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Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus:...
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Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
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Energy-Based Model for Accurate Estimation of Shapley Values in Feature Attribution.

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    EmSHAP, an energy-based model, accurately estimates Shapley values for explainable AI. This method effectively captures complex feature dependencies, improving model interpretability in challenging data environments.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Data Science

    Background:

    • Shapley value is crucial for explainable AI (XAI), attributing feature contributions to model outputs.
    • Estimating Shapley values is challenging due to complex conditional dependencies in data.

    Purpose of the Study:

    • Propose EmSHAP (Energy-based model for Shapley value estimation) for accurate Shapley value estimation.
    • Address challenges in estimating Shapley values within complex data environments.

    Main Methods:

    • Utilize energy-based models (EBMs) to model complex distributions and estimate conditional probabilities.
    • Employ a Gated Recurrent Unit (GRU) network for partition function estimation, capturing long-term dependencies.
    • Incorporate a dynamic masking mechanism to enhance robustness and accuracy.

    Main Results:

    • EmSHAP demonstrates higher accuracy in Shapley value estimation compared to existing methods.
    • The proposed method exhibits better scalability for complex datasets.
    • Theoretical analysis confirmed error bounds and practical case studies validated performance.

    Conclusions:

    • EmSHAP offers an effective solution for accurate Shapley value estimation in XAI.
    • The integration of EBMs and GRUs enhances interpretability for complex machine learning models.
    • EmSHAP provides a scalable and robust approach for feature attribution.