{"id":54898,"date":"2026-03-10T00:01:18","date_gmt":"2026-03-10T00:01:18","guid":{"rendered":"https:\/\/mihcm.com\/?p=54898"},"modified":"2026-03-10T00:28:24","modified_gmt":"2026-03-10T00:28:24","slug":"resume-screening-in-2026-a-guide-to-ai-powered-screening-ats-integration-bias-governance","status":"publish","type":"post","link":"https:\/\/mihcm.com\/mm\/resources\/blog\/resume-screening-in-2026-a-guide-to-ai-powered-screening-ats-integration-bias-governance\/","title":{"rendered":"Resume screening in 2026: A guide to AI-powered screening, ATS integration &#038; bias governance"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"54898\" class=\"elementor elementor-54898\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5ae1178 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5ae1178\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c03ddeb\" data-id=\"c03ddeb\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fe693cc elementor-widget elementor-widget-text-editor\" data-id=\"fe693cc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Resume screening is the process of filtering applicants to a shortlist suitable for interview. AI-based resume screening augments that process by converting documents into structured fields, extracting skills and experience, and scoring candidates for relevance. The result is faster shortlists, more consistent evaluation and auditable decision traces.<\/p><p>Why it matters: manual screening becomes costly and slow as applicant volumes rise. Screening choices directly affect time-to-hire, cost-per-hire and quality-of-hire. Practical failures today include poor parsing of scanned or layout-rich CVs, inconsistent reviewer decisions, and regulatory scrutiny around automated decisioning.<\/p><p>How AI changes screening: modern approaches move beyond token matching to contextual NLP and embeddings that capture synonyms, experience recency and semantic fit. They add per-field confidence scores, explainability layers and human-in-the-loop fallbacks to reduce risk when parses or scores are uncertain.<\/p><ul><li>Why traditional manual screening breaks at scale: inconsistent criteria, reviewer fatigue, and long cycle times.<\/li><li>What \u2018AI screening\u2019 actually refers to in 2026: pipelines combining parsing, feature extraction, ML ranking and governance controls (confidence bands, audits, manual overrides).<\/li><\/ul><p>This guide delivers integration patterns, a governance checklist, candidate-facing advice and a MiHCM mapping for pilots so teams can pilot rapid screening with auditability and privacy controls.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d05960e elementor-widget elementor-widget-heading\" data-id=\"d05960e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Key takeaways for recruiters, HR Ops and candidates <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-76e71b3 elementor-widget elementor-widget-text-editor\" data-id=\"76e71b3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>AI can markedly reduce screening effort but must be deployed with robust integrations, monitoring and fallbacks. Empirical studies report time-savings commonly in the 50\u201380% range for screening tasks, though results vary by role and implementation (see source: <a href=\"https:\/\/doi.org\/10.3390\/app142411750\" rel=\"nofollow noopener\" target=\"_blank\">MDPI, 2024<\/a>; IJBMI, 2024). MDPI, 2024 | <a href=\"https:\/\/www.ijbmi.org\/papers\/Vol(13)9\/1309143150.pdf\" rel=\"nofollow noopener\" target=\"_blank\">IJBMI, 2024<\/a>.<\/p><p>Prioritise parsing robustness (native PDF\/DOCX handling) and per-field confidence workflows before delegating hiring actions to automation. Scanned PDFs add OCR overhead and reduce parse quality (arXiv, 2025).<\/p><ul><li>Require vendor auditability during procurement: model cards, bias metrics, training-data summaries and retraining triggers.<\/li><li>Start small: run a pilot on one role or team, track parsing rate, precision\/recall (or F1), time-to-hire and demographic fairness metrics, then iterate.<\/li><li>Offer a candidate-facing free checker to reduce friction and improve inbound resume quality while gathering anonymised parser feedback.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a57539f elementor-widget elementor-widget-heading\" data-id=\"a57539f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Resume screening and how AI changes it <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-412f167 elementor-widget elementor-widget-text-editor\" data-id=\"412f167\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Resume screening is an end-to-end workflow: job description \u2192 resume intake \u2192 parsing \u2192 feature extraction \u2192 scoring\/ranking \u2192 human review \u2192 interview \u2192 hire. AI augments several stages: automated parsing, semantic skill-matching, ranking by composite fit scores and routing to reviewers when confidence is low.