Machine learning (ML) and artificial intelligence (AI) play a pivotal role in decision-making across various domains, including automated driving, medical diagnostics, and finance. With ML and AI models often surpassing human capabilities, it becomes crucial to understand their prediction and estimation processes. Interpretive ML algorithms and explainable models, such as LIME and SHAP, were developed to address this need by constructing approximate simple models to explain the impact of data features on outcomes. However, deriving explanations from existing methods can be challenging, prompting the development of a novel approach called Approximate Inverse Model Explanations (AIME). Associate Professor Takafumi Nakanishi of Musashino University introduced AIME, a method that provides more intuitive explanations by estimating and constructing an inverse operator for an ML or AI model. This operator evaluates the significance of both local and global features on the model’s outputs. AIME also incorporates a representative similarity distribution plot that employs representative estimation instances to identify how a particular prediction relates to other instances, shedding light on the target dataset distribution’s complexity. Studies have shown that explanations obtained from AIME are simpler and more intuitive compared to those from LIME and SHAP, proving effective for diverse datasets, including tabular data and handwritten images. Additionally, the similarity distribution plot provides an objective visualization of the model’s complexity. AIME’s robustness in handling multicollinearity was also demonstrated through experiments. This development enhances the interpretability of ML and AI models, fostering trust and bridging the gap between humans and AI.
Innovative Approximate Inverse Model Explanations Method Unveils Local and Global ML Insights
