skip to main content

This content will become publicly available on November 1, 2024

Title: ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AI
Unexplainable black-box models create scenarios where anomalies cause deleterious responses, thus creating unacceptable risks. These risks have motivated the field of eXplainable Artificial Intelligence (XAI) which improves trust by evaluating local interpretability in black-box neural networks. Unfortunately, the ground truth is unavailable for the model's decision, so evaluation is limited to qualitative assessment. Further, interpretability may lead to inaccurate conclusions about the model or a false sense of trust. We propose to improve XAI from the vantage point of the user's trust by exploring a black-box model's latent feature space. We present an approach, ProtoShotXAI, that uses a Prototypical few-shot network to explore the contrastive manifold between nonlinear features of different classes. A user explores the manifold by perturbing the input features of a query sample and recording the response for a subset of exemplars from any class. Our approach is a locally interpretable XAI model that can be extended to, and demonstrated on, few-shot networks. We compare ProtoShotXAI to the state-of-the-art XAI approaches on MNIST, Omniglot, and ImageNet to demonstrate, both quantitatively and qualitatively, that ProtoShotXAI provides more flexibility for model exploration. Finally, ProtoShotXAI also demonstrates novel explainability and detectability on adversarial samples.  more » « less
Award ID(s):
Author(s) / Creator(s):
Publisher / Repository:
Date Published:
Journal Name:
Journal of machine learning research
Page Range / eLocation ID:
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Background

    Advanced machine learning models have received wide attention in assisting medical decision making due to the greater accuracy they can achieve. However, their limited interpretability imposes barriers for practitioners to adopt them. Recent advancements in interpretable machine learning tools allow us to look inside the black box of advanced prediction methods to extract interpretable models while maintaining similar prediction accuracy, but few studies have investigated the specific hospital readmission prediction problem with this spirit.


    Our goal is to develop a machine-learning (ML) algorithm that can predict 30- and 90- day hospital readmissions as accurately as black box algorithms while providing medically interpretable insights into readmission risk factors. Leveraging a state-of-art interpretable ML model, we use a two-step Extracted Regression Tree approach to achieve this goal. In the first step, we train a black box prediction algorithm. In the second step, we extract a regression tree from the output of the black box algorithm that allows direct interpretation of medically relevant risk factors. We use data from a large teaching hospital in Asia to learn the ML model and verify our two-step approach.


    The two-step method can obtain similar prediction performance as the best black box model, such as Neural Networks, measured by three metrics: accuracy, the Area Under the Curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC), while maintaining interpretability. Further, to examine whether the prediction results match the known medical insights (i.e., the model is truly interpretable and produces reasonable results), we show that key readmission risk factors extracted by the two-step approach are consistent with those found in the medical literature.


    The proposed two-step approach yields meaningful prediction results that are both accurate and interpretable. This study suggests a viable means to improve the trust of machine learning based models in clinical practice for predicting readmissions through the two-step approach.

    more » « less
  2. Despite AI’s significant growth, its “black box” nature creates challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in high-risk applications. Explainable AI (XAI) has emerged to help with this problem. Designing effectively fast and accurate XAI is still challenging, especially in numerical applications. We propose a novel XAI model named Transparency Relying Upon Statistical Theory (TRUST) for XAI. TRUST XAI models the statistical behavior of the underlying AI’s outputs. Factor analysis is used to transform the input features into a new set of latent variables. We use mutual information to rank these parameters and pick only the most influential ones on the AI’s outputs and call them “representatives” of the classes. Then we use multi-model Gaussian distributions to determine the likelihood of any new sample belonging to each class. The proposed technique is a surrogate model that is not dependent on the type of the underlying AI. TRUST is suitable for any numerical application. Here, we use cybersecurity of the industrial internet of things (IIoT) as an example application. We analyze the performance of the model using three different cybersecurity datasets, including “WUSTLIIoT”, “NSL-KDD”, and “UNSW”. We also show how TRUST is explained to the user. The TRUST XAI provides explanations for new random samples with an average success rate of 98%. Also, the advantages of our model over another popular XAI model, LIME, including performance, speed, and the method of explainability are evaluated. 
    more » « less
  3. Recent development in the field of explainable artificial intelligence (XAI) has helped improve trust in Machine-Learning-as-a-Service (MLaaS) systems, in which an explanation is provided together with the model prediction in response to each query. However, XAI also opens a door for adversaries to gain insights into the black-box models in MLaaS, thereby making the models more vulnerable to several attacks. For example, feature-based explanations (e.g., SHAP) could expose the top important features that a black-box model focuses on. Such disclosure has been exploited to craft effective backdoor triggers against malware classifiers. To address this trade-off, we introduce a new concept of achieving local differential privacy (LDP) in the explanations, and from that we establish a defense, called XRand, against such attacks. We show that our mechanism restricts the information that the adversary can learn about the top important features, while maintaining the faithfulness of the explanations. 
    more » « less
  4. While machine learning classifier models become more widely adopted, opaque “black-box” models remain mostly inscrutable for a variety of reasons. Since their applications increasingly involve decisions impacting the lives of humans, there is increasing demand that their predictions be understandable to humans. Of particular interest in eXplainable AI (XAI) is the interpretability of explanations, i.e., that a model’s prediction should be understandable in terms of the input features. One popular approach is LIME, which offers a model-agnostic framework for explaining any classifier. However, questions remain about the limitations and vulnerabilities of such post-hoc explainers. We have built a tool for generating synthetic tabular data sets which enables us to probe the explanation system opportunistically based on its architecture. In this paper, we report on our success in revealing a scenario where LIME’s explanation violates local faithfulness. 
    more » « less
  5. This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy.

    more » « less