<\/p><p>Workflow diagram (intake to hire)<\/p><ul><li>Job posting and JD structuring (define must-haves and preferred skills).<\/li><li>Resume intake (direct upload, email, ATS ingest or spreadsheet URLs).<\/li><li>Parsing and OCR (if required) to extract structured fields.<\/li><li>Feature extraction and normalisation (titles, skills, dates, seniority).<\/li><li>Scoring and ranking (hard filters + soft scoring).<\/li><li>Human review for medium\/low-confidence cases; interview routing for high matches.<\/li><\/ul><p>Differences from keyword-only systems: keyword filters match token presence and are brittle to synonyms and context. Modern NLP and embedding-based systems evaluate semantic fit: they can infer that \u201cmachine learning engineer\u201d and \u201cML engineer\u201d are equivalent and weight recent, relevant experience higher.<\/p><p>Where errors occur: layout-rich CVs, multi-column or decorative designs, scanned documents with low-quality OCR output, nonEnglish resumes and unconventional job titles. Those cases frequently need fallback parsing or manual review queues.<\/p><p>Hybrid flows are common: automatically shortlist high-confidence candidates, route the middle band to human reviewers and avoid automated rejections without explicit oversight. This balance manages speed, accuracy and explainability according to role criticality and regulatory context (<a href=\"https:\/\/ico.org.uk\/media2\/migrated\/4031620\/ai-in-recruitment-outcomes-report.pdf\" rel=\"nofollow noopener\" target=\"_blank\">ICO, n.d.<\/a>).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f9bac33 elementor-widget elementor-widget-heading\" data-id=\"f9bac33\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Types of AI approaches used for resume screening <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-04c65c5 elementor-widget elementor-widget-text-editor\" data-id=\"04c65c5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>There are several technical patterns used in screening solutions. Choosing the right approach depends on hiring volume, role complexity and governance requirements.<\/p><p>Pros and cons matrix: keyword vs ML vs deep learning<\/p><div style=\"overflow-x: auto; width: 100%;\"><table style=\"border-collapse: collapse; width: 100%; min-width: 1100px;\"><thead><tr style=\"background-color: #f4f4f4;\"><th style=\"border: 1px solid #ddd; padding: 10px; text-align: left;\">Approach<\/th><th style=\"border: 1px solid #ddd; padding: 10px; text-align: left;\">Strengths<\/th><th style=\"border: 1px solid #ddd; padding: 10px; text-align: left;\">Weaknesses<\/th><\/tr><\/thead><tbody><tr style=\"background-color: #fff;\"><td style=\"border: 1px solid #ddd; padding: 10px;\">Keyword-based<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Fast, auditable, easy to implement<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Brittle; high false negatives if wording differs<\/td><\/tr><tr style=\"background-color: #f9f9f9;\"><td style=\"border: 1px solid #ddd; padding: 10px;\">Classical ML (feature engineering)<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Interpretable features (years, titles); efficient<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Requires curated features; may miss semantic matches<\/td><\/tr><tr style=\"background-color: #fff;\"><td style=\"border: 1px solid #ddd; padding: 10px;\">NLP &amp; embeddings<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Captures synonyms and context; good fuzzy matching<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Needs taxonomy mapping and calibration<\/td><\/tr><tr style=\"background-color: #f9f9f9;\"><td style=\"border: 1px solid #ddd; padding: 10px;\">Deep learning \/ transformers<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">High recall and nuanced ranking<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Less interpretable; heavier monitoring required<\/td><\/tr><tr style=\"background-color: #fff;\"><td style=\"border: 1px solid #ddd; padding: 10px;\">Hybrid<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Pragmatic: rules + ML + LLM ranking where needed<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Operational complexity; needs strong governance<\/td><\/tr><\/tbody><\/table><\/div><p>When to use which: high-volume, low-risk roles often use keyword + classical ML for speed and auditability. Specialist or senior roles may justify transformer-based models combined with human review. Many enterprise deployments use hybrid stacks: rule-based excludes, feature-based scoring, and an embedding or transformer layer for reranking.