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skills/ai-ethics-validator/assets/example_model.pkl

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+# example_model.pkl
+
+# This is a placeholder file for a pickled machine learning model.
+# In a real-world scenario, this file would contain the serialized representation
+# of a trained machine learning model using the `pickle` library.
+
+# This model is used by the ai-ethics-validator plugin to demonstrate
+# how to load and use a model for fairness validation.
+
+# INSTRUCTIONS:
+# 1. Replace this placeholder with your actual trained model.
+# 2. Ensure the model is compatible with the `validate-ethics` command
+#    in the plugin.  The command expects the model to have a `predict` method
+#    that takes input data and returns predictions.
+# 3. Update the `validate_ethics` function in the plugin's main script
+#    to correctly load and use your model.
+# 4. Consider using a model that can be easily validated for bias, such as
+#    a logistic regression or decision tree.
+
+# Example of how to create a dummy model (FOR TESTING ONLY):
+# import pickle
+# from sklearn.linear_model import LogisticRegression
+# from sklearn.datasets import make_classification
+#
+# # Generate a synthetic dataset
+# X, y = make_classification(n_samples=100, n_features=2, random_state=42)
+#
+# # Train a logistic regression model
+# model = LogisticRegression(random_state=42)
+# model.fit(X, y)
+#
+# # Save the model to a file
+# with open("example_model.pkl", "wb") as f:
+#     pickle.dump(model, f)
+
+# Placeholder content to prevent errors if the file is not replaced.
+# In a real application, this would be replaced with the pickled model.
+# Replace this with the actual pickled model data.
+class PlaceholderModel:
+    def predict(self, data):
+        # Placeholder prediction logic
+        return [0] * len(data)
+
+import pickle
+model = PlaceholderModel()
+with open("example_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+
+
+# END OF FILE