Text-to-image (TTI) models are increasingly used in professional, educational, and creative contexts, yet their outputs often embed and amplify social biases. This paper investigates gender representation in six state-of-the-art open-weight models: HunyuanImage 2.1, HiDream-I1-dev, Qwen-Image, FLUX.1-dev, Stable-Diffusion 3.5 Large, and Stable-Diffusion-XL. Using carefully designed prompts, we generated 100 images for each combination of five hospital-related professions (cardiologist, hospital director, nurse, paramedic, surgeon) and five portrait qualifiers ("", corporate, neutral, aesthetic, beautiful). Our analysis reveals systematic occupational stereotypes: all models produced nurses exclusively as women and surgeons predominantly as men. However, differences emerge across models: Qwen-Image and SDXL enforce rigid male dominance, HiDream-I1-dev shows mixed outcomes, and FLUX.1-dev skews female in most roles. HunyuanImage 2.1 and Stable-Diffusion 3.5 Large also reproduce gender stereotypes but with varying degrees of sensitivity to prompt formulation. Portrait qualifiers further modulate gender balance, with terms like corporate reinforcing male depictions and beautiful favoring female ones. Sensitivity varies widely: Qwen-Image remains nearly unaffected, while FLUX.1-dev, SDXL, and SD3.5 show strong prompt dependence. These findings demonstrate that gender bias in TTI models is both systematic and model-specific. Beyond documenting disparities, we argue that prompt wording plays a critical role in shaping demographic outcomes. The results underscore the need for bias-aware design, balanced defaults, and user guidance to prevent the reinforcement of occupational stereotypes in generative AI.

Extended Abstract In this research project, the focus is on addressing the critical issue of stereotype bias in image generation models, particularly gender bias, which poses significant ethical implications. Leveraging the potential of Reinforcement Learning from Artificial Intelligence Feedback (RLAIF), a novel pipeline using Denoising Diffusion Policy Optimization (DDPO) is proposed to fine-tune image generation models and mitigate gender bias. The study utilizes a pretrained stable diffusion model and a gender classification Transformer model to evaluate bias in generated images. The gender classification model achieved high accuracy, reaching 100% in specific tests. Experiments showcase the pipeline's ability to reach stable gender balance, indicating the potential of RLAIF for bias reduction in image generation models.

Speaker Diversity at DPS 2022 Continuing our efforts from last year, the DPS Team will put a considerable amount of effort towards speaker diversity. We will strive hard to achieve the following: Our panels & pool of speakers will represent a gender balance and color diversity % increase in speakers of colors % increase in women speakers Improve gender balance Encouraging & welcoming new speakers assigning mentors additonal support Approach speakers from diverse backgrounds and work towards racial equality Encourage speakers whose first language is not English (DPS 2022 is multi-lingual) Compensate our speakers fairly and equitably Overall, the DPS team is here to support speakers who are women, support BIPOC speakers, engage Asian speakers, expand our reach to the LGTBQIA community, put in extra efforts to reach out to speakers with disabilities (both invisible and visible disabilities). Lastly, encourage speakers whose first language is not English. Yes, DPS 2022 is multi-lingual.

The old stereotype of video game players as spotty, socially isolated boys in basements is finally disappearing after decades, but the popular image of game developers is enduring. They are imagined to be white and beardy, with glasses and a probable fondness for sci-fi and fantasy, and this is hardly unjustified. Cast an eye over the development floor of pretty much any major game developer in the western world and there's an undeniable homogeneity. The same can be said about video games industry executives. Whether clean-shaven or bearded, besuited or smart-casual, creative or corporate, they are almost universally white and male. In 15 years on the games beat, I have interviewed more men called Phil in senior games industry positions than women and people of colour combined.

I am writing this article while my baby daughter sleeps. Like all new parents, her dad and I have spent the last few months in a joy-filled, sleepy haze of getting to know her and imagining what her future might look like. This brings a new intensity, and a little more trepidation, to my role advising on the future of work. What will work look like for this generation of young women, especially as more and more of our roles are being automated -- or even replaced -- by artificial intelligence (AI)? And how can leaders ensure that AI does not lead to gender bias in their organizations? Recent research is beginning to answer these questions, and the outlook is mixed: on the one hand, women may be spared from the job disruptions men will face in the longer-term.