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-429107d elementor-widget elementor-widget-heading\" data-id=\"429107d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">How AI resume screening works: parsing, scoring and ranking <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d604e1c elementor-widget elementor-widget-text-editor\" data-id=\"d604e1c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>AI screening pipelines typically include three technical stages: parsing, feature extraction and scoring. Each stage must emit confidence metadata so downstream logic can assign human review or automation.<\/p><ul><li>Parsing confidence: why you need per-field scores<\/li><li>Parsing stage: convert file content (native PDF, DOCX, TXT or OCRed scans) into structured fields \u2014 name, contact, experience, education, skills. Use OCR for scanned PDFs and attach OCR confidence to parsed text (<a href=\"https:\/\/arxiv.org\/abs\/2306.13775\" rel=\"nofollow noopener\" target=\"_blank\">arXiv, 2023<\/a>).<\/li><li>Feature extraction: normalise titles, map skills to controlled taxonomies, derive seniority and years-in-role metrics, detect language and compute recency scores.<\/li><li>Scoring models: combine hard filters (must-haves) with soft scoring (skill match, recency, role fit) to produce composite fit scores and ranked shortlists.<\/li><li>Explainability: surface the top features that drove a score \u2014 skills, keywords, recency \u2014 so reviewers can validate automated outcomes and respond to candidate queries.<\/li><li>Human-in-the-loop: auto-shortlist high-confidence matches, route medium-confidence to reviewers and treat auto-reject cautiously for sensitive roles.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e8b6e75 elementor-widget elementor-widget-heading\" data-id=\"e8b6e75\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Resume screening and ATS integration: API, plugin and CSV patterns <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0b0781e elementor-widget elementor-widget-text-editor\" data-id=\"0b0781e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Integration patterns determine how screening outputs flow back into recruiting workflows. Common patterns are real-time API sync, embedded ATS plugins and batch CSV import\/export for legacy systems.<\/p><div style=\"overflow-x: auto; width: 100%;\"><table style=\"border-collapse: collapse; width: 100%; min-width: 1100px;\"><thead><tr style=\"background-color: #f4f4f4;\"><th style=\"border: 1px solid #ddd; padding: 10px; text-align: left;\">Pattern<\/th><th style=\"border: 1px solid #ddd; padding: 10px; text-align: left;\">When to use<\/th><th style=\"border: 1px solid #ddd; padding: 10px; text-align: left;\">Notes<\/th><\/tr><\/thead><tbody><tr style=\"background-color: #fff;\"><td style=\"border: 1px solid #ddd; padding: 10px;\">API \/ webhooks<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Real-time pipelines, low-latency workflows<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Supports eventing: parsed, scored, moved to review<\/td><\/tr><tr style=\"background-color: #f9f9f9;\"><td style=\"border: 1px solid #ddd; padding: 10px;\">ATS plugin \/ embedded UI<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Minimal context switching for recruiters<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">UI maps scores and highlights inside existing candidate record<\/td><\/tr><tr style=\"background-color: #fff;\"><td style=\"border: 1px solid #ddd; padding: 10px;\">CSV import \/ export<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Legacy ATS, bulk uploads<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Include manifest files, checksums and schema version<\/td><\/tr><\/tbody><\/table><\/div><p>Example data flow: job posting \u2192 resume upload \u2192 parse \u2192 ATS sync<\/p><ul><li>Canonical schema: create an ATS canonical schema (title, company, start\/end dates, skills) and maintain schema versioning to avoid mapping drift.<\/li><li>Field mapping: map parsed fields to ATS fields with validation rules for dates and numerics; flag low-confidence fields for reviewer correction.<\/li><li>Eventing: use webhooks for state changes (candidate parsed, score available, moved to review) and trigger downstream actions like assessments or background checks.<\/li><li>Provenance: store original file fingerprints and parse-versioned outputs so hires can be traced to a specific parse and model version for audits.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3800f7a elementor-widget elementor-widget-heading\" data-id=\"3800f7a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Parse accuracy, confidence scores and fallback validation <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dd760d9 elementor-widget elementor-widget-text-editor\" data-id=\"dd760d9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Parsing health is foundational. Measure both overall parsing rate (successful structured output\/total uploads) and per-field accuracy for dates, titles and skills. Per-field confidence lets you route ambiguous cases appropriately.