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New Books in Science, Technology, and Society
What Decision Means
Jan 19, 2024
Bill Cope, Mary Kalantzis, and Gang Wang discuss the meaning and implications of decision-making in the context of AI. They explore topics such as explaining machine learning for security tasks, adding logic and reasoning to ML models, connecting domain knowledge and machine learning, and the differences between machine learning and human decision making. They also emphasize the importance of understanding AI and its impact on decision making.
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Quick takeaways
- Tailoring machine learning explanations to specific security use cases is crucial for effective understanding and action during security events.
- Providing meaningful explanations for machine learning models is challenging due to their black box nature, but efforts should be made to align with user needs and workflows.
Deep dives
Machine learning explanations need to be tailored to specific use cases and requirements
A study discussed in the podcast highlights the importance of tailoring machine learning explanations to specific security use cases and requirements. Participants in the study expressed the need for explanations that not only help them understand the classification model but also provide context for understanding the detected security events. Existing research has primarily focused on developing explanations for understanding the model itself, but insufficiently explored providing explanations to support informed actions during security events. The study recommends proactive engagement with target users during the design of explanation methods for security tasks, considering users' downstream tasks and evaluating whether explanations can save time.
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