Translation: Employment, social security, talent and personnel affairs, labor relations, and public services are all closely related to people's livelihood security. The "15th Five-Year Plan" outline proposes to "deepen and expand 'AI+,' empowering economic and social development and governance capacity enhancement." Recently, the Ministry of Human Resources and Social Security, the National Development and Reform Commission, the Ministry of Industry and Information Technology, and the National Data Administration jointly issued the "Opinions on Accelerating the Development of 'AI+ Human Resources and Social Security' Applications," aiming to accelerate the implementation of the "AI+" initiative, promote synergistic optimization of the AI industry and human resources and social security work through creating major AI application scenarios. This issue invites experts to discuss relevant issues. Developing Digital-Intelligent Employment to Promote Efficient Job Matching How should we understand the meaning of digital-intelligent employment? What role does it play in improving the efficiency of employment service management? Bao Chunlei (Director of the Think Tank Work Office, China Academy of Labor and Social Security, Researcher): Employment is the most basic people's livelihood issue. The "Opinions on Accelerating the Development of 'AI+ Human Resources and Social Security' Applications" proposes developing digital-intelligent employment and creating a new model for employment service management. Digital-intelligent employment focuses on meeting needs in employment management, policy application, employment services, and entrepreneurship services, utilizing AI technology to accelerate the integrated linkage of "job demand + skills training + skills evaluation + employment services," continuously expand AI application scenarios in the employment and entrepreneurship fields, and improve the efficiency of employment service management. Developing digital-intelligent employment is an inevitable requirement for promoting high-quality development of employment services in the digital economy era, and also a key measure to address structural employment contradictions and assist key groups in employment and entrepreneurship. Overall, digital-intelligent employment has the following distinctive characteristics: 1. Data-Driven: By building a unified employment information resource database, integrating multi-dimensional data from workers, employers, and market institutions, a dynamic employment data foundation covering all areas is formed, providing foundational support for precise services. 2. Intelligent Matching: Leveraging occupational classification knowledge graphs and job-seeking/recruitment models, a two-way digital profile of enterprise labor demand and job seekers' labor skills is constructed, effectively breaking down information barriers, helping job seekers find suitable positions faster, and reducing enterprise recruitment costs. 3. Precise Services: Systematically integrating functions such as unemployment registration, information services, subsidy applications, and employment guidance, precision profiling technology is used to provide personalized career guidance and entrepreneurship services for different groups. 4. Smart Governance: Leveraging smart employment operation monitoring and risk early warning, employment market dynamics are scientifically analyzed, unemployment risks are predicted in advance, and data support is provided for employment policy formulation

Translation: Under the dual drive of policy guidance and technological empowerment, digital-intelligent employment is accelerating its expansion into multiple scenarios and sectors. In intelligent recruitment and interviews: Standardized job-matching models are built upon occupational classification knowledge graphs, and new models such as digital human live-streaming for job postings, remote recruitment, and smart interviews are widely implemented. In Long'an County, Guangxi, the "Xiao Liang Job Delivery" digital platform created an employment service scenario combining "real-person + digital human job delivery + smart stations + data visualization," effectively addressing issues such as information asymmetry and untimely matching for rural laborers seeking employment outside their hometowns. In smart employment service management: Many regions have established integrated online service platforms, shifting employment services from "offline processing" to "online and mobile handling." Shandong Province, using 30 digital-intelligent employment service zones as a抓手 (key measure), promoted the digital-intelligent employment service model, accelerated innovative applications of big data and AI in employment fields, and has already built 45 "AI+Employment" application scenarios, cumulatively serving over 2 million employment-related cases. Digital-intelligent employment construction has been incorporated into the province's "15th Five-Year Plan" outline. In smart employment monitoring: Various regions are actively building employment information monitoring platforms, leveraging multi-dimensional data to real-time