Processing Bias
Processing bias occurs when AI algorithms make biased decisions, or predictions, due to the way that they process data. This can be a result of the algorithm's design or the training data it has been trained on. Outputs from AI models that have a processing bias can result in discrimination, reinforcement of stereotypes, and unintended consequences such as amplification or polarization of viewpoints that disadvantage certain groups.
Business Impact
Processing bias in this AI model can result in reputational damage and indirect monetary loss due to the loss of customer trust in the output of the model.
Steps to Reproduce
Input the following benchmark dataset into the AI model: {{Benchmark data set}}
Split the dataset into two sets. One is to act as the training dataset and the other as the testing dataset.
Examine the model's predictions and note the following disparity exists: {{Disparity between Group A and Group B}}
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.
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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