Skip to main content
Social Sci LibreTexts

2: Teaching AI Ethics- Bias and Discrimination

  • Page ID
    207219
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    undefined

    This is the first post in a series exploring the nine areas of AI ethics outlined in this original post. Each post will go into detail on the ethical concern as well as providing practical ways to discuss these issues in a variety of subject areas.

    UPDATE: Here’s a pre-post-script to this post which raises an important point about bias in image generation. It comes from a DM conversation and subsequent comment on the post on LinkedIn:

    Excellent comment via Lori Mazor on the image with this post – I’m bringing it out of our DM conversation because it’s an important point, especially in the context of this topic.

    Lori highlighted the ‘white male’ bias of the image. I’d noticed the “whiteness” of the image, but not critically thought about the “maleness”. What’s interesting is that the prompt doesn’t contain any reference to people at all, male or female:

    /imagine prompt: digital collage, glitch art, post-structuralist, wealth, pained, hegemony, power, tech post feature image, header image –ar 2:1 –q 2 –v 4

    Lori’s message has me wondering which words in the prompt have conjured the white male face. I’m going to guess that unfortunately it’s the combination of “wealth” and “power”.

    My intent by using only abstract concepts in the prompt was to generate something random and broadly in-keeping with the theme (you’ll notice that the following posts in this series have a similar aesthetic). Some images contain female figures – I’m going to go back and explore which words have most likely drawn this out of the image gen.

    On with the article!

    As term one rapidly unfolds, the Artificial Intelligence boom kick-started in late 2022 by ChatGPT shows no sign of letting m qup. Since the start of term, we have seen the release of Microsoft’s new Bing Chat, and OpenAI has updated its terms and conditions to permit use by anyone over 13.

    While AI can undoubtedly be a valuable tool in education, it’s important for educators to understand the ethical concerns that surround its use. We must ensure that we are using these technologies in ways that are responsible, just, and fair. The original Teaching AI Ethics post has proved hugely popular, but many educators from primary to tertiary have asked for more details on each of the nine areas. In this post, I’ll explore the first and most widely-known issue: bias and discrimination.


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

    • Was this article helpful?