track employment and labor usage changes within their jurisdictions, achieving dynamic monitoring and early warning of employment scale, structure, and mobility trends. In Nanyang, Henan, the smart employment platform integrated government data resources, dynamically generating regional labor supply-demand heat maps and industry talent gap indices, enabling advance prediction of structural employment contradictions. Currently, digital-intelligent employment is evolving from single-point breakthroughs to a full-chain service ecosystem, becoming an important engine for promoting high-quality and full employment. However, it should also be acknowledged that digital-intelligent employment still faces certain constraints during its advancement. For example: Uneven service foundations: There are significant gaps between central/western regions and eastern regions, as well as between urban and county-level areas. Incomplete cross-departmental data sharing mechanisms: "Information silos" still exist. Shortage of composite talents: There is insufficient reserve of professionals who understand both business operations and digital literacy. Digital divide: Older workers and low-skilled laborers face challenges in fully benefiting from digital-intelligent employment dividends. To address these issues, we should base our efforts on grassroots realities, continuously strengthen guarantees, and promote effective policy implementation. Three Key Recommendations: 1. Strengthen Policy Guidance and Foundation Support, Perfecting the Digital-Intelligent Employment Service System Strengthen infrastructure construction for digital-intelligent employment and increase support for central/western regions and county-level areas. Improve data sharing rules and coordination mechanisms, break down cross-departmental data barriers, and solidify the employment foundation. Promote talent development for digital-intelligent employment services, conduct digital skills training for grassroots employment service personnel, and build a service team with both strong professional capabilities and digital operation skills. 2. Promote Scenario Innovation and Ecosystem Building, Stimulating Digital-Intelligent Employment Development Momentum Encourage regions to explore differentiated digital-intelligent employment application scenarios based on industrial characteristics and employment service needs. Support business entities in participating in the construction and operation of digital-intelligent employment platforms, enriching service content such as career guidance and rights protection. Optimize mechanisms to promote direct employment services to employers, building a digital-intelligent employment service ecosystem with precise supply-demand matching, comprehensive service coverage, and outstanding governance effectiveness. 3. Promote Inclusive Sharing and Precise Empowerment, Expanding and Improving Digital-Intelligent Employment For groups with weaker digital literacy, such as older workers and low-skilled laborers, develop convenient functional modules adapted to their usage habits, and optimize on-site employment service interfaces and processes. Encourage grassroots institutions to reasonably equip digital service guides, providing timely business agency and simple terminal operation guidance. Improve full-process tracking and management mechanisms, using intelligent means to achieve precise and refined services for key groups. 

IV. Strengthen Information Protection and Algorithm Regulation to Build a Robust Security Defense for Digital Intelligence Employment Digital intelligence employment platforms collect and store vast amounts of personal information from job seekers and employment data from enterprises during operation. It is urgently necessary to establish a strict data classification and grading management system, clarify security standards, implement the platform's main responsibility, and prevent risks of data leakage and misuse. We must regulate the application of algorithms in AI-powered recruitment and job-person matching to prevent employment inequality caused by algorithmic discrimination, ensuring that digital intelligence employment operates within a compliant and secure framework, safeguarding the legitimate rights and interests of both workers and employers. Deepening Smart Social Insurance to Enhance Management and Service Capabilities What is the significance of promoting intelligent applications in the insurance service sector for improving the quality of public participation in social insurance? Fang Lianquan (Researcher at the Chinese Academy of Social Sciences' Institute of Chinese Modernization and Director of the World Social Security Research Center): Social insurance has a wide coverage, large volume of business, and long policy chains. The Implementation Opinions on Accelerating the Development of "Artificial Intelligence + Human Resources and Social Security" Applications proposes deepening smart social insurance to promote new improvements in management and service capabilities. Generally, social insurance mainly includes basic old-age insurance, basic medical insurance, work-related