{
  "version": 1,
  "canonical_url": "https://digidai.github.io/answers/",
  "json_url": "https://digidai.github.io/answers/index.json",
  "markdown_url": "https://digidai.github.io/answers/index.md",
  "description": "Question-first access to curated evidence records. These summaries are not original experiments, product tests or legal advice. Preserve scope and limitations; cite the primary source for its findings and the record for editorial analysis.",
  "editorial_disclosure": "Gene Dai is a co-founder of Metix AI; that commercial relationship matters when reading the hiring analysis.",
  "date_modified": "2026-09-09",
  "count": 14,
  "items": [
    {
      "id": "ai-hiring-shl-assessment-validity",
      "topic": "ai-hiring",
      "topic_title": "AI hiring procurement evidence",
      "subject": "SHL",
      "question": "Does an AI assessment still need job-related validation?",
      "answer": "SHL's AI principles require reliability, validity and job relevance for AI assessments, as for other assessments. The document also treats explainability as a design requirement.",
      "evidence_kind": "vendor_documentation",
      "source": {
        "title": "SHL: Core Principles for the Ethical and Effective Use of AI to Assess Talent",
        "url": "https://www.shl.com/assets/documents/how-shl-is-using-ai-and-machine-learning-white-paper.pdf",
        "published": null
      },
      "checked_at": "2026-09-08",
      "scope": "Publicly indexed excerpt of vendor AI-assessment principles; direct document retrieval failed during review. Not a technical manual for a named test or employer.",
      "limitation": "Principles alone do not establish predictive validity, accessibility or subgroup performance for your assessment, language and applicant population.",
      "next_check": "Request the technical manual, validation cohort, norm group, accommodation process and monitoring plan for the exact instrument and scoring version.",
      "citation_url": "https://digidai.github.io/research/ai-hiring/#shl-assessment-validity",
      "markdown_url": "https://digidai.github.io/research/ai-hiring/index.md"
    },
    {
      "id": "ai-hiring-hirevue-explainability",
      "topic": "ai-hiring",
      "topic_title": "AI hiring procurement evidence",
      "subject": "HireVue",
      "question": "Which input and output does the assessment actually use?",
      "answer": "HireVue publishes an AI explainability statement describing its assessment approach. Treat that statement as a starting point for tracing candidate inputs, scoring and human decisions.",
      "evidence_kind": "vendor_documentation",
      "source": {
        "title": "HireVue 2024 AI Explainability Statement",
        "url": "https://www.hirevue.com/wp-content/uploads/2024/09/HV_2024_AI-Explainability-Statement.pdf",
        "published": "2024"
      },
      "checked_at": "2026-09-08",
      "scope": "Indexed excerpt and publisher description of the named 2024 statement; full-PDF retrieval exceeded the review tool's size limit. It cannot automatically describe later products or a customer's current configuration.",
      "limitation": "A public explanation does not verify the current model, enabled modalities, score use or retention settings in a prospective deployment.",
      "next_check": "Ask for the current statement, exact input modalities, model version, candidate notice, deletion controls and how an assessor can contest or override an output.",
      "citation_url": "https://digidai.github.io/research/ai-hiring/#hirevue-explainability",
      "markdown_url": "https://digidai.github.io/research/ai-hiring/index.md"
    },
    {
      "id": "ai-hiring-ashby-application-review",
      "topic": "ai-hiring",
      "topic_title": "AI hiring procurement evidence",
      "subject": "Ashby",
      "question": "Who defines the criteria used to review applications?",
      "answer": "Ashby documents job-specific resume criteria for AI-assisted application review, with organization-admin enablement and role-level permissions for editing criteria.",
      "evidence_kind": "vendor_documentation",
      "source": {
        "title": "Ashby: AI-Assisted Application Review",
        "url": "https://docs.ashbyhq.com/ai-assisted-application-review",
        "published": null
      },
      "checked_at": "2026-09-08",
      "scope": "Documented configuration of application review, not observed screening accuracy or a guarantee of plan entitlement.",
      "limitation": "A resume can omit a skill the candidate has. Criteria matching is not a validated measure of future job performance.",
      "next_check": "Test ambiguous and incomplete resumes; identify who can change criteria, how changes are logged and whether a reviewer sees the underlying evidence before rejecting anyone.",
      "citation_url": "https://digidai.github.io/research/ai-hiring/#ashby-application-review",
      "markdown_url": "https://digidai.github.io/research/ai-hiring/index.md"
    },
    {
      "id": "ai-hiring-greenhouse-ai-audit-claims",
      "topic": "ai-hiring",
