual workflows.
Where should an employer begin?
Choose one role and clarify the work, relevant evidence, review responsibility, and applicant support. A focused workshop can help define that starting point.
Make the next hiring conversation
better informed.
Start with clear work, relevant examples, and questions people can answer.