injury insurance, unemployment insurance, and maternity insurance, safeguarding citizens' rights to receive material assistance according to law in situations such as old age, illness, work-related injury, unemployment, and childbirth. Deepening smart social insurance aims to promote intelligent applications in acceptance, review, service, risk control, and other links, innovate social security handling service models, and continuously improve the quality of public participation in social insurance. China's social insurance has a large number of participants and a huge fund revenue and expenditure scale. According to data from the Ministry of Human Resources and Social Security, as of the end of March this year, the number of participants in national basic old-age insurance, unemployment insurance, and work-related injury insurance (including occupational injury protection) reached 1.073 billion, 247 million, and 319 million respectively, increasing by 1.87 million, 2.8 million, and 21.52 million year-on-year respectively. In the first quarter, the total revenue of the three social insurance funds was 2.54 trillion yuan, and total expenditure was 2.06 trillion yuan. Such a large number of participants and massive fund operations place higher demands on handling efficiency, service quality, and risk prevention. Deepening smart social insurance offers the following advantages: 表格AdvantageDescription1. More Efficient ServicesThrough data sharing, systems proactively identify service needs of insured individuals, shifting from "people seeking policies" to "policies seeking people" 2. More Precise ManagementCorrelation analysis of insurance status, payment records, benefit eligibility, and other information helps handling staff identify anomalies and reduce errors3. More Coordinated OperationsImproving cross-regional social insurance relationship transfers and deepening model applications with cross-departmental collaboration can effectively enhance intelligent management and supervision of social insurance funds Promoting intelligent applications in the insurance service sector is both an urgent need and an inevitable trend. Recent Progress in Digital Intelligence of Insurance Services In recent years, China's level of digital intelligence in the insurance service sector has continued to improve. As of the end of March this year, the National Social Security Public Service Platform has launched 104 nationwide, cross-regional social insurance services, with cumulative visits exceeding 10 billion person-times. Matters such as social insurance relationship transfers, benefit calculations, and eligibility certification have gradually been realized through online and cross-province processing. Some regions have conducted silent certification for pension benefit eligibility through data comparison among human resources, medical insurance, civil affairs, and other departments, allowing retirees to avoid visiting service windows, greatly improving social security benefit audit capabilities. These positive changes indicate that social security information construction has gradually shifted from single-business online processing to cross-regional, cross-departmental collaboration and full-insurance-type, full-process management. The empowerment of existing platforms, data, and service channels has laid an important foundation for AI to accelerate penetration into participation analysis, benefit audit, and fund supervision, and has also created conditions for deepening smart social insurance to expand from local exploration to systematic application. For example, in the medical insurance service sector, as of the end of 2025, the number of people who have activated the National Medical Insurance Code exceeded 1.25 billion, and the number of real-name users on the integrated medical insurance service platform reached 660 million. In 2025, cross-province and cross-region medical treatment direct settlement benefited 308 million person-times, reducing advance payments by the public by 207.506 billion yuan. Key Policy Background The Ministry of Human Resources and Social Security, along with the National Development and Reform Commission, the Ministry of Industry and Information Technology, and the National Data Administration jointly issued the Implementation Opinions on Accelerating the Development of "Artificial Intelligence + Human Resources and Social Security" Applications . The policy outlines: 2026: Build "AI + Human Resources and Social Security" application infrastructure, create 20 application scenarios based on industry large models 2027: Popularize a batch of human resources and social security industry large models and agents, explore around 50 high-value application scenarios 2030: Form an innovative situation of widespread AI application in the human resources and social security sector The policy defines 67 specific application scenarios across six core areas, including digital intelligence employment (11 scenarios), smart social insurance (12 scenarios), precise talent cultivation, smart labor relations, smart human resources services, and smart human resources governance . 

