Implicit Bias
Implicit bias occurs when there are biases present within the training data of an AI model that affects its decision-making. These implicit biases are usually introduced into the AI model via the developers who affect the design, implementation, and deployment of the AI system.
Business Impact
Implicit bias in this AI model can result in unintended discrimination and unfairness which can lead to reputational damage and a loss of customer trust in the output of the model.
Steps to Reproduce
Provide the AI model with data containing subtle, implicit biases.
Observe the model's decisions and identify instances where it unintentionally favors certain groups or viewpoints.
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.
Last updated