<\/p><p>KPIs to monitor for parsing health<\/p><ul><li>Parsing rate: structured outputs divided by uploads (target benchmark depends on input mix; native text formats outperform scans).<\/li><li>Per-field accuracy: dates, titles and skills error rates measured via sampling and human validation.<\/li><li>Field-mismatch spikes: sudden increases in unknown tokens or failed mappings often indicate parser or schema changes.<\/li><\/ul><p>Fallback strategies include OCR for scanned documents, secondary parser ensembles, and a manual parsing UI where recruiters correct fields. Auto-corrections should be surfaced to reviewers rather than silently applied: normalise date formats but show the corrected value and allow overrides.<\/p><p>Monitoring: instrument parse errors, unknown token rates and field-mismatch alerts. Tie alerts to model-version metadata so operations teams can quickly roll back or triage parser releases.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-599e129 elementor-widget elementor-widget-heading\" data-id=\"599e129\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Privacy, data retention and candidate consent (GDPR\/CCPA and UK guidance) <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e60de13 elementor-widget elementor-widget-text-editor\" data-id=\"e60de13\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Privacy and consent are core compliance requirements. Record candidate consent at upload with explicit use-case text and provide clear deletion or withdrawal flows. Under GDPR consent must be demonstrable; organisations should maintain records that show how and when consent was obtained (<a href=\"https:\/\/www.edpb.europa.eu\/sites\/default\/files\/files\/file1\/edpb_guidelines_202005_consent_en.pdf\" rel=\"nofollow noopener\" target=\"_blank\">EDPB, 2020<\/a>).<\/p><ul><li>Minimise storage: where feasible store parsed fields and a hashed file fingerprint rather than raw resume files; document retention windows by role and jurisdiction.<\/li><li>Cross-border transfers: if processing or models are outside the EU\/UK use Standard Contractual Clauses or equivalent safeguards and record lawful bases for transfer.<\/li><li>Right to access and deletion: provide candidates with an access channel and a simple deletion request flow; CCPA explicitly grants the right to delete personal data for California residents (<a href=\"https:\/\/oag.ca.gov\/privacy\/ccpa\" rel=\"nofollow noopener\" target=\"_blank\">California DOJ, 2024<\/a>).<\/li><li>Audit trails: store model version and parse metadata to allow human reviews and to provide explanations to candidates who request information on automated decisions.<\/li><\/ul><p>Candidate-facing consent example text<\/p><ul><li>&#8220;By uploading your CV you consent to processing for candidate screening, shortlisting and retention for X months. You can request deletion at any time via [link].&#8221;<\/li><\/ul><p>Include Data Processing Agreements (DPAs) with subprocessors, SLA terms for deletion and breach notifications, and make sub-processor lists available to enterprise customers.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0d892f1 elementor-widget elementor-widget-heading\" data-id=\"0d892f1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Bias mitigation, auditability and human-in-the-loop governance <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-392a875 elementor-widget elementor-widget-text-editor\" data-id=\"392a875\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Bias mitigation is a governance priority. Start by defining protected attributes relevant to your jurisdiction and monitor outcome metrics (selection rates, false positive\/negative rates) across groups. Keep model cards and training-data summaries to support procurement and audits.<\/p><p>Practical bias tests to run during a pilot<\/p><ul><li>Selection-rate parity: compare shortlist rates across demographic groups.<\/li><li>False negative auditing: sample rejected candidates to estimate missed qualified candidates.<\/li><li>Feature-importance checks: review which features drive scores and mask or remove proxies for protected attributes where lawful.<\/li><\/ul><p>Techniques include anonymisation (where lawful), fairness-aware reweighting, adversarial de-biasing and post-hoc score calibration. Maintain independent periodic audits and require human oversight for adverse-impact cases. Regulatory guidance recommends clarity on whether AI supports or makes decisions and insists on human oversight for recruitment use-cases (<a href=\"https:\/\/www.gov.uk\/government\/publications\/responsible-ai-in-recruitment-guide\/responsible-ai-in-recruitment\" rel=\"nofollow noopener\" target=\"_blank\">UK Government, 2024<\/a>).