Context: 

Recently, the United Nations’ International Labour Organization (ILO) released a report on the impact of Generative AI and other platforms on jobs quantity and quality.

Findings of Report:

• Job Augmentation: Most jobs and industries are only partially exposed to automation and are more likely to be complemented rather than substituted by AI.
  • Generative AI systems similar to GPT (Generative Pre-trained Transformer) are more likely to become productivity tools, supporting and speeding up the execution of some tasks within certain occupations.

• Fears Over Job Destruction: The surge in generative AI and its chatbot applications has sparked concerns about job destruction, akin to the concerns surrounding the introduction of the moving assembly line in the 1950s.
  • Technology can enhance job quality in the workplace by automating routine tasks, allowing more engaging work, but also limiting worker agency or increasing work intensity.
• Impact on Countries: Recent progress in machine learning, in particular developments around Large Language Mode (LLMs), is likely to have disruptive effects on labour markets, with larger effects in high-income countries and specific occupational groups.
  • 5.5 percent of total employment in high-income countries was potentially exposed to the automating effects of generative AI, whereas only 0.4 percent of employment in low-income countries.
• Professional Variation: Clerical support workers face the greatest risk of being affected by AI. 
  • About 25% of clerical workers’ tasks face a high exposure to generative AI, and 58% of their tasks face a medium exposure to the technology.
  • Example: Jobs such as typists, travel consultants, scribes, contact center information clerks, bank tellers, and survey and market research interviewers that could eventually be automated.
  • Only 4% of service and sales workers’ tasks face high exposure to generative AI, and 18% of their tasks face medium exposure. 
  • Thus impact is not likely to be jobs reduction but rather the potential changes to the quality of jobs, notably work intensity and autonomy.
• Gendered Impact: Effects of automation are “highly gendered”, with more than double the share of women potentially affected by automation, due to their overrepresentation in clerical work, especially in high- and middle-income countries.
  • In high-income countries, 7.8% of jobs held by women ( around 21 million jobs) have the potential to be automated.
  • In contrast, only 2.9% of jobs in high-income countries held by men (around 9 million jobs) face the potential of being automated.
  • The recent increase in women’s labor market participation may be threatened by concentrated job losses in female-dominated occupations.

• Digital Divide: Generative AI technology is dependent on access and cost of broadband connectivity, as well as electricity. 
  • In 2022, one-third of the global population (around 2.7 billion people), still did not have access to the internet.
• Access to Electricity: According to the World Bank Enterprise Survey, 49% of registered firms in developing countries experienced electrical outages, averaging 4.5 days per month.
  • Without proper policies in place, there is a risk that only some of the well-positioned countries and market participants will be able to harness the benefits of the transition, while the costs to affected workers could be brutal.

About Generative Artificial Intelligence (AI):

• It is a type of AI technology that can produce various types of content, including text, imagery, audio and synthetic data.
• It utilizes deep learning, neural networks, and machine learning techniques to enable computers to produce content that closely resembles human-created output autonomously. 
• Example:  ChatGPT, DALL-E, and Bard.

Report Recommendations:

• All Stakeholder Approach: Consultation and negotiation between employers and workers is critical for managing the transition process as it encourages redeployment and training over job loss. 
• Adhering to The ILO’s Employment Protection Convention (No. 158, 1982): To reduce negative externalities from dismissal and support an orderly process that balances workers’, employers’, and societies’ needs.
  • It includes provisions on the termination of employment for technological reasons. 
• Investing in the Care Economy: Improving job quality for care economy workers can provide decent employment and meet society’s care needs to offset the negative externalities of Generative AI in the long term. 
  • It requires increased investment, training, and income support during the transition.
  • Potential: According to the ILO, achieving the SDG targets would require more than double employment in care economy sectors from 206 million in 2015 to 475 million in 2030.
• Quality Adherence: Ensure quality of the new jobs created as a result of technological change.

News Source: Economic Times