Papers for

diversity and inclusion teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Participatory speech dataset creation methods improve queer community inclusion

Towards participatory speech dataset curation: A queer case study and conceptual framework

Abstract: In this paper, we motivate the need for a participatory speech dataset creation framework through a case study of the LGBTQIA+, or queer, community - a community with documented concerns about AI and reported harms, including attempts to develop 'gaydar' technologies that purportedly identify individuals as queer. We review common speech data collection practices, why these methods may be unsuitable for engaging with queer speakers, and discuss previous efforts in participatory AI with queer community engagement, as well as participatory endeavours specific to speech data collection for other marginalized communities. From this review, we develop a conceptual framework for participatory speech data curation by, for, and with marginalized communities drawing on insights from co-design and knowledge sharing. We propose a framework comprising overlapping and two-way processes of defining a community, project formulation, modes of participation, and personal autonomy.

Mon 21 SeptArtificial IntelligenceComputers and Society
The gist
Collecting voice data often misses the needs and concerns of queer people, especially due to past harmful AI uses. The authors look at current speech data methods and why they may not work well for the LGBTQIA+ community. They review efforts to include queer voices in AI and speech data work. From this, they suggest a new way to create speech datasets that involves queer and marginalized communities as active participants who help shape the project and keep control over their data.
Open → 2609.25496v1

Text to image AI models show strong gender bias in jobs

Newer Is Not Fairer: Gender Stereotyping in Text-to-Image AI Across Model Generations

Abstract: Text-to-image generative models are widely used in professional and creative settings, yet how they represent gender across occupations -- and whether newer models are fairer -- remains poorly understood across multiple generations. We evaluate gender representation across 20 occupations, 5 prompt templates, and 4 Stable Diffusion model generations (SD 1.5, SD 2.1, SDXL, SD 3 Medium), generating 8,000 images with n = 100 per occupation-model cell (5 prompts x 20 images), and classifying all with DeepFace. Across the 8,000 open-source images, 76.4% show male subjects (95% CI [75.1%, 78.7%], p < 2.2 x 10^-16, Benjamini-Hochberg adjusted). More strikingly, 57.6% of images for historically female-coded occupations show male subjects (raw p = 3.43 x 10^-22, BH-adjusted p = 1.71 x 10^-21). All nine significant tests reported in this paper survive BH correction across 10 tests. When compared against U.S. Bureau of Labor Statistics workforce data, models underrepresent women by 20-46pp on average, with particularly large deviations for near gender-balanced occupations: scientist (48% female in BLS, 82-99% male in model outputs) and cleaner (46% female in BLS, 80-92% male in outputs). Model generations do not improve steadily: bias worsens from SD 1.5 to SDXL before partially recovering in SD 3 Medium. A preliminary comparison with GPT-image-1 on five occupations suggests lower bias than open-source models, though the practical effect is small (Cramer's V = 0.080) and the comparison is exploratory. No model achieves gender parity.

Wed 16 SeptComputer Vision and Pattern RecognitionArtificial IntelligenceComputers and Society
The gist
Text-to-image AI models often create images that wrongly show men more than women in many jobs, especially in roles typically done by women. The authors tested different versions of a popular AI model and found that newer versions did not get better at showing people fairly by gender. For some jobs, like scientists and cleaners, the images mostly showed men even when many women work in those roles in real life. This bias hasn’t gone away in these AI tools, which might affect how people see gender roles.
Open → 2609.18007v1