      "topic_title": "AI hiring procurement evidence",
      "subject": "Greenhouse",
      "question": "Does the audit cover the feature you plan to enable?",
      "answer": "Greenhouse's AI principles page says Talent Matching and AI Interviewer undergo independent monthly bias audits by Warden AI. This record captures the vendor's claim, not a review of those audit reports.",
      "evidence_kind": "vendor_documentation",
      "source": {
        "title": "Greenhouse: Responsible AI in Recruitment for Hiring Teams",
        "url": "https://www.greenhouse.com/ai-principles",
        "published": null
      },
      "checked_at": "2026-09-08",
      "scope": "Named features and vendor-stated audit cadence on a public principles page.",
      "limitation": "An audit cadence does not specify the dataset, tested model version, selection thresholds, exceptions or applicability to a customer.",
      "next_check": "Obtain the latest feature-specific report, auditor identity, test dates, sample sizes, excluded groups and documented limitations. Compare them with the deployment you are buying.",
      "citation_url": "https://digidai.github.io/research/ai-hiring/#greenhouse-ai-audit-claims",
      "markdown_url": "https://digidai.github.io/research/ai-hiring/index.md"
    },
    {
      "id": "ai-hiring-workday-spotlight-audit-scope",
      "topic": "ai-hiring",
      "topic_title": "AI hiring procurement evidence",
      "subject": "Workday / HiredScore",
      "question": "Can one employer's audit stand in for yours?",
      "answer": "Workday publishes a Secretariat analysis of its own Spotlight deployment. Testing completed March 20, 2026; the stated applicant period is September 1, 2025 through February 28, 2026.",
      "evidence_kind": "vendor_audit_summary",
      "source": {
        "title": "Workday: Responsible AI and Bias Mitigation for HiredScore Spotlight",
        "url": "https://www.workday.com/en-us/legal/responsible-ai-and-bias-mitigation.html",
        "published": null
      },
      "checked_at": "2026-09-08",
      "scope": "U.S. residents applying for greater-New-York-City roles in Workday's own hiring; selected high-volume job profiles and demographic-data exclusions are described in the source.",
      "limitation": "Workday explicitly limits the analysis to its implementation and says the page does not satisfy a customer-specific legal obligation. It is not a universal fairness certificate.",
      "next_check": "Compare your job mix, thresholds, candidate pool and human review with the reported scope; request evidence for the currently deployed model and your own use case.",
      "citation_url": "https://digidai.github.io/research/ai-hiring/#workday-spotlight-audit-scope",
      "markdown_url": "https://digidai.github.io/research/ai-hiring/index.md"
    },
    {
      "id": "ai-hiring-ukg-contract-scope",
      "topic": "ai-hiring",
      "topic_title": "AI hiring procurement evidence",
      "subject": "UKG",
      "question": "Which contracted module performs the promised workflow?",
      "answer": "UKG publishes separate service descriptions for Pro HCM, Pro Workforce Management, Ready and AI-related services. A product-family name is not a complete statement of the purchased scope.",
      "evidence_kind": "vendor_documentation",
      "source": {
        "title": "UKG: Services Descriptions",
        "url": "https://www.ukg.com/legal/services-descriptions",
        "published": null
      },
      "checked_at": "2026-09-08",
      "scope": "Public service-description directory, not an executed order form, statement of work or customer entitlement.",
      "limitation": "A feature appearing in the portfolio does not prove it is included, configured, supported in a country or connected to the employer's ATS.",
      "next_check": "Map each promised action to a module, order-form line, implementation owner, data integration, country scope and acceptance test.",
      "citation_url": "https://digidai.github.io/research/ai-hiring/#ukg-contract-scope",
      "markdown_url": "https://digidai.github.io/research/ai-hiring/index.md"
    },
    {
      "id": "ai-employment-support-qje-2025",
      "topic": "ai-employment",
      "topic_title": "AI and employment evidence",
      "subject": "Customer support productivity",
      "question": "Can AI assistance improve measured output at work?",
      "answer": "The published study reports a 15% average increase in issues resolved per hour after AI assistance was introduced, with different effects across worker experience levels.",
      "evidence_kind": "field_study",
      "source": {
        "title": "Brynjolfsson, Li and Raymond: Generative AI at Work (QJE, 2025)",
        "url": "https://academic.oup.com/qje/article/140/2/889/7990658",
        "published": "2025-02-04"
      },
      "checked_at": "2026-09-08",
      "scope": "Staggered introduction of assistance to 5,172 customer-support agents. This is the published QJE version, not the earlier 5,179-agent working-paper version.",
