# Thinking beyond Bias: Analyzing Multifaceted Impacts and Implications of AI on Gendered Labour

**Authors:** Satyam Mohla, Bishnupriya Bagh, Anupam Guha  
**arXiv:** [2406.16207](https://arxiv.org/abs/2406.16207)  
**Date:** 2024-06-23  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2406.16207v1/

## Key points

- AI is a pivotal force in global production, impacting gender dynamics in labor
- This paper goes beyond biases to explore the multifaceted impacts of AI on gendered labor
- Authors emphasize the need to understand how AI is reshaping work structures and fueling platform-based models
- AI exacerbates job insecurity, especially for women, leading to exclusion from digital labor force
- Urgent attention is required from the research community to address gender disparities in AI-driven transformations

## Summary

Artificial Intelligence (AI) has become a pivotal force in global production, exerting significant influence on gender dynamics within the realm of labor. Existing discussions often focus on AI's role in perpetuating epistemic biases. However, this paper delves deeper into the multifaceted impacts and implications of AI on gendered labor. The authors - Satyam Mohla, Bishnupriya Bagh, and Anupam Guha - highlight the urgent need to broaden our understanding of how AI is reshaping the nature of work beyond bias generation. By examining AI as an integral component of the larger economic structure, the paper sheds light on how it is fueling the expansion of platform-based work models and exacerbating job insecurity, particularly for women. Of particular concern is the growing exclusion of women from meaningful participation in the digital labor force - a critical issue that demands immediate attention from the AI research community. To address these challenges effectively, a nuanced examination of economic transformation and its implications for gender equity is essential. By uncovering these intersections between AI and gendered labor dynamics, this paper aims to stimulate comprehensive discussions and catalyze targeted actions aimed at mitigating the gender disparities accentuated by AI-driven transformations. The research conducted by Mohla, Bagh, and Guha underscores the importance of considering not only biases but also broader societal impacts when analyzing AI's effects on gendered labor. This nuanced approach provides valuable insights for policymakers, industry stakeholders, and researchers striving to create more inclusive and equitable workplaces in an increasingly AI-driven world.