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37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
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# A Simple script to illustrate an example of a basic AI Risk Matrix
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import matplotlib.pyplot as plt
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import numpy as np
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# Define the risks and their impact and likelihood
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risks = {
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"Data Privacy Risk": {"Impact": "Medium", "Likelihood": "Medium"},
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"Diagnostic Accuracy Risk": {"Impact": "Very High", "Likelihood": "Low"},
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"Bias Risk": {"Impact": "High", "Likelihood": "Medium"}
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}
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# Mapping of impact and likelihood to numerical values
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impact_mapping = {"Low": 1, "Medium": 2, "High": 3, "Very High": 4}
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likelihood_mapping = {"Low": 1, "Medium": 2, "High": 3, "Very High": 4}
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# Prepare data for plotting
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x = [likelihood_mapping[risks[risk]['Likelihood']] for risk in risks]
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y = [impact_mapping[risks[risk]['Impact']] for risk in risks]
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labels = list(risks.keys())
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# Create the plot
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plt.figure(figsize=(8, 6))
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plt.scatter(x, y, color='blue')
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plt.title('AI System Risk Matrix')
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plt.xlabel('Likelihood')
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plt.ylabel('Impact')
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plt.xticks([1, 2, 3, 4], ['Low', 'Medium', 'High', 'Very High'])
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plt.yticks([1, 2, 3, 4], ['Low', 'Medium', 'High', 'Very High'])
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plt.grid(True)
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# Annotate the points
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for i, label in enumerate(labels):
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plt.annotate(label, (x[i], y[i]))
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plt.show()
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