Why Is It So Hard for AI to Imagine a Female Athlete?

🌈 Abstract

The article discusses the issue of bias in AI-generated images, particularly in the context of portraying Olympic athletes. It highlights the underrepresentation of female athletes and the unrealistic depiction of their body shapes compared to male athletes in the AI-generated images.

🙋 Q&A

[01] Bias in AI-generated Olympic scenes

1. What are the key findings regarding the representation of female athletes in AI-generated Olympic scenes?

• Only 10 out of 80 AI-generated images (12.5%) showed female athletes, compared to the real-world quota of 50% female athletes in the Olympics.
• The sports in which women were represented were limited to climbing, gymnastics, tennis, volleyball, and surfing. The AI could not imagine female athletes in other disciplines like javelin throwing, shot put, or judo.
• When women were depicted, their body shapes were often portrayed in an unrealistic and exaggerated manner, such as the volleyball player with an "hourglass figure that would make Kim Kardashian jealous."

2. What are some potential reasons for the bias in AI-generated images?

• The training dataset used to develop the AI models is biased, as it reflects the existing gender inequality in society.
• The decisions made during the development, training, and tuning of the AI models can also reinforce biases.
• The user feedback and ratings used to improve the models may be skewed towards a homogeneous user base, further reinforcing the biases.

3. What are some potential strategies to address the bias in AI-generated images?

• Processing user prompts before passing them to the model to mitigate biases, though this approach can also backfire.
• Investigating the data and training procedures to identify and address the root causes of bias.
• Incorporating diverse user feedback and ratings to help the model learn less biased representations.
• Penalizing biased images and nudging the algorithm towards less biased outputs during the training process.

[02] Bias in AI-generated images of female software engineers

1. What is the author's observation regarding how AI portrays female software engineers? The author notes a similar trend of underrepresentation of female software engineers in AI-generated images, despite their presence in the real world.

2. What is the author's view on the efforts made by AI companies to address the issue of bias? The author states that despite the awareness of racial and gender bias in AI, companies have not yet come up with a significant effort to address this issue. The author suggests that more could be done to monitor and mitigate bias during the model creation and training process, as well as by incorporating diverse user feedback.

3. What is the author's overall assessment of the priority given by AI companies to addressing bias? The author expresses disappointment that mitigating bias does not seem to be a high priority for AI companies, who are more focused on developing algorithms to produce hyper-realistic images. The author believes that this is problematic, as biased images can reinforce harmful stereotypes.