Technology CritiquePapers

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.

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.

Cohn's participant-observation study shows how apparently neutral technical language turns nuclear destruction into a masculinized, abstract, and emotionally insulated form of rational practice.