The 15th Five-Year Plan Outline: Strengthening Social Insurance System Sustainability The "15th Five-Year Plan" outline has made deployments to enhance the sustainability of the social insurance system, proposing to "implement and fully implement the national pooling system for basic old-age insurance, basically achieve provincial-level pooling for basic medical insurance, and consolidate provincial-level pooling for unemployment and work-related injury insurance." Recently issued, the Human Resources and Social Security Development "15th Five-Year Plan" has made arrangements for optimizing social insurance handling and management services, requiring strengthened social security data governance and the establishment of a comprehensive "large-scale control" system for all insurance types. With the deepening of population aging, increased cross-regional mobility of workers, and the continuous development of flexible employment and new employment forms, changes in insurance status and service needs are accelerating. This requires localities to further align on policy implementation, handling processes, and data standards, promoting AI to identify renewal insurance needs from massive information, assist in auditing benefit application conditions, screen fund risk clues, and provide references for fund revenue-expenditure forecasting and policy evaluation. Challenges in AI Applications for Insurance Services We must also recognize that there are still shortcomings and weaknesses in AI applications in China's insurance service sector. For example: 表格ChallengeDescriptionDifferent Pooling LevelsDifferent insurance types have varying pooling levels and management approachesUnaligned StandardsBusiness rules and data standards across regions have not yet been alignedData Sharing GapsInsufficient data sharing and untimely updates affect model analysis and integrated processingSecurity RisksRisks such as information leakage and unauthorized use still existData Governance ChallengesStrengthening data governance and ensuring data integrity and consistency face challenges Key Measures for Future Development Overall, measuring the effectiveness of smart social insurance development hinges on whether public services are convenient, handling management is standardized, and fund operations are secure. In the future, deepening smart social insurance and promoting new improvements in management and service capabilities can focus on the following aspects: 表格PriorityAction Items1. Data Standards & Business NormsEstablish cross-departmental data sharing and quality verification mechanisms, improve policy knowledge base update mechanisms, and perfect a nationwide integrated social security handling service network2. High-Value ScenariosPrioritize expanding high-value scenarios such as insurance participation services, discontinuation identification, policy push, benefit application condition identification, relationship transfer and continuation, silent certification for benefit eligibility, and fraud prevention warnings; avoid low-level repetitive construction3. Intelligent Model ApplicationsPromote in-depth application of intelligent analysis models for insurance participation across acceptance, review, service, and risk control links; improve all-insurance-type, full-process risk control rule libraries and regulatory models; strengthen cross-departmental data comparison to enhance detection of abnormal benefits, duplicate benefits, and fraud clues; expand auxiliary applications for work injury prevention, work injury recognition, and labor capacity appraisal4. Online-Offline IntegrationRetain service windows, hotlines, and home visit services to provide convenient, barrier-free services for special groups Precision Talent Selection and Utilization: Building a New Intelligent Supply Mechanism How to build a virtuous ecosystem for talent "recruitment, development, retention, and utilization" relying on AI means? Sun Rui (Director of the Talent Theory and Technology Research Office, Chinese Academy of Personnel Sciences, and Researcher): The talent work of human resources and social security departments and the development and utilization of human resources go hand in hand, encompassing many links including talent cultivation, introduction, evaluation, mobility, utilization, incentive, and services, with characteristics of foundational nature, wide coverage, full-chain operation, and universality. The Implementation Opinions on Accelerating the Development of "Artificial Intelligence + Human Resources and Social Security" Applications proposes building six major AI application scenarios, with "strengthening precise talent cultivation and utilization, and building a new intelligent talent supply mechanism" being one of them. Currently, the new round of technological revolution and industrial transformation is accelerating, with new business formats, new positions, and new professions flourishing, and new fields, new growth drivers, and new tracks continuously emerging. Talent development needs, skill requirements, and service demands continue to iterate. Relying on AI to connect the full chain of talent "recruitment, development, retention, and utilization," grasping demands through data, matching resources based on algorithms, and providing precise training corresponding to needs to build a new intelligent talent supply mechanism with dynamic perception, precise adaptation, and full-domain coverage is not only a key measure for deepening the "AI+" action but also a necessary option for laying a solid talent foundation for comprehensively advancing Chinese modernization. 

