---
title: 9 Biodata: evaluating biographical information for job fit
---

# 9 Biodata: evaluating biographical information for job fit

Biodata is the new instrument this sprint researched most deeply, and the one on which the Blind CV review and the knock-out criteria depend. This chapter argues in six steps that biographical information can be a valid and fair source of evidence – but only under a specific method – and specifies that method.

## 9.1 A CV score is not a CV score

How valid and how biased the scoring of biographical data is depends entirely on the method, not on whether names and photographs have been removed. Three methods are in play. Naive CV screening rates the document as a whole – titles, employers, years, education, presentation – without a fixed framework or fixed weights and with the candidate's identity in view. There is no validity estimate for the whole, and its ingredients are weak: years of job experience predict performance at ρ = .07, years of education and the training-and-experience point method at .10 and .11 respectively \[3\].

Its bias is the largest of the three: years alone carry a group difference of d = .49 \[3\], and the correspondence audits since Bertrand and Mullainathan \[10\] show fewer call-backs for minority and female names on otherwise identical CVs. Its reasoning cannot be reconstructed. Rationally keyed biodata scores standardised entries with rules that follow from the requirement – which tasks, skills and knowledge count, how duration, recency and responsibility are weighed – fixed before any candidate is seen.

Its validity is moderate (ρ = .22) but job-relevant by construction, so it adds information next to the question scores; its group difference is d = .33, and because the score needs neither name nor age nor employer, those can be removed and proxies excluded explicitly; every level cites a rule, a requirement and the entry. Empirically keyed biodata uses the same entries with weights estimated from the relation between past candidates' biodata and a criterion, cross-validated.

It is the most valid of the three (ρ = .38) once enough outcome data exists per role family, carries the same d = .33, but can learn who was hired rather than who performed, and its weights are statable without being logically justified.

![Figure 14 – Naive screening, rationally keyed and empirically keyed biodata compared on validity, bias and explainability.](../media/image15.png)

*Figure 14 – Naive screening, rationally keyed and empirically keyed biodata compared on validity, bias and explainability.*

The consequence is the position this report takes. A naive, rule-of-thumb score on the CV, even anonymised, has poor validity, and that noise would leak into the overall assessment. The minimum standard is a standardised format, rationally scored, and ideally bias-mitigated. The remaining bias potential is real and higher than for the anonymous answers – but, unlike the answers, biographical evidence is what the customers actually asked for, and section 9.6 shows that in a taxonomy-based system that potential can be measured and reduced.

## 9.2 What the biodata literature says about keying

Biodata instruments have been used in selection for decades, traditionally as standardised questionnaires whose responses are converted into scores either by expert judgement (rational keying), by their statistical relation with an outcome (empirical keying) or by a combination of both (hybrid keying); factor-analytic scaling is a scale-construction technique rather than a separate source of justification \[5\]. Three studies shape the path this concept proposes.

Stokes and Searcy \[6\] built biodata scales for mechanical-equipment franchise owners and compared broad rational scales, narrowly specified rational scales, internally derived scales and an empirical item key against sales performance and supervisory ratings. Their abstract reports higher validities and less faking with empirical keys overall – and, decisive for us, that the specific rational scales were as predictive as the empirical item key. The lesson is not that rational scoring equals empirical scoring in general, but that rational scoring works when it is tied to specific, clearly defined constructs rather than to a loose combination of education, titles and years.

Cucina, Caputo, Thibodeaux and Maclane \[7\] compared rational, empirical and hybrid scoring on 139 items of the US Office of Personnel Management's Individual Achievement Record. Rational keying yielded the lowest validities in their data; hybrid methods performed best at the smallest development samples, empirical keying at moderate sizes and stepwise regression weighting at the largest. Their vertical-percentage example makes the empirical logic concrete: a response chosen by 18.2 % of high performers and 9.5 % of low performers receives a weight of +8.7 – it scores because it discriminates, not because it sounds desirable.

Speer, Siver and Christiansen \[8\] addressed the main criticism of empirical keys, their opacity and poor generalisation, and showed across two field studies that the similarity between development and application context affects how empirical keys relate to external measures; empirical associations therefore have to be read against the underlying construct and the job context.

![Figure 15 – The three families of biodata scoring and the chosen path: rational now, hybrid next, empirical later.](../media/image16.png)

*Figure 15 – The three families of biodata scoring and the chosen path: rational now, hybrid next, empirical later.*

Taken together the three studies justify a staged path. Rational keying first: it needs no outcome data, it is transparent, and it is defensible when constructs are narrow and requirement-linked – which is exactly what the taxonomy provides. Hybrid calibration next: once reliable performance observations exist, the weights of the same constructs are estimated statistically while the construct structure, and with it the explanation, is kept. Empirical keying later, where volume and outcome quality allow it. Two guardrails hold throughout \[5\]. The evidence record stays separate from the score, so a change in scoring method never requires re-extracting or rewriting a record. And calibration uses reviewer judgements and, where available, job performance – never hiring decisions or recruiter preferences, which would reproduce existing bias rather than predict performance.

