JPN-001

Hello Work AI-Enhanced Job Matching and Career Guidance System

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Japan East Asia & Pacific High income Pilot / Controlled Trial Phase Confirmed

Ministry of Health, Labour and Welfare (MHLW) - Public Employment Security Office (Hello Work)

At a Glance

What it does Ranking and decision systems — Matching and recommendation
Who runs it Ministry of Health, Labour and Welfare (MHLW) - Public Employment Security Office (Hello Work)
Programme Hello Work AI-Enhanced Job Matching and Career Guidance System
Confidence Confirmed
Deployment Status Pilot / Controlled Trial Phase
Key Risks Model-related risks
Key Outcomes No quantitative outcomes reported as of April 2025.
Source Quality 6 sources — Legal document / regulation, News article / media, Report (government / official), +2 more

Japan's Ministry of Health, Labour and Welfare (MHLW) is developing and piloting an artificial intelligence system to enhance job-matching services and career guidance at Hello Work, the country's national network of public employment service offices. Hello Work operates through 544 locations nationwide, serving approximately 4.4 million registered job seekers and handling 9.95 million job openings. The AI initiative was formally launched in September 2024, when MHLW established an internal AI Consideration Project Team led by the Director of the Vocational Stability Bureau. The project team commissioned PwC Consulting LLC to conduct a formal study, which included stakeholder interviews with AI vendors, technology development companies, and Hello Work frontline personnel. The resulting report was published on 31 March 2025, and a public summary was released by MHLW on 22 April 2025.

The AI system under development comprises two principal components. The first is a staff-facing recommendation engine designed to support Hello Work counsellors in their job-matching activities. This component uses machine learning trained on approximately ten million historical records of job seeker profiles and employer vacancy data to propose optimised job matches. The system analyses application rates and hiring success rates, then outputs the top ten recommended positions for a given job seeker, with reasoning provided for each suggestion. It also generates advisory recommendations for employers whose vacancies are proving difficult to fill, suggesting specific adjustments such as relaxing certification requirements or adjusting wage levels to improve fill rates. Hello Work counsellors review all AI-generated recommendations before presenting them to job seekers, maintaining a human-in-the-loop oversight model.

The second component is a public-facing concierge function utilising generative AI, deployed on the Hello Work Internet Service (HWIS), which receives approximately 78 million monthly accesses and through which more than 80 percent of job postings are submitted. This chatbot-style system provides automated responses covering basic Hello Work information, unemployment insurance procedures, subsidy consultations, job posting guidance, and terminology explanations, available on a 24-hour, seven-day basis. The concierge function is intended to reduce the burden on frontline staff and improve service accessibility for users who cannot visit physical Hello Work offices during business hours.

The initiative operates under the explicit principle, stated in the MHLW summary, that AI will not replace all staff work but serves as a tool to enhance the convenience of Hello Work services. This framing reflects a deliberate decision to position AI as an advisory support mechanism rather than an autonomous decision-making system. The staff-facing recommendation engine began pilot testing at ten Hello Work locations nationwide in fiscal year 2025 (beginning April 2025), with the public-facing concierge function scheduled for launch in January 2026.

The context for this initiative includes significant structural challenges facing Japan's public employment services. Job seeker registrations at Hello Work declined by 26.3 percent over the decade from 2014 to 2024, while the current job-matching success rate stands at just 25.9 percent. Japanese Hello Work counsellors manage a caseload of approximately 173 unemployed persons each, substantially higher than comparable public employment services in Germany (15 per counsellor), France (38), and Sweden (42). The AI system is intended to help address this staffing imbalance by augmenting counsellor capacity.

The regulatory and governance framework for this initiative is shaped by several instruments. The Act on the Protection of Personal Information (APPI), administered by the Personal Information Protection Commission, governs the handling of personal data including job seeker profiles and employment records. APPI applies uniformly to both public and private sector entities and does not provide special exemptions for employment data. The Digital Agency's Guideline for Japanese Governments' Procurements and Utilizations of Generative AI, approved on 27 May 2025, establishes procurement and risk management requirements for generative AI systems in government operations, including the appointment of a Chief AI Officer (CAIO) within each ministry and mandatory reporting of risk incidents. The OECD's 2025 report on Artificial Intelligence and the Labour Market in Japan specifically recommended that MHLW enhance the matching functions of Hello Work through AI and that the Basic Policy on Economic and Fiscal Management and Reform 2025 promoted AI use at Hello Work to improve job matching and staff working conditions.

