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Artificial Intelligence (AI) has applications in labour and industrial relations for workforce management, negotiations, dispute resolution, and policymaking. It can be used for everything from delivering advanced analytics for strategic decision-making to automating administrative duties. AI is pertinent to labor and industrial relations because of its capacity to gather and analyze vast volumes of data, identify patterns and trends, and produce predictions or recommendations based on this research. Workforce planning and optimization, performance management and evaluation, compensation and benefits analysis, labour market analysis and forecasting, collective bargaining and negotiations, compliance and risk management, employee engagement and satisfaction, and other related fields can all benefit from this capacity. AI is expected to play a bigger part in labour and industrial relations as it develops, thereby upending preconceived notions about what it means to work and be employed. AI has a wide range of applications in labour and industrial relations. Here are a few major domains where AI is becoming increasingly prevalent [1]. By automating resume screening, interviewing candidates, and even projecting candidate success based on past performance, AI-powered solutions can expedite the hiring process. These technologies can enhance the effectiveness of talent acquisition procedures and lessen prejudice in recruiting decisions. AI systems are capable of analysing worker data to forecast employee attrition, find skill gaps, and optimize employment numbers. This can assist companies in allocating resources, hiring personnel, and providing training with better information. By evaluating numerous variables and seeing trends in employee performance, AI can offer more unbiased and data-driven performance reviews. Fairer evaluations and more focused development strategies may result from this. The systematic field of industrial artificial intelligence (AI) is centred on creating, evaluating, and implementing different machine learning algorithms for industrial applications that have long-term performance.

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Legal and Ethical Considerations in AI-Driven IR

  • 章节

  • 发表日期：

    2026年

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  • Soumi Majumdar,
  • Bitan Misra

摘要

Artificial Intelligence (AI) has applications in labour and industrial relations for workforce management, negotiations, dispute resolution, and policymaking. It can be used for everything from delivering advanced analytics for strategic decision-making to automating administrative duties. AI is pertinent to labor and industrial relations because of its capacity to gather and analyze vast volumes of data, identify patterns and trends, and produce predictions or recommendations based on this research. Workforce planning and optimization, performance management and evaluation, compensation and benefits analysis, labour market analysis and forecasting, collective bargaining and negotiations, compliance and risk management, employee engagement and satisfaction, and other related fields can all benefit from this capacity. AI is expected to play a bigger part in labour and industrial relations as it develops, thereby upending preconceived notions about what it means to work and be employed. AI has a wide range of applications in labour and industrial relations. Here are a few major domains where AI is becoming increasingly prevalent [1]. By automating resume screening, interviewing candidates, and even projecting candidate success based on past performance, AI-powered solutions can expedite the hiring process. These technologies can enhance the effectiveness of talent acquisition procedures and lessen prejudice in recruiting decisions. AI systems are capable of analysing worker data to forecast employee attrition, find skill gaps, and optimize employment numbers. This can assist companies in allocating resources, hiring personnel, and providing training with better information. By evaluating numerous variables and seeing trends in employee performance, AI can offer more unbiased and data-driven performance reviews. Fairer evaluations and more focused development strategies may result from this. The systematic field of industrial artificial intelligence (AI) is centred on creating, evaluating, and implementing different machine learning algorithms for industrial applications that have long-term performance.