PWR 91NF: Writing and Representing Ourselves in the Time of GenAI

Lately, the developments promised from Generative AI have been compared to an industrial revolution, or more specifically, that moment in history where people witnessed the domestication of electricity in all households (Kaplan, 2024). GenAI promises to help us with some of the greatest challenges in healthcare, education, finance, customer care, and the list goes on. However, how can we ensure that we benefit from a more ethical & human-centered use of this technology that encompasses and benefits all humans? We need to engage in meaningful discussions and learn how to critically examine the far-reaching socio-cultural and linguistic impacts these GenAI tools have on daily interactions, as well as far-reaching global impacts.

Recently, Antonio Guterres, Secretary-General of the United Nations expressed concern that “AI can amplify bias, reinforce discrimination and enable new levels of authoritarian surveillance”, noting that the key concerns stem from AI’s unpredictability and potential for misuse. Meanwhile, and more locally here at Stanford, researchers tested ChatGPT and other popular Large Language Models (LLMs), and found that these models offer different financial advice based on “first and last names suggestive of race or gender,” whereby Black women received “the least advantageous outcome” (Haim, Salinas & Nyarko, 2024).

These examples are but a few of how Generative AI tools exhibit forms of racial, gender (or language-based) bias. In such instances, LLMs clearly fall short in offering a fair representation of the lived experiences of BIPOC, women, as well as other minoritized or socioeconomically disadvantaged groups. The ubiquity of these tools and the speed upon which they produce content by scraping an overwhelming US-centric internet, is now creating “homogenized American narratives” (Rettberg 2024). Indeed, there are ongoing attempts to rectify such results from LLMs by their parent companies (and there is much enthusiasm about the potential of Generative AI/LLMs to enhance our lives (in Medicine, Public Health, Education, Environmental Sciences, etc.). Yet, the popular discourse tends to focus on the “hype” of AI, with little attention to the lived-experiences of individuals who will bear the consequences of such biased AI-generated content.

This course offers you the opportunity to reflect more deeply on the challenges that Generative AI poses in terms of reinforcing stock-narratives or images about marginalized groups. In this class, we will work together via our course readings, guest-lecture visits, and course assignments to find practical ways to counter the normative narratives that feed these Generative AI tools. In this class you will engage with recently published texts from researchers such as Timnit Gebru, Safiya Noble, Emily Bender, Julian Nyarko (Stanford Institute for Human-Centered AI) as well as podcasts like the BBC’s Digital Human. Together we’ll examine how these authors effectively communicate their messages to a public audience, while considering how some of the solutions they propose can be applied to a specific problem you have identified in a Generative AI tool of your choice. A key component of this class will be rhetorically analyzing the results of the culture and identity-focused prompts that you feed into your chosen Generative AI tool. Some students can choose to build on their multilingual knowledge and examine the extent to which minoritized languages are vastly underrepresented when using LLM for knowledge finding, versus when using a “traditional” search engine.

Major Assignments

Critical Discourse Analysis of GenAI Tool Results (1800-2000 words)

You will use a GenAI tool* of your choice (either text-generating or image-generating, close-sourced or open-sourced) and analyze the type of narratives/discourse produced based on the different queries and word choices you experiment with (i.e. do they emphasize the rhetorical traditions, languages or practices of communities of color and other minoritized groups or erase certain narratives, and consider the implications of any power imbalance you observe). Your analysis will be informed by the readings we discuss in class as well as a foundational text in Critical Discourse Analysis (CDA). 

Public-Facing (flipped) Genre Project

Based on your earlier Critical Discourse Analysis (CDA) of a GenAI tool’s responses, you will propose and adopt a new genre to display your findings/analysis with the aim of exploring ideas on how to intervene and counter some of the dominant and normative cultural narrative results you (may) have come across earlier (eg. feeding the tool with more diverse images/examples, or proposing a different model). In this genre “flip”, you can choose to develop an infochart, a comic, tiktok video, Instagram Slide-Deck, X (formerly Twitter) thread, podcast transcript or write a Medium article. 

Presentation of Research Process and Recommendations (6-8 mins)

Based on the first two assignments, you will deliver a presentation (as part of a panel) that offers your peers an insider’s view of your research process throughout the quarter, specifically how you guided your original CDA (assignment 1) as well as a reflection on the rhetorical audience-focused choices you made for your public-facing project (assignment 2) given the community/communities you focused on. One question you can consider during this presentation is what are the overtly advertised “merits” of your selected GenAI platform, as well as the less-exposed underbelly of this platform. You are encouraged to reference any relevant course texts as well as guest speakers. You will be encouraged to engage with ideas presented by your peers during the panel, with the goal of having a conversation around potential alternatives/improvements to current Gen-AI models that are more community and social-justice focused. During this final showcase (over two days), you can invite peers to attend. 

*Students will have free access to a range of text and image-based GenAI tools.