AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations
This work addresses the need for customizable and diverse explanations in machine learning, particularly for users requiring insights into model predictions, though it appears incremental by building on existing example-based methods.
The paper tackled the problem of explaining black-box model predictions by identifying influential training samples, proposing AIDE to provide antithetical, intent-based, diverse explanations, and demonstrated its effectiveness on image and text classification tasks through quantitative, qualitative, and user study evaluations.
For many use-cases, it is often important to explain the prediction of a black-box model by identifying the most influential training data samples. Existing approaches lack customization for user intent and often provide a homogeneous set of explanation samples, failing to reveal the model's reasoning from different angles. In this paper, we propose AIDE, an approach for providing antithetical (i.e., contrastive), intent-based, diverse explanations for opaque and complex models. AIDE distinguishes three types of explainability intents: interpreting a correct, investigating a wrong, and clarifying an ambiguous prediction. For each intent, AIDE selects an appropriate set of influential training samples that support or oppose the prediction either directly or by contrast. To provide a succinct summary, AIDE uses diversity-aware sampling to avoid redundancy and increase coverage of the training data. We demonstrate the effectiveness of AIDE on image and text classification tasks, in three ways: quantitatively, assessing correctness and continuity; qualitatively, comparing anecdotal evidence from AIDE and other example-based approaches; and via a user study, evaluating multiple aspects of AIDE. The results show that AIDE addresses the limitations of existing methods and exhibits desirable traits for an explainability method.