The data types likely used include job seeker profiles, employment histories, qualifications, vacancy descriptions, application records, and hiring outcome data. No official schema or data dictionary has been published for the AI pilots. The MHLW project team report acknowledges risks and challenges requiring ongoing monitoring but has not published a formal risk assessment framework or detailed bias audit procedures specific to the AI components.

Classifications follow the DCI AI Hub Taxonomy. Hover over field labels for definitions.

Social Protection Functions

Implementation/delivery chain
Profiling, job matching and support services primaryOutreach/communications/sensitisation
SP Pillar (Primary) The social protection branch: social assistance, social insurance, or labour market programmes. Labour market programmes
Programme Name Hello Work AI-Enhanced Job Matching and Career Guidance System
Programme Type The type of social protection programme, classified under social assistance, social insurance, or labour market programmes. View in glossary Public employment services
System Level Where in the social protection system the AI is applied: policy level, programme design, or implementation/delivery chain. View in glossary Implementation/delivery chain
Programme Description Japan's national public employment service network (Hello Work), operated by MHLW through 544 locations nationwide. The AI initiative enhances job matching and career guidance through a staff-facing ML recommendation engine and a public-facing generative AI concierge function on the Hello Work Internet Service.
Implementation Type How the AI output is produced: Classical ML, Deep learning, Foundation model, or Hybrid. Affects validation, compute requirements, and governance profile. View in glossary Hybrid
Lifecycle Stage Current stage in the AI lifecycle, from problem identification through to monitoring, maintenance and decommissioning. View in glossary Integration and Deployment
Model Provenance Origin of the AI model: developed in-house, adapted from open-source, commercial/proprietary, or accessed via third-party API. View in glossary Not documented
Compute Environment Where the AI system runs: on-premise, government cloud, commercial cloud, or edge/device. View in glossary Not documented
Sovereignty Quadrant Classification of data and compute sovereignty: I (Sovereign), II (Federated/Hybrid), III (Cloud with safeguards), or IV (Shared Innovation Zone). View in glossary Not assessed
Hybrid Components Staff-facing component uses classical ML trained on historical job seeker and vacancy records for recommendation scoring. Public-facing concierge uses generative AI (foundation model) for natural language interaction and automated responses.
Data Residency Where the data used by the AI system is stored: domestic, regional, or international. View in glossary Not documented
Cross-Border Transfer Whether data crosses national borders, and if so, whether documented safeguards are in place. View in glossary Not documented
Is Agentic Whether the system autonomously plans and executes multi-step workflows, selecting tools and chaining actions with limited human intervention. View in glossary Partial
Agentic Pipeline Description of the chained workflow steps in the agentic pipeline. The generative AI concierge function operates semi-autonomously, receiving user queries and generating responses without immediate human review. The staff-facing recommendation engine generates ranked suggestions but all outputs are reviewed by counsellors before action.
Agentic Autonomy Degree of autonomy: fully autonomous, semi-autonomous (human checkpoints), or supervised (human approval at each step). Semi-autonomous
Override Points Where in the pipeline human review or override is triggered. Staff-facing recommendations: counsellors review and select from AI-generated matches before presenting to job seekers. Concierge function: automated responses with escalation to human staff for complex queries.
Decision Criticality The rights impact of the decision the AI supports. High criticality requires HITL oversight; moderate requires HOTL; low may operate HOOTL. View in glossary Moderate
Human Oversight Type Level of human involvement: Human-in-the-Loop (active review), Human-on-the-Loop (monitoring), or Human-out-of-the-Loop (periodic audit). View in glossary HITL
Development Process Whether the AI system was developed fully in-house, through a mix of in-house and third-party, or fully by an external provider. View in glossary Mix of in-house and third-party
Highest Risk Category The most significant structural risk source identified: data, model, operational, governance, or market/sovereignty risks. View in glossary Model-related risks
Risk Assessment Status Whether a formal risk assessment, informal assessment, or independent audit has been conducted for this system. Informal assessment

Impact Dimensions

Autonomy, human dignity and due process
Equality, non-discrimination, fairness and inclusion
Systemic and societal
  • Human oversight protocol
CategorySensitivityCross-System LinkageAvailabilityKey Constraints
Administrative data from other sectorsPersonalLinks data across multiple systemsCurrently available and usedEmployer vacancy records, application rates, and hiring outcome data submitted through Hello Work and HWIS. Over 80% of job postings submitted via HWIS.
Beneficiary registries and MISPersonalLinks data across multiple systemsCurrently available and usedJob seeker profiles including employment histories, qualifications, and job preferences. ML model trained on approximately 1 million historical records. No official data schema published.

