Skip to main content
Social Sci LibreTexts

9: Teaching AI Ethics- Human Labour

  • Page ID
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

    ( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\id}{\mathrm{id}}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\kernel}{\mathrm{null}\,}\)

    \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\)

    \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\)

    \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    \( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

    \( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

    \( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vectorC}[1]{\textbf{#1}} \)

    \( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

    \( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

    \( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

    \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)


    When people think of Artificial Intelligence, the image that often springs to mind is that of sentient machines or shiny metallic robots, a depiction heavily influenced by popular culture. This narrative, along with language around “magical” or “mythical” AI, tends to overshadow actual pressing ethical issues associated with AI development and usage. This post will explore the exploitation of human labour in AI development, including low paid workers used for categorising and labelling data, and the impact of the AI infrastructure on human workers.

    In the ongoing arms race towards creating autonomous AI systems, multinational technology corporations are relying on a lot of ‘ghost work.’ This term, coined by anthropologist Mary L. Gray and computational social scientist Siddharth Suri, refers to labour carried out by a “global underclass” of precarious workers. Occupying roles such as content moderators, data labellers, and delivery drivers, these workers often come from economically disadvantaged backgrounds and perform critical tasks for the tech industry at low wages and under suboptimal working conditions.

    The way AI functions currently leans heavily on methodologies like statistical machine learning and deep learning through artificial neural networks. Such methods necessitate vast quantities of data. To obtain this data economically, platforms like Amazon’s Mechanical Turk have emerged, enabling ‘crowd work’ which involves breaking down large tasks into smaller units that can be handled by numerous workers.

    The emergence of such platforms and data-labelling companies, however, has resulted in workers being treated like parts in a machine, rather than individuals with rights and needs. These workers are often subjected to constant surveillance and repetitive tasks and face punitive measures for any deviation from assigned tasks. The mental and physical toll can be considerable, especially for content moderators who are continuously exposed to traumatic content without adequate support systems in place.

    This situation shines a light on a key issue in AI ethics: the exploitation of labour in the AI industry. It’s a stark reminder that the journey towards creating autonomous AI systems is not as ‘autonomous’ as it appears. It’s built on the labour of often exploited workers who, ironically, contribute to the development of AI systems that might eventually replace them.

    Transnational worker organising efforts, research collaborations with workers, and public accessibility of research findings are some avenues that have been explored to address these challenges. An essential aspect of this conversation is the role of solidarity between high-income tech workers and their lower-income counterparts. There’s potential here for those with more influence within corporations to advocate for their colleagues who have less.

    9: Teaching AI Ethics- Human Labour is shared under a CC BY-NC 4.0 license and was authored, remixed, and/or curated by LibreTexts.

    • Was this article helpful?