Since the 18th National Congress of the Communist Party of China, Especially During the "14th Five-Year Plan" Period Since the 18th National Congress of the CPC, particularly during the "14th Five-Year Plan" period, human resources and social security departments have closely served national strategies and central tasks, taking human resource development and utilization as the main thread, focusing on building teams of professional and technical talents, skilled talents, and foreign expert talents. We have deepened institutional and mechanism reforms in talent cultivation, evaluation, utilization, mobility, incentives, and safeguards, expanded channels for introducing overseas talent and intellectual resources, continuously improved the talent management and service system, and further highlighted the role of talent in leading and driving high-quality development. Key Achievements 表格AchievementDescription1. Knowledge Update ProjectDeeply implemented to cultivate urgently needed talents adapting to new quality productive forces development2. Skills China ActionVigorously promoted the High-Level Skilled Talent Cultivation Plan to lead and drive skilled talent team building3. Professional Title System ReformMade important breakthroughs in optimizing talent evaluation standards, innovating title evaluation methods, and delegating title review authority4. Personnel File ServicesOptimized services for floating personnel files, achieving provincial and cross-province processing for file transfers5. Human Resources Market ConstructionGuided localities to build regional, professional, and industry-specific talent markets, expanding employment channels for key groups During the "14th Five-Year Plan" period, China's professional and technical talents exceeded 80 million, skilled workers exceeded 220 million, and high-level skilled talents exceeded 72 million, providing solid talent support for high-level scientific and technological self-reliance and new quality productive forces development. Challenges in the Digital Intelligence Era However, we must also recognize that under the impact of the digital intelligence wave, shortcomings in the traditional talent supply and service supply system have become prominent, with issues of unclear baseline, supply-demand mismatch, resource misallocation, and low efficiency, becoming invisible barriers facing talents in employment, entrepreneurship, and innovation. Specifically: 表格ChallengeDescription1. Fragmented Talent DataInformation gaps exist in industrial demand analysis2. Single Training ModelsDifficult to achieve personalized, differentiated instruction3. Imprecise Talent EvaluationPrecision of talent evaluation urgently needs improvement4. Inefficient Personnel File ManagementTalent factor mobility still faces obstacles Solving these problems urgently requires using AI to connect the full chain of talent "recruitment, development, retention, and utilization" and build a new intelligent talent supply mechanism. The "15th Five-Year Plan" Outline: Innovation in Incentive and Evaluation Mechanisms The "15th Five-Year Plan" outline has made deployments on linking and promoting innovation in incentive and evaluation mechanisms, proposing to "promote application research and technology development evaluation based on user and market feedback, and use new technologies and new products as important basis for performance assessment, title evaluation, and talent plan support." In the coming period, we need to use AI to drive the deep integration of talent chains, industrial chains, innovation chains, and service chains, continuously cultivating a massive army of knowledge-based, skilled, and innovative workers, using high-quality talent to cultivate new quality productive forces, empower livelihood improvement, and drive high-quality economic and social development. We need to carry out systematic planning around key links such as supply-demand, cultivation, evaluation, allocation, and safeguards, building a new intelligent talent supply mechanism with data interoperability, algorithm empowerment, and scenario implementation. Key Focus Areas 表格PriorityAction Items1. Unified Data FoundationConnect departmental data interfaces, promote cross-departmental talent information collection and sharing, build an integrated technical and skilled talent resource database, link enterprise employment and institutional cultivation data, and break "data silos." Rely on large models to capture market, industry, and talent information in real-time, build talent demand maps and talent atlases, and form dynamic perception and trend prediction mechanisms to provide digital decision support for regional talent intelligent search and resource matching2. AI + Continuing EducationExplore new paradigms for "AI + Continuing Education," particularly in identifying continuing education capability needs, building intelligent continuing education platforms, popularizing intelligent practical training scenarios, expanding remote intelligent training coverage, and promoting autonomous learning in the intelligent era, creating a personalized, full-domain, lifelong autonomous learning system3. Smart Evaluation and File ManagementFocus on human resources service needs in the digital intelligence era, promote intelligent and smart talent examination and personnel testing, build intelligent scenarios from question setting, examination, supervision to review and evaluation. Simplify evaluation application processes, achieve automatic material verification and online seamless review, and accelerate the formation of a talent evaluation system oriented by innovation value, capability, and contribution. Promote full-process intelligence for floating personnel file scanning and warehouse management, reduce repeated paper material submission links, and promote paperless and digital file management. Work to break invisible barriers to talent mobility and promote more sufficient talent flow and optimized allocation