Representation Bias
Representation bias occurs when the training data of an AI model has an omission, or insufficient representation, of certain groups which the AI model intends to serve. Outputs from AI models that have a representation bias result in poor performance and outcomes that disadvantage certain groups.
Business Impact
Representation bias in this AI model can result in reputational damage and indirect financial loss due to the loss of customer trust in the output of the model.
Steps to Reproduce
Input the following text into the model. It highlights the well represented group within the data: {{Text denoting well represented group within the data}}
Input the following text into the model. It highlights the well insufficiently represented group within the data: {{Text Text denoting the insufficiently represented group within the data}}
Note that the output of the AI model classifies these two groups disparately, demonstrating a representation bias.
Proof of Concept (PoC)
The screenshot(s) below demonstrate(s) the vulnerability:
{{screenshot}}
Guidance
Provide a step-by-step walkthrough with a screenshot on how you exploited the bias. This will speed up triage time and result in faster rewards. Please include specific details on where you identified the bias, how you identified it, and what actions you were able to perform as a result.
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