TecHR 30
www.hrtechseries.comTecHR Series covers news,views and interviews from the HR technology realm
Curated from 55+ AI blogs with 170+ articles on talent acquisition, HR automation & employee engagement. Updated daily.
AI in HR is a high-stakes niche: a bad algorithm affects who gets hired and who gets let go, and the tools are shipping faster than the audits can catch up. This page tracks sources serious about both the opportunity and the risk.
We index 165+ articles from 55+ sources on AI in HR and recruiting. The signal leans practitioner-heavy — dedicated HR publications like TecHR and HR Executive contribute most of the coverage, with CIO and business media filling in the enterprise-deployment and change-management angles.
Unlike our AI for Business directory, which covers enterprise transformation broadly, this page focuses specifically on the people-operations side: hiring workflows, performance management, employee experience, and the bias-and-fairness questions that HR teams now have to own.
How we rank these blogs →TecHR Series covers news,views and interviews from the HR technology realm
Human Resource Executive
The most recent home feed on DEV Community.
TecHR Series covers news,views and interviews from the HR technology realm
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Leading from the front lines of work, HR and tech
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Leading platforms: HireVue (AI video interviews and assessments), Eightfold AI (talent intelligence and matching), Phenom (candidate experience platform), Textio (inclusive job description optimization), Paradox/Olivia (conversational AI recruiting assistant), and Beamery (talent CRM with AI). For smaller companies, tools like Manatal ($15 per user per month) and Breezy HR offer affordable AI-powered applicant tracking.
AI can reduce bias by standardizing evaluation criteria, blind-screening resumes, and using structured assessments rather than gut feelings. However, AI trained on historical hiring data can perpetuate past discrimination. Best practices: audit AI tools for disparate impact across demographics, use diverse training data, combine AI screening with structured human interviews, and regularly test for adverse impact. The NYC AI hiring law requires annual bias audits.
Key metrics: time-to-fill reduction (AI typically cuts 30-50%), quality-of-hire scores at 6 and 12 months, candidate experience ratings (AI responsiveness), diversity of candidate pipeline at each stage, cost-per-hire changes, hiring manager satisfaction, and AI recommendation acceptance rates. Track false positive and false negative rates to calibrate AI screening thresholds. Benchmark against pre-AI baselines for accurate ROI measurement.
AI predicts flight risk 3-6 months before resignation by analyzing patterns in engagement surveys, communication frequency, performance data, and peer departure signals. Tools like Visier and Qualtrics use AI for real-time sentiment analysis. AI-powered career pathing shows employees growth opportunities matched to their skills. Organizations using predictive retention AI report 15-25% improvement in turnover rates for at-risk employees.
AI can automate 60-70% of recruiting tasks: sourcing candidates, screening resumes, scheduling interviews, answering candidate FAQs, sending status updates, and generating offer letters. However, human involvement remains critical for culture fit assessment, complex negotiation, executive hiring, sensitive conversations, and final hiring decisions. The best approach is AI handling administrative work while recruiters focus on relationship building and strategic hiring.
Key regulations: NYC Local Law 144 requires annual bias audits for automated employment decision tools. The EU AI Act classifies hiring AI as high-risk, requiring transparency and human oversight. EEOC guidance holds employers liable for AI-driven discrimination. Illinois BIPA covers AI video interview biometric data. Best practice: maintain documentation of AI tool validation, conduct regular adverse impact analyses, and provide candidates with disclosure and opt-out options.