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

Satyam Mohla, Bishnupriya Bagh, Anupam Guha

TL;DR

This paper argues that AI's impact on gendered labour cannot be reduced to bias or representation alone; it is shaped by capitalist production, platformization, and wage- and governance-related dynamics. It develops a political economy framework to analyze both microwork (data generation and supervision) and visible AI labour (leadership, hiring, and monitoring), highlighting how these processes produce and exacerbate gender inequalities. The authors point to concrete risks such as wage depression, job displacement, and underrepresentation in academia and policy, with IMF estimates underscoring the vulnerability of women's employment, particularly in the global south. They call for integrating social, economic, and governance considerations into AI research and policy, proposing an interdisciplinary, action-oriented research agenda to mitigate gender disparities created by AI-driven transformations.

Abstract

Artificial Intelligence with its multifaceted technologies and integral role in global production significantly impacts gender dynamics particularly in gendered labor. This paper emphasizes the need to explore AIs broader impacts on gendered labor beyond its current emphasis on the generation and perpetuation of epistemic biases. We draw attention to how the AI industry as an integral component of the larger economic structure is transforming the nature of work. It is expanding the prevalence of platform based work models and exacerbating job insecurity particularly for women. Of critical concern is the increasing exclusion of women from meaningful engagement in the digital labor force. This issue often overlooked demands urgent attention from the AI research community. Understanding AIs multifaceted role in gendered labor requires a nuanced examination of economic transformation and its implications for gender equity. By shedding light on these intersections this paper aims to stimulate in depth discussions and catalyze targeted actions aimed at mitigating the gender disparities accentuated by AI driven transformations.

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

TL;DR

This paper argues that AI's impact on gendered labour cannot be reduced to bias or representation alone; it is shaped by capitalist production, platformization, and wage- and governance-related dynamics. It develops a political economy framework to analyze both microwork (data generation and supervision) and visible AI labour (leadership, hiring, and monitoring), highlighting how these processes produce and exacerbate gender inequalities. The authors point to concrete risks such as wage depression, job displacement, and underrepresentation in academia and policy, with IMF estimates underscoring the vulnerability of women's employment, particularly in the global south. They call for integrating social, economic, and governance considerations into AI research and policy, proposing an interdisciplinary, action-oriented research agenda to mitigate gender disparities created by AI-driven transformations.

Abstract

Artificial Intelligence with its multifaceted technologies and integral role in global production significantly impacts gender dynamics particularly in gendered labor. This paper emphasizes the need to explore AIs broader impacts on gendered labor beyond its current emphasis on the generation and perpetuation of epistemic biases. We draw attention to how the AI industry as an integral component of the larger economic structure is transforming the nature of work. It is expanding the prevalence of platform based work models and exacerbating job insecurity particularly for women. Of critical concern is the increasing exclusion of women from meaningful engagement in the digital labor force. This issue often overlooked demands urgent attention from the AI research community. Understanding AIs multifaceted role in gendered labor requires a nuanced examination of economic transformation and its implications for gender equity. By shedding light on these intersections this paper aims to stimulate in depth discussions and catalyze targeted actions aimed at mitigating the gender disparities accentuated by AI driven transformations.
Paper Structure (6 sections, 3 figures)

This paper contains 6 sections, 3 figures.

Table of Contents

  1. A Critique of the Critique
  2. Political Economy of AI and Gendered Labour
  3. Microwork: neglected labour in AI Industry
  4. The visible AI workers
  5. Gendered Impact of the AI Industry
  6. What is to be done?

Figures (3)

  • Figure 1: Microwork done by platform wage workers provides expert knowledge & curated data which is essential for machine learning. However, not only is this work unrecognised, but also often exploitative.
  • Figure 2: Assisted recruitment using algorithms for emotion detection, filtering etc. has often suffered from biases, including misogyny & racism. Emotion algorithms especially promote the pseudoscience of pathognomy, & rely on the unfounded assumption that it is possible to accurately infer internal emotional states solely from facial cues.
  • Figure 3: Disproportionate concentration of women in clerical, services, and sales positions put their jobs at risk of being eliminated due to AI. Digital divide, limited digital infrastructure and lack of access to education exacerbates the issue further.