Aggregation Bias
Aggregation bias occurs in an AI model when systematic favoritism is displayed when processing data from different demographic groups. This bias originates from training data that is skewed, or that has an under representation of certain groups. Outputs from AI models that have an aggregation bias can result in unequal treatment of users based on demographic characteristics, which can lead to unfair and discriminatory outcomes.
Business Impact
Aggregation 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
Obtain a diverse dataset containing demographic information
Feed the dataset into the AI model
Record the model's predictions and decisions
Compare outcomes across different demographic groups
Observe the systematic favoritism displayed by the model toward one or more specific groups
Proof of Concept (PoC)
The screenshot(s) below demonstrate(s) the vulnerability:
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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.
Recommendation(s)
Establish practices and policies that ensure responsible data collection and training. This can include:
Conducting a comprehensive review of the training data to find and remediate biases. This includes re-sampling underrepresented groups and adjusting the model parameters to promote fairness.
Business processes that index ethical frameworks, best practices, and concerns should be developed, monitored, and evaluated.
Clearly define the desired outcomes of the AI model, then frame the key variables to capture.
Ensuring that the data collected and used to train the AI model illustrates the environment that it will be deployed in and contains diverse and representative data.
Design and develop algorithms that are sensitive to fairness considerations, and audit these regularly.
Practice data collection principles that do not disadvantage specific groups.
Document the development of the AI model, including all datasets, variables identified, and decisions made throughout the development cycle.
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