## 9.3 From questionnaire items to standardised CV records

A classic biodata item measures one construct with an ordered response scale: how many projects the candidate has coordinated independently in the last five years (none, one, two to three, four or more); the highest level of project responsibility in the most recent role (participated, parts, independently, several complex projects); how often the candidate coordinated with external suppliers; how many non-required trainings they completed. The scale value is the rational score.

ObjectivEye's operational requirement is time efficiency \[5\]: a second questionnaire next to the assessment is not acceptable. The same constructs can, however, be filled from standardised CV records. Experience records that carry the task "coordinate projects" with responsibility "independent" and a duration give the coordination level; the highest responsibility attribute across records gives the responsibility level; a certificate record gives the learning construct. What the CV cannot show – frequency, scale – is asked as a single confirmation question rather than as a form.

The CV thus becomes the quick way to fill a biodata instrument whose items the candidate never has to see.

![Figure 16 – Typical biodata questionnaire items, and the same constructs filled from the Skills Passport.](../media/image17.png)

*Figure 16 – Typical biodata questionnaire items, and the same constructs filled from the Skills Passport.*

## 9.4 The Skills Passport as classification

TNO's Skills Passport research provides the classification for those records. The passport is positioned as a digital tool for recording competences that gives workers an overview for career development and helps employers with recruitment and selection, as an alternative to relying on occupations and formal qualifications alone \[11\]. In TNO's 2022 stakeholder study nearly 60 % of employers considered it a good idea and 82 % of those would use it; workers named privacy and self-determination – control over sharing, correcting and deleting their data – as preconditions, and ownership and governance were explicitly left open \[11\].

The 2025 programme in the metal and electrical engineering sector tests three technical concepts, insists on interoperability between systems and on a range of validation from soft (online tests) to hard (demonstrated skills), and notes that AI used on the data must be explainable and fair \[12\]; the Metalektro pilot with seven companies reported in July 2026 \[13\].

The methodological note \[5\] summarises the expected contents as formal qualifications, professional experience, skills and competencies, practical achievements, validated proficiency and development interests, and cites TNO's stakeholder research for the weight employers give to them (93 % qualifications, 92 % work experience, 91 % a skills list, 84 % certificates).

![Figure 17 – What a Skills Passport contains (TNO) and a schematic record set with validation status per record.](../media/image18.png)

*Figure 17 – What a Skills Passport contains (TNO) and a schematic record set with validation status per record.*

Two features of the passport carry over into this concept. First, the record types themselves: the standardised profile classifies biographical information into those six types, each record linked to taxonomy concepts. Second, the distinction between self-reported and validated evidence. A skill listed on a CV, two years in a role that uses it, a portfolio that applies it, a standardised assessment of it and independently verified work establish different things and must not receive fixed scores by source alone; a certificate proves completion, not proficiency. Every record therefore carries a validation status – self-reported, confirmed by the candidate, verified, assessed – and the scoring guide decides what a status is worth for a given requirement. Ownership follows TNO's model: the passport belongs to the candidate, who controls what is shared.

Operationally, a CV becomes a set of blind, classified passport records in four steps. Extraction splits the document into entries – experience, education, certificate – and free text into task statements. Mapping links each entry to taxonomy concepts: occupation, tasks, skills, knowledge, education level (ESCO, ISCED). Standardisation computes the same fields for every candidate – duration in years, recency, responsibility level, sector. Confirmation shows the candidate only the standardised entries to correct and complete; nothing is inferred silently. Name, age, address, photograph, gender, employer names and exact dates are removed before anything is scored, and every entry keeps its source span in the CV so that provenance is available without the document. The records are reused for the next requirements profile; the CV is never read again.

![Figure 18 – From the uploaded CV to standardised, blind entries in four steps.](../media/image19.png)

*Figure 18 – From the uploaded CV to standardised, blind entries in four steps.*

## 9.5 Skills-based matching on the taxonomy graph

Once requirements and records are expressed in the same concepts, matching no longer needs documents or titles. Occupations, tasks, skills and knowledge are nodes of one graph – ESCO plus the ObjectivEye task layer. A requirements profile and a biographical profile are both sets of nodes with weights. The links of the graph define relatedness: an occupation has essential and optional skills, a skill has broader and narrower skills, a task uses skills, an occupation has typical tasks. A path between a requirement node and a candidate node is a reason, and the match is coverage per requirement: a required node is covered directly, via an allowed path with a distance penalty, or not at all. There is no single similarity number.