      "limitation": "Issues resolved per hour is a task-productivity outcome in one setting, not an economy-wide employment, wage or headcount estimate.",
      "next_check": "Compare the work and experience mix with your team; count quality and escalations alongside throughput.",
      "citation_url": "https://digidai.github.io/research/ai-employment/#support-qje-2025",
      "markdown_url": "https://digidai.github.io/research/ai-employment/index.md"
    },
    {
      "id": "ai-employment-metr-developers-2025",
      "topic": "ai-employment",
      "topic_title": "AI and employment evidence",
      "subject": "Experienced developer productivity",
      "question": "Did early-2025 coding tools always save time?",
      "answer": "METR's randomized study found that permitting AI tools increased task-completion time by 19% in its sampled experienced open-source developers.",
      "evidence_kind": "randomized_experiment",
      "source": {
        "title": "METR: Early-2025 AI and Experienced Open-Source Developer Productivity",
        "url": "https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/",
        "published": "2025-07-10"
      },
      "checked_at": "2026-09-08",
      "scope": "16 developers, 246 tasks in familiar mature repositories; tools from February through June 2025.",
      "limitation": "This result is not a ranking of current coding products and does not represent all developers, unfamiliar repositories or later models.",
      "next_check": "Read the 2026 follow-up before citing this as evidence about current tools; measure accepted changes and review time locally.",
      "citation_url": "https://digidai.github.io/research/ai-employment/#metr-developers-2025",
      "markdown_url": "https://digidai.github.io/research/ai-employment/index.md"
    },
    {
      "id": "ai-employment-metr-selection-2026",
      "topic": "ai-employment",
      "topic_title": "AI and employment evidence",
      "subject": "Developer study selection bias",
      "question": "Can the follow-up settle the current speedup?",
      "answer": "METR says its follow-up provides an unreliable estimate of current productivity effects because of participant and task selection, plus measurement difficulties with concurrent agents.",
      "evidence_kind": "methodology_update",
      "source": {
        "title": "METR: We are Changing our Developer Productivity Experiment Design",
        "url": "https://metr.org/blog/2026-02-24-uplift-update/",
        "published": "2026-02-24"
      },
      "checked_at": "2026-09-08",
      "scope": "Update on the experiment begun in August 2025, separate from the early-2025 randomized result.",
      "limitation": "Raw follow-up estimates should not be advertised as a clean causal estimate or a definitive reversal of the earlier result.",
      "next_check": "Look for revised study designs and report who declined AI-disallowed tasks. Count concurrent-agent time consistently.",
      "citation_url": "https://digidai.github.io/research/ai-employment/#metr-selection-2026",
      "markdown_url": "https://digidai.github.io/research/ai-employment/index.md"
    },
    {
      "id": "ai-employment-ilo-exposure-2025",
      "topic": "ai-employment",
      "topic_title": "AI and employment evidence",
      "subject": "Global occupational exposure",
      "question": "Does potential exposure mean jobs disappear?",
      "answer": "ILO and NASK estimate that about one in four jobs worldwide is in occupations potentially exposed to generative AI; the source distinguishes transformation from replacement.",
      "evidence_kind": "exposure_model",
      "source": {
        "title": "ILO / NASK: Refined Global Index of Occupational Exposure",
        "url": "https://www.ilo.org/resource/news/one-four-jobs-risk-being-transformed-genai-new-ilo%E2%80%93nask-global-index-shows",
        "published": "2025-05-20"
      },
      "checked_at": "2026-09-08",
      "scope": "Global task-based exposure classification, combining occupational tasks, expert input, AI scoring and labor microdata.",
      "limitation": "Potential technical exposure is not observed adoption, feasible whole-job automation or a forecast of layoffs.",
      "next_check": "Pair exposure with actual adoption, wages, employment and transitions in the geography being discussed.",
      "citation_url": "https://digidai.github.io/research/ai-employment/#ilo-exposure-2025",
      "markdown_url": "https://digidai.github.io/research/ai-employment/index.md"
    },
    {
      "id": "ai-employment-ilo-indicators-2026",
      "topic": "ai-employment",
      "topic_title": "AI and employment evidence",
      "subject": "Interpreting exposure indicators",
      "question": "What can an exposure indicator predict on its own?",
      "answer": "ILO's 2026 methodological brief warns against reading exposure measures alone as predictions of job losses or labor-market outcomes.",
      "evidence_kind": "methodology_update",
      "source": {
        "title": "ILO: What AI exposure indicators reveal about jobs",
        "url": "https://www.ilo.org/resource/news/new-ilo-brief-explains-what-ai-exposure-indicators-reveal-about-jobs",
