Interpretable Representations in Explainable AI: Analyzing Properties, Assumptions, and Limitations for Tabular, Image, and Text Data

Konsep Inti

Interpretable representations are the core component of many explainability methods that target black-box predictive models. They translate low-level data representations into high-level human-intelligible concepts to convey explanatory insights. However, many explainers overlook the merit of interpretable representations and suffer from suboptimal design choices, leading to inadequate explanations. This paper analyzes the properties, assumptions, and limitations of interpretable representations for tabular, image, and text data to provide guidelines for building trustworthy, faithful, and algorithmically sound interpretable representations.

Abstrak

The paper investigates the capabilities and limitations of interpretable representations, where presence and absence of interpretable concepts is encoded with a binary on/off vector. It first overviews popular interpretable representations for text, image, and tabular data, and identifies their core elements, parameterization, and deficiencies.
For image data, the paper examines the influence of segmentation granularity and occlusion strategy on the effectiveness of the information removal proxy. It finds that the mean-color occlusion strategy is not as effective at hiding information from the black-box model as using a single, random, or randomized color. The paper also demonstrates that as the number of segments increases, the ineffectiveness of the mean-coloring approach gets magnified due to the increased color uniformity of individual super-pixels.
For tabular data, the paper studies the influence of suboptimal configuration of interpretable representations built upon discretization of numerical features. It analyzes the implications of employing various algorithmic proxies necessary to make them computationally feasible and scalable. The paper shows that tabular data explainers built upon discretization-based interpretable representations combined with surrogate linear models are fragile, possibly misleading, and can be easily manipulated due to information loss. As a solution, the paper proposes using supervised discretization algorithms that produce up to three bins per numerical feature and employing alternative types of surrogate models, such as decision trees.
The findings allow the paper to create guidelines for building trustworthy, faithful, and algorithmically sound interpretable representations. It highlights the importance of developing representative validation criteria and metrics for individual components of explainability algorithms, which is an improvement over evaluating only the final, end-to-end explainer.

Statistik

"Interpretable representations need to be crafted for the problem at hand – ideally with a human in the loop – to become a trustworthy foundation of explainability."
"The information removal proxy required by image and tabular interpretable representations should be deterministic and domain-aware."
"For images, the occlusion colour and segmentation granularity play important roles, with mean-colour occlusion exhibiting a number of undesired properties."
"For tabular data, fidelity of continuous feature discretisation is critical, with class-aware methods yielding best results."
"Tabular data explainers built upon discretisation-based interpretable representations combined with surrogate linear models are fragile, possibly misleading, and can be easily manipulated due to information loss."

Kutipan

"Interpretable representations need to be crafted for the problem at hand – ideally with a human in the loop – to become a trustworthy foundation of explainability."
"The information removal proxy required by image and tabular interpretable representations should be deterministic and domain-aware."
"For images, the occlusion colour and segmentation granularity play important roles, with mean-colour occlusion exhibiting a number of undesired properties."
"For tabular data, fidelity of continuous feature discretisation is critical, with class-aware methods yielding best results."
"Tabular data explainers built upon discretisation-based interpretable representations combined with surrogate linear models are fragile, possibly misleading, and can be easily manipulated due to information loss."

Wawasan Utama Disaring Dari

Interpretable Representations in Explainable AI: From Theory to Practice

by Kacper Sokol... pada arxiv.org 04-29-2024

https://arxiv.org/pdf/2008.07007.pdf

Pertanyaan yang Lebih Dalam

How can the design of interpretable representations be automated to reduce human involvement while maintaining trustworthiness and faithfulness

Automating the design of interpretable representations is crucial to reduce human involvement while ensuring trustworthiness and faithfulness in explainable AI systems. One approach to automate this process is through the use of machine learning algorithms that can learn the optimal interpretable representation for a given dataset. By training models to generate interpretable representations based on the data characteristics and the desired level of explanation, the need for manual intervention can be minimized. Additionally, techniques such as autoML (Automated Machine Learning) can be employed to automatically select and optimize the parameters of interpretable representations, further streamlining the design process.
Another strategy is to leverage meta-learning algorithms that can learn from past instances of designing interpretable representations and apply this knowledge to new datasets. By capturing patterns and best practices from previous designs, these algorithms can generate interpretable representations that are tailored to specific use cases and datasets. Furthermore, the use of reinforcement learning can enable the system to iteratively improve the design of interpretable representations based on feedback from the performance of the explainable AI system.
Overall, by harnessing the power of machine learning, meta-learning, and reinforcement learning techniques, the design of interpretable representations can be automated effectively, reducing the need for manual intervention while maintaining trustworthiness and faithfulness in explainable AI systems.

What are the potential risks and unintended consequences of using interpretable representations that do not accurately reflect the underlying black-box model's decision-making process

Using interpretable representations that do not accurately reflect the underlying black-box model's decision-making process can lead to several risks and unintended consequences in explainable AI systems. One significant risk is the potential for misleading or incorrect explanations being provided to users, which can result in a loss of trust in the system. If the interpretable representation does not capture the true factors influencing the model's predictions, users may make decisions based on flawed or incomplete information, leading to undesirable outcomes.
Moreover, inaccurate interpretable representations can also result in biased or unfair explanations, where certain factors are either overemphasized or underrepresented in the explanation. This can perpetuate existing biases in the model and lead to discriminatory outcomes, especially in sensitive domains such as healthcare or finance.
Additionally, using interpretable representations that do not align with the underlying model's decision-making process can hinder the interpretability and usability of the system. Users may struggle to understand the explanations provided, leading to confusion and mistrust in the system's capabilities. This can ultimately limit the adoption and effectiveness of explainable AI systems in real-world applications.
To mitigate these risks and unintended consequences, it is essential to carefully design interpretable representations that accurately reflect the model's decision-making process and provide meaningful insights to users. Validation and testing of the interpretable representations against the black-box model's predictions are crucial to ensure their reliability and effectiveness in explaining the model's behavior.

How can insights from other fields, such as natural language processing, image segmentation, and discretization of tabular data, be leveraged to improve the design of interpretable representations for explainable AI

Insights from other fields such as natural language processing, image segmentation, and discretization of tabular data can be leveraged to enhance the design of interpretable representations for explainable AI systems.
From natural language processing, techniques for tokenization, stemming, and lemmatization can be adapted to preprocess textual data and create interpretable representations based on meaningful concepts. Additionally, methods for sentiment analysis and topic modeling can inform the design of interpretable representations that capture the key factors influencing text-based predictions.
Image segmentation algorithms can provide valuable insights into partitioning image data into interpretable segments, which can then be used to create binary representations for explaining image-based predictions. Techniques for occlusion and feature extraction from images can also be applied to improve the robustness and accuracy of interpretable representations in image-based explainable AI systems.
In the realm of tabular data, discretization methods from machine learning and statistics can be utilized to transform continuous features into interpretable concepts. Decision tree algorithms for feature selection and binning can inform the creation of interpretable representations that capture the essential attributes influencing tabular data predictions. Additionally, techniques for feature importance and sensitivity analysis can be adapted to evaluate the effectiveness of different interpretable representations in explaining the model's behavior.
By integrating insights and methodologies from these diverse fields, the design of interpretable representations can be enriched, leading to more effective and trustworthy explanations in explainable AI systems.