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ProtoSteer: Steering Deep Sequence Model with Prototypes.

Yao Ming, Panpan Xu, Furui Cheng

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    |September 13, 2019
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    Summary
    This summary is machine-generated.

    Domain experts can now directly steer deep sequence models using ProtoSteer (Prototype Steering). This approach uses case-based reasoning to create interpretable models with concise prototypes, enhancing accuracy and accessibility.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • Deep sequence models (e.g., LSTMs) are widely adopted but their black-box nature limits expert accessibility and knowledge incorporation.
    • Incorporating domain-specific knowledge into complex models is challenging without intermediaries.
    • Existing methods lack direct involvement of domain experts in model steering.

    Purpose of the Study:

    • To enable direct steering of deep sequence models by domain experts.
    • To enhance model interpretability and knowledge integration through expert interaction.
    • To reduce reliance on model developers for model refinement.

    Main Methods:

    • Developed ProtoSteer (Prototype Steering), a system leveraging case-based reasoning.
    • Utilized ProSeNet (Prototype Sequence Network) to learn exemplar cases (prototypes) from data.
    • Enabled interactive inspection, critique, and revision of prototypes by domain experts.
    • Incorporated user-specified prototypes to incrementally update the model.

    Main Results:

    • ProtoSteer facilitates direct expert involvement in steering deep sequence models.
    • Prototypes serve as both data summaries and explanations for model decisions.
    • Domain expert involvement led to more interpretable models with concise prototypes.
    • Model accuracy was retained while enhancing interpretability.

    Conclusions:

    • ProtoSteer successfully integrates domain experts into the deep sequence model development process.
    • The case-based reasoning approach enhances model transparency and expert trust.
    • This method offers a viable solution for creating more accessible and understandable AI models in various domains.