Gender Bias in AI Generated Images

• Recently, there has been an increase in awareness of the biases and harms large language models can perpetuate.
• One such model is DALL-E 2 by OpenAI which is a generative model that creates images based on a user’s text inputs.
• Representative harms can occur in AI models when not all groups are fairly treated and represented.
• Does DALL-E 2 present some form of gender-bias when generating images for occupations?

Since the invention of the multi-layer perceptron network, there has never been a time when AI has been more prevalent or visible in our society than the present day. Machine learning is used for almost everything; healthcare, business analytics, correctional systems, scientific exploration, and even in seemingly mild and light hearted applications that have achieved widespread commercial and critical success. One of these applications is the image generating model known colloquially as ‘DALL-E 2’, a software that receives prompts in the form of natural language and outputs its own perception of what the user wants to see. At first glance this seems completely fangless and friendly. However, as other investigations have demonstrated, this is clearly and concisely not the case.

In this project, we will investigate just how far the team at OpenAI has come in terms of bias mitigation by examining the outputs of several different prompts that have been passed through their image generation software.

• Step 1:

  Occupations were chosen based on their proximity to having demographic parity in the workplace. For workplace demographic data, the US Bureau of Labor Statistics (BLS) and specifically their ‘Labor Force Statistics from the Current Population Survey’. was used. Once this source was found, and prompts could be generated, the actual investigation could begin.
  
  
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  Step 2:

  In order to calculate sample sizes, a one-sample dichotomous outcome formula was used. A sample size of 210 for each occupation was chosen in order to have a statistical power of 99%. Due to technical and budget limitations, only ten occupations were chosen.
  
  Five of those occupations were selected as female-dominated while the remaining five were selected as male-dominated as according to the BLS data from step 1.
  
  The table below shows the ten occupations selected as prompts for DALL-E 2 along with the number of BLS survey respondents and the percentage of non-male respondents
  
  

  Occupation	N	% Non-Male
  Financial/Investment Analysts	387	40.2
  Janitors/Building Cleaners	2183	40.2
  Lawyers	1141	38.5
  Cooks	2012	38.4
  Dentists	140	36.6
  Bartenders	457	59
  Biological Scientists	110	57.9
  Secondary School Teachers	1000	58.7
  Pharmacists	375	59.6
  Trainers/Fitness Instructors	234	62.9

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  Step 3:

  For each occupation selected, 210 images where generated and stored with a filename associated with its occupation. The images were then manually labeled by all group members and given a final label based on the majority consensus.
  
  Below are sample images for images generated with the labels: cook, financial analyst, secondary school teacher, and bartender.
  
  
  
  
  
  
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  Step 4:

  For each occupation a hypothesis test was done as follows:
  • H0: The images generated from DALL-E 2 for this occupation follows demographic parity
  • H1: The images generated from DALL-E 2 for this occupation do not follows demographic parity
  The test statistic used was the Absolute Difference in Proportions of Male Images
  
  
  View Sample Distributions

• All generated result sets were tested against an ideal 50-50 distribution with an alpha of 0.05
• In only one case did a result set have a majority female response (Secondary School Teacher)

Through our work, our group has proven the existence of significant occupational gender bias in OpenAI’s DALL-E 2 model. Several prompts did not even generate a majority female-presenting response, even when the data obtained from the BLS indicated that the profession was female dominated. While the impacts of such extreme bias may not be immediately apparent, as the model begins to see more widespread use across various applications, the impacts will likely grow more severe and visible. As such, OpenAI should move to address the bias present in their algorithms as soon as possible, before they see widespread commercial use.