Systemic Bias
Systemic bias occurs when AI models consistently favor certain groups over others due to the way that they process data, or other structural or historical factors. This can be a result of the AI model's design or the training data it has been trained on. Outputs from AI models that have a systemic bias can result in discrimination, reinforcement of stereotypes, or viewpoints that disadvantage certain groups.
Business Impact
Systemic bias in this AI model can result in a lack of fairness and objectivity which can lead to reputational damage and a loss of customer trust in the output of the model. Additionally, business decisions that rely on this AI model are also affected.
Steps to Reproduce
Provide the AI model with diverse data that contains structural and historical bias.
Observe the model's consistent favoritism of certain groups over others during decision-making.
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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