<ul data-eligibleForWebStory="false"><li>A recent paper delves into the importance of trustworthy explanations in machine learning models, especially in high-risk domains.</li><li>Interpretable models like rule-based models, such as decision trees, are commonly used in high-risk applications despite their inherent shortcomings.</li><li>The paper examines the negative aspects of rule-based models like negative overlap and redundancy and proposes algorithms to analyze and address these issues.</li><li>It concludes that existing tools for learning rule-based ML models often lead to rule sets that exhibit these undesirable characteristics.</li></ul>

On Trustworthy Rule-Based Models and Explanations

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