![Figure 19 – Skills-based matching as path-finding on the taxonomy graph, applied to keyed biodata.](../media/image20.png)

*Figure 19 – Skills-based matching as path-finding on the taxonomy graph, applied to keyed biodata.*

Applied to keyed biodata this gives the rational key a precise form. Every standardised entry becomes a set of nodes with duration, recency and level attached. The key is a weight per link type – same task above narrower skill, narrower skill above essential skill of the occupation, that above occupation group – scaled by duration and recency, fixed in advance and readable as rules. Where the taxonomy has no path between a requirement and an entry, nothing is scored: a missing link is a taxonomy task, not a candidate penalty, and the absence shows as missing evidence rather than as a low level. When empirical or hybrid calibration becomes possible, it re-estimates the same link-type weights; the structure, and with it the explanation, stays.

![Figure 20 – The whole flow in one view for a fictitious project-coordinator case: blind CV → CV graph → skills graph ← requirements graph ← vacancy, with the match read off per requirement.](../media/image1.png)

*Figure 20 – The whole flow in one view for a fictitious project-coordinator case: blind CV → CV graph → skills graph ← requirements graph ← vacancy, with the match read off per requirement.*

Reading the figure: the requirement "plan projects with milestones" points at the task node plan project schedules; the candidate's experience record as project officer contains the same task, five years, own projects, confirmed – a direct match and level 4 under the requirement's rule L4. "Apply public procurement rules" points at the knowledge node public procurement, which no candidate record reaches: no evidence, shown as missing, and because the requirement is a knock-out, a decision for the reviewer rather than a low score. The same picture holds for a Skills Passport that arrives already classified: its records already carry taxonomy concepts and a validation status, so it enters where the CV graph does.

## 9.6 Bias: what remains after blinding, and why the graph helps

Blinding closes one door. Five others stay open in biographical scoring, and each has to be named with its control and its measure. The first is the structure of the taxonomy itself.

In Adhikari, Vethman, Vos, Lenz, Cocu, Tolios and Veenman \[9\] language-model representations of occupational skills – bag of words, word2vec and BERT – reproduce the gender segregation of the labour market, and across all representations and distance metrics tested that segregation carries through to which occupations a profile is matched to; the authors recommend evaluating matching models on matching performance and gender-segregation risk together and making the bias explicit.

The control here is to score through tasks and skills rather than occupation similarity, to let occupation nodes widen the search without scoring, and to down-weight or disable link types that carry segregated structure; the measure is the group gap per link type and per requirement and the balance point from the Bias Monitoring Framework \[14\]. The second door is the proxies inside the entries: years, employer prestige, credential level, career gaps and part-time periods correlate with age, gender and background.

The rational key scores task and responsibility rather than years or credentials alone, gaps and part-time are not fields, recency is capped; the measure is the pass-rate difference per entry attribute and the sensitivity of levels to years and credentials. The third is extraction and language: CV style, non-native phrasing and sector vocabulary change what an extractor finds, and some groups document more than others. The candidate's confirmation and completion step is the control, together with extraction evaluated per language; the measures are extraction recall by language and sector and the correction rate per group.

The fourth is historical outcome data, which enters with empirical or hybrid keying: a key can learn who was hired or rated well rather than who performed. Calibration therefore uses reviewer judgements and performance criteria only, with a group check before every weight update and weights that stay statable rules; the measure is the group gap before and after each calibration and cross-validated validity per group. The fifth is reviewer anchoring: a visible CV score steers the judgement of the answers and the reverse.

CV, question and probe scores stay separate and side by side, the order of reveal is a workflow setting, identity reveal is controlled; the measure is agreement with and without the CV score visible, one of the indicators the Box 3 analysis already lists \[1\].

![Figure 21 – Five bias risks that remain after blinding, with control and measure for each.](../media/image21.png)

*Figure 21 – Five bias risks that remain after blinding, with control and measure for each.*

What distinguishes a taxonomy-based system is not that these risks are smaller but that they are locatable. In a vector model a group gap can only be observed. Here every CV score is a set of paths – requirement node, link type, candidate node, weight – and a group difference in scores decomposes into the same parts: which requirement, which link type, which entry type produces it. The gap can be measured where it arises, on blind entries, so that the cause is a rule or a node and never a name. It can be corrected at that element – a link type down-weighted, a proxy node excluded, a missing transferable link added – with each change a readable rule that is re-measured afterwards. And because transferable tasks and skills count across occupations, more relevant evidence is used while every rule stays visible, which is how validity can rise and bias-proneness fall at the same time. That is the case for embedding keyed biodata in the graph rather than in a model.