Algorithmic Biases
Algorithmic bias occurs in an AI model when the algorithms used to develop the model produce biased outcomes as a result of inherent flaws or limitations in their design. This bias originates from assumptions made during algorithm development, selection of inappropriate models, or the way data is processed and weighted. This results in AI models that make unfair, skewed, or discriminatory decisions.
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
Select an AI algorithm known to have potential biases
Train the algorithm on a dataset that may amplify these biases
Test the algorithm's decisions or predictions on a diverse dataset
Identify and document instances where the algorithm's output is biased
Proof of Concept (PoC)
The screenshot(s) below demonstrate(s) the vulnerability:
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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.
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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