<\/p><p>Governance process essentials: vendor evidence of fairness testing, internal change controls for threshold adjustments, retraining cadence tied to drift signals and documented escalation paths when bias signals appear.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d817adb elementor-widget elementor-widget-heading\" data-id=\"d817adb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Measuring ROI and operational metrics for AI screening <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-64a6829 elementor-widget elementor-widget-text-editor\" data-id=\"64a6829\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Measure screening impact across operational and business KPIs. Primary metrics include parsing rate, shortlist precision@N, time-to-shortlist and time-to-hire. Quality metrics include precision\/recall or F1 on sampled shortlists versus hires.<\/p><div style=\"overflow-x: auto; width: 100%;\"><table style=\"border-collapse: collapse; width: 100%; min-width: 1100px;\"><thead><tr style=\"background-color: #f4f4f4;\"><th style=\"border: 1px solid #ddd; padding: 10px; text-align: left;\">Metric<\/th><th style=\"border: 1px solid #ddd; padding: 10px; text-align: left;\">Why it matters<\/th><\/tr><\/thead><tbody><tr style=\"background-color: #fff;\"><td style=\"border: 1px solid #ddd; padding: 10px;\">Parsing rate<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Shows upstream data quality and parser robustness<\/td><\/tr><tr style=\"background-color: #f9f9f9;\"><td style=\"border: 1px solid #ddd; padding: 10px;\">Precision@N<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Measures shortlist quality (how many shortlisted advanced to interviews)<\/td><\/tr><tr style=\"background-color: #fff;\"><td style=\"border: 1px solid #ddd; padding: 10px;\">Throughput (resumes\/hour)<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Operational capacity and scaling performance<\/td><\/tr><tr style=\"background-color: #f9f9f9;\"><td style=\"border: 1px solid #ddd; padding: 10px;\">Average reviewer time<\/td><td style=\"border: 1px solid #ddd; padding: 10px;\">Operational cost and process efficiency<\/td><\/tr><\/tbody><\/table><\/div><p>How to compute precision@N and sample-size recommendations<\/p><p>Precision@N is the fraction of top-N shortlisted candidates who pass the human screen or are interviewed. Use statistically significant sampling for quality checks; for typical pilots a few hundred sampled candidates give reasonable power to detect major precision shifts. Dashboards in MiHCM Analytics and model-performance alerts in MiHCM Data &amp; AI automate sampling and feedback capture to shorten detection times for degradation.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-be28e78 elementor-widget elementor-widget-heading\" data-id=\"be28e78\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Candidate guidance: how to format your CV, test with free checkers and improve scores <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a693016 elementor-widget elementor-widget-text-editor\" data-id=\"a693016\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Candidates should optimise for parsability and clarity. Preferred formats are native PDF or DOCX with selectable text; avoid scanned image PDFs and multi-column layouts. Native files parse more reliably than scans because OCR adds error and noise (<a href=\"https:\/\/arxiv.org\/pdf\/2511.08343\" rel=\"nofollow noopener\" target=\"_blank\">arXiv, 2025<\/a>).<\/p><ul><li>Headings and structure: use standard headings \u2014 Work Experience, Education, Skills, Certifications \u2014 and list dates consistently (MM\/YYYY or YYYY\u2013YYYY).<\/li><li>Keywords strategy: include both acronyms and full forms (eg \u201cSEO\u201d and \u201cSearch Engine Optimisation\u201d) and contextualise skills with outcomes and metrics.<\/li><li>Layout: avoid complex multi-column formats, decorative fonts and embedded images that confuse parsers.<\/li><li>AI editing: using AI to rewrite a CV is acceptable for clarity, but preserve accuracy and truthfulness; use AI to quantify achievements rather than fabricate experience.<\/li><\/ul><p>How to interpret a resume-checker score and improve it<\/p><p>Free resume checkers surface parsed fields and confidence scores. Focus on correcting the core fields \u2014 title, dates, companies, skills \u2014 until they parse correctly. If a checker flags low confidence on dates or titles, adjust headings or simplify layout and re-upload.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f3f9cf3 elementor-widget elementor-widget-heading\" data-id=\"f3f9cf3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Deployment roadmap, pilot &amp; governance checklist (POC to production) <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2187ae2 elementor-widget elementor-widget-text-editor\" data-id=\"2187ae2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Run a phased deployment: proof-of-concept (POC), pilot, incremental rollout and production. Target a 0\u201312 week POC that validates parsing baseline, precision sampling, integration and consent flows.