Digital Agency (2025). The Guideline for Japanese Governments' Procurements and Utilizations of Generative AI for the sake of Evolution and Innovation of Public Administration. Tokyo: Digital Agency (approved 27 May 2025). Available at: https://www.digital.go.jp/en/news/3579c42d-b11c-4756-b66e-3d3e35175623 (Accessed 24 Mar 2026).

View source Legal document / regulation

Japan Telecommunications Users Association (JTUA) (2025). 'ハローワークがAIを活用した実証実験を開始' [Hello Work Begins AI Demonstration Experiments]. Tokyo: JTUA (Oct 2025). Available at: https://www.jtua.or.jp/ict/solution/reform/ai-jobmatching/202510_01/ (Accessed 24 Mar 2026).

View source News article / media

MHLW / PwC Consulting LLC (2025). 'ハローワークにおけるAI検討プロジェクトチーム' 結果報告書 [Hello Work AI Consideration Project Team Results Report] (令和7年3月31日). Tokyo: MHLW. Available at: https://www.mhlw.go.jp/content/11601100/001478788.pdf (Accessed 24 Mar 2026).

View source Report (government / official)

Ministry of Health, Labour and Welfare (2025). Press release: '将来を見据えたハローワークにおけるAI活用について' を公表 [Looking Ahead: AI Utilization in Hello Work]. Tokyo: MHLW (22 Apr 2025). Available at: https://www.mhlw.go.jp/stf/houdou/newpage_57223.html (Accessed 24 Mar 2026).

View source Government website / press release

OECD (2025). Artificial Intelligence and the Labour Market in Japan. Paris: OECD Publishing. Available at: https://www.oecd.org/en/publications/artificial-intelligence-and-the-labour-market-in-japan_b825563e-en.html (Accessed 24 Mar 2026).

View source Report (multilateral / development partner)

Personal Information Protection Commission (2023). Act on the Protection of Personal Information (Consolidated text). Tokyo: PPC. Available at: https://www.ppc.go.jp/en/legal/ (Accessed 24 Mar 2026).

View source Legal document / regulation
Deployment Status How far the system has progressed into real-world operational use, from concept/exploration through to scaled and institutionalised. View in glossary Pilot / Controlled Trial Phase
Year Initiated The year the AI system was first initiated or development began. 2024
Scale / Coverage The scale and geographic or population coverage of the deployment. Pilot at 10 Hello Work locations nationwide (FY2025); full network comprises 544 locations serving 4.4 million job seekers
Funding Source The source(s) of funding for the AI system development and deployment. MHLW operational budget (commissioned study to PwC Consulting LLC)
Technical Partners External technology vendors, academic partners, or development partners involved. PwC Consulting LLC (commissioned study and results report); Hello Work Internet Service digital platform operated by MHLW
Outcomes / Results No quantitative outcomes reported as of April 2025. Pilot demonstrations commenced in FY2025 at 10 locations. Current Hello Work matching success rate is 25.9 percent; AI system intended to improve this rate.
Challenges Job seeker registrations declined 26.3% over 2014-2024 decade. Japanese Hello Work counsellors manage 173 unemployed persons each, far exceeding comparable ratios in Germany (15), France (38), and Sweden (42). MHLW report acknowledges risks and challenges requiring ongoing monitoring but has not published detailed risk mitigation procedures.

How to Cite

DCI AI Hub (2026). 'Hello Work AI-Enhanced Job Matching and Career Guidance System', AI Hub AI Tracker, case JPN-001. Digital Convergence Initiative. Available at: https://socialprotectionai.org/use-case/JPN-001 [Accessed: 1 April 2026].

Change History

Created 30 Mar 2026, 08:40
by v2-import (import)