Why Women Risk Being Left Behind by AI

Why is it in News?

• Recent analysis based on India’s latest Time Use Survey (2024) highlights a structural time poverty faced by women, raising concerns that the AI-driven future of work may deepen gender inequality.
• As India pushes AI-led growth through initiatives like the India AI Mission, evidence shows women lack time, access, and flexibility required to upskill for AI-era jobs.
• Aligns with broader debates on:
  • Automation risks
  • Right to disconnect
  • Gender budgeting
  • India’s Viksit Bharat @2047 vision.

Relevance

• GS I – Society
  • Gender roles and unpaid care work
  • Time poverty and gender inequality
• GS II – Governance
  • Gender budgeting
  • Social infrastructure (childcare, transport, water, energy)

Women’s Workload & Time Poverty

• Labour force participation (women): ~40% (2024).
• Average daily work (paid + unpaid):
  • Women: ~9.6 hours/day
  • Peaks at 70–80 hours/week for ages 25–39.
• Key driver:
  • ~40% of women outside the labour force cite household & caregiving responsibilities.
• Nature of work increase:
  • Over 80% of recent rise in women’s workforce participation comes from:
    • Unpaid family work
    • Low-paid self-employment
    • Informal, low-productivity jobs.

Gender Gap in Paid vs Unpaid Work

Across the Life Cycle

• Men:
  • Total work: 54–60 hours/week
  • Unpaid work: minimal and stable across ages.
• Women:
  • Total work exceeds men at almost all ages.
  • 25–39 age group:
    • Women spend 2× more time on unpaid caregiving than men.
    • Childcare is the largest component.
• Even in later life:
  • Men’s unpaid work rises marginally (elderly care),
  • Structural inequality at home persists across income, occupation, and age.

AI-Specific Risks for Women

1. Higher Automation Exposure

• Women overrepresented in:
  • Routine, clerical, low-skill service jobs
  • Informal and home-based work
• These roles are more automation-prone under AI adoption.

2. Algorithmic Bias

• AI-driven productivity metrics:
  • Ignore caregiving interruptions
  • Penalise time constraints
  • Reward uninterrupted, long-hour availability
• Care work remains invisible to algorithms.

3. Upskilling Time Deficit

• Women spend ~10 hours less per week than men on:
  • Learning
  • Skill enhancement
  • Self-development
• Gap widens to 11–12 hours/week in prime working years.
• Result:
  • Limited transition from low-skill to high-value AI-linked jobs.

Health & Well-being Costs

• Women sleep 2–2.5 hours less per week than men during peak working years.
• Time adjustment happens at the cost of:
  • Rest
  • Mental health
  • Physical well-being
• Long-term impact:
  • Lower productivity
  • Higher burnout
  • Reduced career longevity.

Policy & Governance Solutions Highlighted

1. Time-Centric Policy Design

• Shift from job-counting to outcome-based employment metrics.
• Explicit use of time-use data in:
  • Labour policy
  • Skill missions
  • AI governance.

2. Gender Budgeting as an Enabler

• Integrate time-use indicators into gender budgeting.
• Prioritise sustained spending on:
  • Affordable childcare
  • Elderly care services
  • Piped water
  • Clean cooking energy
  • Safe public transport.

3. AI-Era Upskilling for Women

• Design lifelong, flexible, modular skilling:
  • Local delivery
  • Hybrid / online formats
  • Low time-intensity learning
• Scale targeted programmes:
  • India AI Mission
  • AI Careers for Women
• Focus on:
  • Digital literacy
  • Applied AI tools
  • Locally relevant vocational tech skills.

Conclusion

• AI will not automatically empower women; without time-sensitive policy design, it may entrench inequality.
• Until women’s time is valued, freed, and integrated into growth strategy, India’s AI ambitions and Viksit Bharat @2047 vision will remain constrained by:
  • Invisible labour
  • Time poverty
  • Underutilised human capital.

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