<\/p><p>POC timeline example: 0\u201312 weeks<\/p><ul><li>Weeks 0\u20132: gather historical resumes and define pilot roles; establish KPIs (parsing rate, precision@N, time-to-shortlist).<\/li><li>Weeks 2\u20136: integrate parser (API or CSV), run initial parsing baseline, set up sampling and human-review queues.<\/li><li>Weeks 6\u20139: run bias and fairness checks on sampled data, tune thresholds and routing rules; validate consent and deletion flows.<\/li><li>Weeks 9\u201312: expand to a pilot cohort, monitor dashboards, collect reviewer feedback and prepare incremental rollout plan.<\/li><\/ul><p>POC checklist items: parsing baseline, precision sampling, API\/CSV integration test, consent and retention flow test, bias checks on historical data and reviewer training. MiHCM offers POC templates, field-mapping blueprints and SmartAssist rules to accelerate pilots and reduce operational risk.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-672cacb elementor-widget elementor-widget-heading\" data-id=\"672cacb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Frequently Asked Questions <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1b0cead elementor-widget elementor-widget-n-accordion\" data-id=\"1b0cead\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;expanded&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Accordion. Open links with Enter or Space, close with Escape, and navigate with Arrow Keys\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2830\" class=\"e-n-accordion-item\" open>\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"true\" aria-controls=\"e-n-accordion-item-2830\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> What file formats are best? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><i aria-hidden=\"true\" class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t<span class='e-closed'><i aria-hidden=\"true\" class=\"fas fa-plus\"><\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2830\" class=\"elementor-element elementor-element-7607bc8 e-con-full e-flex e-con e-child\" data-id=\"7607bc8\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2830\" class=\"elementor-element elementor-element-457c746 e-flex e-con-boxed e-con e-child\" data-id=\"457c746\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b3d3c35 elementor-widget elementor-widget-text-editor\" data-id=\"b3d3c35\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tNative PDF or DOCX with selectable text. Avoid scans and images. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2831\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-2831\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Does a score mean I\u2019m automatically rejected?  <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><i aria-hidden=\"true\" class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t<span class='e-closed'><i aria-hidden=\"true\" class=\"fas fa-plus\"><\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2831\" class=\"elementor-element elementor-element-3bb96e9 e-con-full e-flex e-con e-child\" data-id=\"3bb96e9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2831\" class=\"elementor-element elementor-element-0dab590 e-flex e-con-boxed e-con e-child\" data-id=\"0dab590\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2162035 elementor-widget elementor-widget-text-editor\" data-id=\"2162035\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tNot necessarily: scores are inputs to workflows; organisations should avoid auto-reject without human oversight for sensitive roles.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2832\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-2832\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> How do I remove my data?  <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><i aria-hidden=\"true\" class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t<span class='e-closed'><i aria-hidden=\"true\" class=\"fas fa-plus\"><\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2832\" class=\"elementor-element elementor-element-6f2bea4 e-con-full e-flex e-con e-child\" data-id=\"6f2bea4\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2832\" class=\"elementor-element elementor-element-d0c11f0 e-flex e-con-boxed e-con e-child\" data-id=\"d0c11f0\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-990a709 elementor-widget elementor-widget-text-editor\" data-id=\"990a709\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tOrganisations should publish deletion flows. MiHCM supports deletion via candidate request and DPAs for enterprise customers.