Digital FeminismPapers

This paper investigates the interplay between gender bias in AI systems and the potential of digital literacy to empower women in technology. Synthesizing research from 2010-2024, it examines how gender bias manifests in AI and the effectiveness of digital literacy initiatives.

Klein and D'Ignazio apply the principles of Data Feminism to AI, calling for AI practice grounded in power analysis, intersectionality, co-design, situated knowledge, and refusal of harm.

A groundbreaking 2018 FAccT study revealing severe bias in commercial facial recognition systems against darker-skinned women, with error rates up to 34.7% compared to just 0.8% for lighter-skinned males.

D'Ignazio and Klein bring feminist theory into data visualization, proposing ways to redesign data expression around power, affect, embodiment, positionality, and uncertainty.

Noble argues for Black feminist technology studies, showing how search, platforms, and digital infrastructures reproduce racialized and gendered power rather than operating as neutral tools.

This influential essay reimagines feminist theory through the metaphor of the cyborg - a hybrid of machine and organism. Haraway critiques traditional boundaries between human and non-human, nature and culture, and proposes a post-gender, post-essentialist feminist politics rooted in affinity, irony, and technological embodiment.