Auditing Model Bias with Balanced Datasets with Mimesis
The article discusses how to audit machine learning models for bias using balanced datasets. It introduces Mimesis, an open-source library that generates counterfactual datasets to test for discrimination in model outcomes. A step-by-step guide is provided for creating a biased dataset and using Mimesis to evaluate model fairness based on gender.
- ▪Machine learning models can adopt biases from historical training data.
- ▪Mimesis helps generate balanced datasets to audit model bias without compromising real data.
- ▪The article includes a practical example of creating a biased loan approval dataset and testing it for gender discrimination.
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# Introduction Whether they are well-established classifiers or state-of-the-art massive models like large language models (LLMs), building machine learning solutions often entails a risk: algorithms might silently adopt prejudices inherent in the historical training dataset they were trained on. But in a high-stakes scenario or one where data is sensitive, how can we audit whether a model is biased without compromising real-world information? This hands-on article guides you in training a simple classification model for "loan approval" on biased data. Based on this, we will use Mimesis, an open-source library that can help generate a perfectly balanced, counterfactual dataset.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at KDnuggets.
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