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2833\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"4\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-2833\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> How can recruiters validate the model?  <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><i aria-hidden=\"true\" class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t<span class='e-closed'><i aria-hidden=\"true\" class=\"fas fa-plus\"><\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2833\" class=\"elementor-element elementor-element-05efeb8 e-con-full e-flex e-con e-child\" data-id=\"05efeb8\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2833\" class=\"elementor-element elementor-element-c4ae83b e-flex e-con-boxed e-con e-child\" data-id=\"c4ae83b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-bc9943f elementor-widget elementor-widget-text-editor\" data-id=\"bc9943f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tUse sampled manual reviews, monitor precision\/recall, review feature importance and run bias tests during pilots.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2834\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"5\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-2834\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Can AI introduce bias?  <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><i aria-hidden=\"true\" class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t<span class='e-closed'><i aria-hidden=\"true\" class=\"fas fa-plus\"><\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2834\" class=\"elementor-element elementor-element-ee1f200 e-con-full e-flex e-con e-child\" data-id=\"ee1f200\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2834\" class=\"elementor-element elementor-element-ab88890 e-flex e-con-boxed e-con e-child\" data-id=\"ab88890\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-32941ae elementor-widget elementor-widget-text-editor\" data-id=\"32941ae\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tYes. Run fairness tests, consider anonymisation where lawful and keep human oversight in sensitive roles.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2835\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"6\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-2835\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Is there a free resume checker?  <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><i aria-hidden=\"true\" class=\"fas fa-minus\"><\/i><\/span>\n\t\t\t<span class='e-closed'><i aria-hidden=\"true\" class=\"fas fa-plus\"><\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2835\" class=\"elementor-element elementor-element-6bf811a e-con-full e-flex e-con e-child\" data-id=\"6bf811a\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2835\" class=\"elementor-element elementor-element-5e743a9 e-flex e-con-boxed e-con e-child\" data-id=\"5e743a9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-759c830 elementor-widget elementor-widget-text-editor\" data-id=\"759c830\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Many vendors offer candidate-facing checkers; they are useful for testing and lead-gen but check privacy terms before uploading sensitive data.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Resume screening is the process of filtering applicants to a shortlist suitable for interview. AI-based resume screening augments that process by converting documents into structured fields, extracting skills and experience, and scoring candidates for relevance. The result is faster shortlists, more consistent evaluation and auditable decision traces. Why it matters: manual screening becomes costly and [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":54899,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[18],"tags":[],"class_list":["post-54898","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"_links":{"self":[{"href":"https:\/\/mihcm.com\/mm\/wp-json\/wp\/v2\/posts\/54898","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mihcm.com\/mm\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mihcm.com\/mm\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mihcm.com\/mm\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/mihcm.com\/mm\/wp-json\/wp\/v2\/comments?post=54898"}],"version-history":[{"count":0,"href":"https:\/\/mihcm.com\/mm\/wp-json\/wp\/v2\/posts\/54898\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mihcm.com\/mm\/wp-json\/wp\/v2\/media\/54899"}],"wp:attachment":[{"href":"https:\/\/mihcm.com\/mm\/wp-json\/wp\/v2\/media?parent=54898"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mihcm.com\/mm\/wp-json\/wp\/v2\/categories?post=54898"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mihcm.com\/mm\/wp-json\/wp\/v2\/tags?post=54898"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}