        "published": "2026-04-17"
      },
      "checked_at": "2026-09-08",
      "scope": "Interpretive guidance on exposure indicators, rather than a new measured job-loss count.",
      "limitation": "The warning does not imply no displacement; it specifies what this type of indicator cannot establish.",
      "next_check": "Identify which result is an exposure score and which uses actual labor-market outcomes before combining studies.",
      "citation_url": "https://digidai.github.io/research/ai-employment/#ilo-indicators-2026",
      "markdown_url": "https://digidai.github.io/research/ai-employment/index.md"
    },
    {
      "id": "ai-employment-stanford-canaries-2026",
      "topic": "ai-employment",
      "topic_title": "AI and employment evidence",
      "subject": "Young workers and employment",
      "question": "Are employment changes concentrated in particular groups?",
      "answer": "Stanford's August 2026 revision reports a 19% employment gap for young workers in highly AI-exposed occupations relative to less-exposed peers, while reporting no widespread economy-wide displacement.",
      "evidence_kind": "observational_study",
      "source": {
        "title": "Stanford Digital Economy Lab: Canaries August 2026 update",
        "url": "https://digitaleconomy.stanford.edu/news/canariesaug26/",
        "published": "2026-08-12"
      },
      "checked_at": "2026-09-08",
      "scope": "ADP payroll evidence through June 2026. Workers aged 22 to 25 in the two most-exposed quintiles are compared with similarly aged workers in the three least-exposed quintiles, relative to November 2022.",
      "limitation": "The authors describe these as non-causal patterns, with education, pre-trends and representativeness caveats. The gap is not AI-caused layoffs, an unemployment rate or 19% of all young workers losing jobs.",
      "next_check": "Check the paper revision, cohort definition, comparison group and robustness checks before making a causal or national-population claim.",
      "citation_url": "https://digidai.github.io/research/ai-employment/#stanford-canaries-2026",
      "markdown_url": "https://digidai.github.io/research/ai-employment/index.md"
    },
    {
      "id": "ai-employment-denmark-labor-2026",
      "topic": "ai-employment",
      "topic_title": "AI and employment evidence",
      "subject": "Earnings and recorded hours",
      "question": "Do reported task benefits show up in average labor outcomes?",
      "answer": "The March 2026 NBER revision estimates null effects on earnings and recorded hours at worker and workplace levels, ruling out effects larger than 2% in its study horizon.",
      "evidence_kind": "observational_study",
      "source": {
        "title": "Humlum and Vestergaard: Still Waters, Rapid Currents (NBER 33777, March 2026 revision)",
        "url": "https://www.nber.org/papers/w33777",
        "published": "2026-03"
      },
      "checked_at": "2026-09-08",
      "scope": "Danish adoption surveys linked to administrative records, studying the first two years after ChatGPT. Previously titled Large Language Models, Small Labor Market Effects.",
      "limitation": "Null average earnings and hours effects do not mean no task change, no benefit for individual adopters or no future employment effects elsewhere.",
      "next_check": "Use the revised title and estimates; distinguish the measured horizon from the publication date and the earlier version's estimates.",
      "citation_url": "https://digidai.github.io/research/ai-employment/#denmark-labor-2026",
      "markdown_url": "https://digidai.github.io/research/ai-employment/index.md"
    },
    {
      "id": "ai-employment-anthropic-cadences-2026",
      "topic": "ai-employment",
      "topic_title": "AI and employment evidence",
      "subject": "Observed Claude usage",
      "question": "What does usage telemetry say about employment?",
      "answer": "Anthropic's June 2026 report changes its sampling and classification pipeline to study hourly use and outputs, with separate reporting for Claude conversations and first-party API traffic.",
      "evidence_kind": "usage_telemetry",
      "source": {
        "title": "Anthropic Economic Index: Cadences",
        "url": "https://www.anthropic.com/research/economic-index-june-2026-report",
        "published": "2026-06-26"
      },
      "checked_at": "2026-09-08",
      "scope": "Vendor-observed use of Claude products and first-party API; methods changed from earlier seven-day report samples.",
      "limitation": "Claude usage is not a representative sample of all workers, a verified task-success census or a causal measure of jobs created or lost.",
      "next_check": "Read the methodology appendix before comparing report editions; keep platform use, self-reported benefits and administrative employment outcomes separate.",
      "citation_url": "https://digidai.github.io/research/ai-employment/#anthropic-cadences-2026",
      "markdown_url": "https://digidai.github.io/research/ai-employment/index.md"
    }
  ]
}