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Lab on confusion matrices and COMPAS

Lab on confusion matrices and COMPAS


1. Use Confusion Matrices to Understand the Controversy:

  • Collect relevant data: Gather data related to the criminal justice system, including information on arrests, convictions, sentencing, and demographic details of individuals involved.
  • Define the problem: Clearly specify the controversy or issue related to racial equality in the criminal justice system that you want to address using confusion matrices.
  • Preprocess the data: Clean and prepare the data for analysis, including handling missing values, encoding categorical variables, and splitting the dataset into training and testing sets.
  • Train a predictive model: Choose an appropriate classification algorithm (e.g., logistic regression) to predict relevant outcomes (e.g., convictions).
  • Generate confusion matrices: Use the testing dataset to create confusion matrices, which will help you understand how the model’s predictions align with actual outcomes for different racial groups.
  • Analyze the confusion matrices: Examine metrics like accuracy, precision, recall, and F1-score for each racial group to assess the model’s performance and potential biases.

2. Develop and Validate a Model Similar to COMPAS:

  • Research the COMPAS model: Study the COMPAS model and its controversies to understand its components and biases.
  • Design a logistic regression model: Develop a logistic regression model that simulates the COMPAS model’s functionality, taking into account the features and criteria used in the real-world system.
  • Train and validate the model: Use the collected data to train and validate your model, following best practices like cross-validation to ensure its performance and fairness.

3. Typical Machine Learning Workflow:

  • Implement cross-validation: Split your dataset into multiple folds and perform cross-validation to select the best model, hyperparameters, and assess generalization performance.
  • Model selection: Compare different models and evaluate their performance using appropriate evaluation metrics.
  • Export results: Save the model evaluation results, including confusion matrices, metrics, and model parameters, to CSV files for documentation and analysis.

4. Consider Fairness in Policymaking:

  • Assess fairness: Evaluate the fairness of your model by examining disparities in predictions and outcomes across racial groups.
  • Explore fairness techniques: Research and apply fairness-aware machine learning techniques such as reweighing, adversarial debiasing, or disparate impact analysis to mitigate biases in your model.
  • Reflect on policymaking: Consider the ethical implications of your findings and how they can inform policymaking decisions related to the criminal justice system.

Export to CSV and HTML:

  • Export confusion matrices and model evaluation results to CSV files for data preservation.
  • Create an HTML report summarizing your analysis, including visualizations, key findings, and recommendations. You can use libraries like Jupyter Notebook or Markdown to create the HTML report.

Remember to maintain transparency in your analysis and model development, documenting all steps and decisions made throughout the process. Additionally, engage in discussions around fairness and ethical considerations in the context of your findings and their potential impact on policymaking.

Lab on confusion matrices and COMPAS





export to both csv and html

1. Use confusion matrices to understand a recent controversy around racial equality and criminal

justice system.

2. Use your logistic regression skills to develop and validate a model, analogous to the proprietary

COMPAS model that caused the above-mentioned controversy.

3. Give you some hands-on experience with typical machine learning workflow, in particular

model selection with cross-validation.

4. Encourage you to think over the concept of fairness, and the role of statistical tools in the

policymaking process.

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