It's Academic

This past January, I was fortunate to participate in the “Exploring AI with Critical Information Literacy” course through the Association of College and Research Libraries (ACRL) e-Learning online website. This course allowed participants the opportunity to interact with artificial intelligence (AI) and think through several information literacy concepts and explore power structures within AI itself. This course sparked my interest in digital feminism and AI, and the blog post below shares my initial explorations.

Over the past few years, AI has become a major battleground for debates about ethics, work, and representation. Feminist scholars interrogate the intersections of gender and technology, and algorithmic systems invite renewed scrutiny through a digital feminist lens. Digital feminism blends feminist ideas with online activism to expose power imbalances in digital systems while envisioning more equitable ways to design and govern technology

Feminist analyses of AI build on foundational critiques of bias in sociotechnical systems. As Safiya Umoja Noble (Algorithms of Oppression) and Ruha Benjamin (Race After Technology) demonstrate, machine learning models reproduce the structural inequalities of the datasets from which they are derived. Gendered, racialized, and class-based hierarchies are not incidental to algorithmic outputs; they are constitutive of the systems’ logics and training processes. The resulting forms of “encoded bias” perpetuate exclusion in contexts ranging from automated hiring and predictive policing to image recognition and language generation.

Rather than treating algorithmic unfairness as a technical problem, digital feminism reframes it as an epistemological and political one. This perspective asks not only how to mitigate bias but whose values and knowledge shape the definition of fairness. Such analyses align with Haraway’s (1988) feminist epistemology and the broader challenge to objectivity, emphasizing situated knowledge and contextual accountability in technological design.

Digital feminism also advances material and creative interventions in AI development. Initiatives like Caroline Sinders’ Feminist Data Set show a move toward participatory and care-centered design. These projects foreground the ethics of inclusion, transparency, and consent in dataset creation, seeking to operationalize feminist principles through applied methods. Similar efforts within global and community-driven AI projects adapt feminist theory to local contexts, challenging Western-centric and corporate models of technological innovation. Such interventions reveal a dual orientation within digital feminism: resisting algorithmic domination while cultivating emergent practices of feminist technoscience. The tradition of feminist hacking, speculative design, and art-based critique plays a crucial role here, converting abstract ethical commitments into tangible, experiential forms of inquiry.

As AI systems reshape the conditions of knowledge production and public discourse, feminist pedagogy offers tools for critical engagement. Developing AI literacy, understood as the ability to interrogate, interpret, and co-create algorithmic systems, requires approaches grounded in collaboration and reflexivity. Within libraries, classrooms, and digital learning environments, feminist educators are fostering practices of co-learning that resist techno-solutionism and promote collective agency. Ultimately, digital feminism repositions the question of artificial intelligence from “how can we make machines more objective?” to “how can we embed justice, plurality, and care into technological futures?” In doing so, it reframes AI not as an autonomous force but as a sociocultural artifact shaped by human choices.

Exploring AI with Critical Information Literacy” with Sarah Morris has an upcoming session in May 2026. The registration link is forthcoming in case you are interested in bookmarking it.

I put together a reading list below to get you started on this topic. If you want to explore more on this topic and are interested in collaborating, please reach out to me (Denise A. Wetzel) directly at dawetzel[at]psu[dot]edu. 

1. Akinwale, O., Ogunyemi, A., & Oluwatobi, S. (2025). Gender biases within artificial intelligence and ChatGPT: Evidence, sources of biases and solutions. Computers in Human Behavior: Artificial Humans, 3(2), Article 100129. https://doi.org/10.1016/j.chbah.2025.100129
2. Atenas, J., Beetham, H., Bell, F., Walji, S., & Swartz, S. (2022). Feminisms, technologies and learning: Continuities and contestations. Learning, Media and Technology, 47(1), 1–10. https://doi.org/10.1080/17439884.2022.2041830
3. Bangura, S. (2025, June 3). What does feminist AI look like? And how do we design just processes when using the experiences of survivors to build it? Medium. https://medium.com/wethecatalysts/what-does-feminist-ai-look-like-and-how-do-we-design-just-processes-when-using-the-experiences-of-b62b71bb3c6e
4. Schelenz L. (2025). Black feminism and Artificial Intelligence: the possibilities and limitations of contesting discriminatory AI from a critical social theory perspective. Frontiers in sociology, 10, 1602947. https://doi.org/10.3389/fsoc.2025.1602947 
5. Toupin, S. (2024). Shaping feminist artificial intelligence. New Media & Society, 26(1), 580-595. https://doi.org/10.1177/14614448221150776
6. Vachhani, Sheena J. 2024. “Networked Feminism in a Digital Age—Mobilizing Vulnerability and Reconfiguring Feminist Politics in Digital Activism.” Gender, Work & Organization 31(3): 1031–1048. https://doi.org/10.1111/gwao.13097. 

Dear Colleagues, 

I am conducting a research study about librarians’ individual values and decisions to use or refuse to use generative artificial intelligence. The aim is to help explain differences in librarian attitudes and intentions regarding generative artificial intelligence by examining how these may relate to individual values.

You are invited to participate if you currently work in an academic library and: 

• hold an MLIS/MLS degree 
• or your primary job responsibilities include providing reference, instruction, cataloging, collection development, or other information services.  

Participation consists of completing an anonymous online survey that takes approximately 10-15 minutes. The survey includes questions about your personal values, your views on generative AI, and basic background information. Your responses will be completely anonymous. No identifying information will be collected, and results will be reported only in aggregate. 

Survey Link: https://millersville.qualtrics.com/jfe/form/SV_1RJQCCtsDLHb79k 

Your participation is entirely voluntary. You may skip any question or stop the survey at any time. There are no penalties for choosing not to participate. 

Generative artificial intelligence is influencing many aspects of our professional responsibilities. Your participation will help create a deeper understanding of how our individual values inform our decisions about acceptance or refusal of generative artificial intelligence in our profession. 

If you have any questions about the study, please feel free to contact: 
Greg Szczyrbak, MSLIS 
Millersville University 
Email: greg.szczyrbak@millersville.edu