From Kepler to Newton: Explainable AI for Science

Li, Zelong; Ji, Jianchao; Zhang, Yongfeng

doi:10.48550/arxiv.2111.12210

Cited by 3 publications

(4 citation statements)

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“…Rather than focusing on societal issues of AI, XAI provides practical techniques and tools that hold significant potential for scientific discovery and research [24], [266], [267]. Here, we categorize these works into three groups by their approaches of leveraging XAI techniques.…”

Section: Scientific Explorations As Model Explanationmentioning

confidence: 99%

Classification of Kinematic Data Using Explainable Artificial Intelligence (XAI) for Smart Motion

Yong¹

2021

Explainable Artificial Intelligence for Smart Cities

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Given the complexity and lack of transparency in deep neural networks (DNNs), extensive efforts have been made to make these systems more interpretable or explain their behaviors in accessible terms. Unlike most reviews, which focus on algorithmic and model-centric perspectives, this work takes a "data-centric" view, examining how data collection, processing, and analysis contribute to explainable AI (XAI). We categorize existing work into three categories subject to their purposes: interpretations of deep models, referring to feature attributions and reasoning processes that correlate data points with model outputs; influences of training data, examining the impact of training data nuances, such as data valuation and sample anomalies, on decision-making processes; and insights of domain knowledge, discovering latent patterns and fostering new knowledge from data and models to advance social values and scientific discovery. Specifically, we distill XAI methodologies into data mining operations on training and testing data across modalities, such as images, text, and tabular data, as well as on training logs, checkpoints, models and other DNN behavior descriptors. In this way, our study offers a comprehensive, data-centric examination of XAI from a lens of data mining methods and applications.

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Section: Scientific Explorations As Model Explanationmentioning

confidence: 99%

Classification of Kinematic Data Using Explainable Artificial Intelligence (XAI) for Smart Motion

Yong¹

2021

Explainable Artificial Intelligence for Smart Cities

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“…Feature Importance in Nonlinear Embeddings automatically determines the important features that revealed previously unknown scientiﬁc attributes. Li et al [ 16 ] showed that concepts similar to Kepler laws of planetary motion and the Newton law of universal gravitation can be obtained through XAI methods.…”

Section: Introductionmentioning

confidence: 99%

“…decreasing NN by16.9% can cause cardinal number (CD), DT, and JJ to decrease or stay unchanged. Although the change in NN does not contribute to flipping the result, the resultant changes of CD and DT are sufficient to flip the outcome.…”

mentioning

confidence: 99%

Revealing the Roles of Part-of-Speech Taggers in Alzheimer Disease Detection: Scientific Discovery Using One-Intervention Causal Explanation

Wen¹,

Wang²,

Subbalakshmi³

et al. 2023

JMIR Form Res

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Background Recently, rich computational methods that use deep learning or machine learning have been developed using linguistic biomarkers for the diagnosis of early-stage Alzheimer disease (AD). Moreover, some qualitative and quantitative studies have indicated that certain part-of-speech (PoS) features or tags could be good indicators of AD. However, there has not been a systematic attempt to discover the underlying relationships between PoS features and AD. Moreover, there has not been any attempt to quantify the relative importance of PoS features in detecting AD. Objective Our goal was to disclose the underlying relationship between PoS features and AD, understand whether PoS features are useful in AD diagnosis, and explore which PoS features play a vital role in the diagnosis. Methods The DementiaBank, containing 1049 transcripts from 208 patients with AD and 243 transcripts from 104 older control individuals, was used. A total of 27 PoS features were extracted from each record. Then, the relationship between AD and each of the PoS features was explored. A transformer-based deep learning model for AD prediction using PoS features was trained. Then, a global explainable artificial intelligence method was proposed and used to discover which PoS features were the most important in AD diagnosis using the transformer-based predictor. A global (model-level) feature importance measure was derived as a summary from the local (example-level) feature importance metric, which was obtained using the proposed causally aware counterfactual explanation method. The unique feature of this method is that it considers causal relations among PoS features and can, hence, preclude counterfactuals that are improbable and result in more reliable explanations. Results The deep learning–based AD predictor achieved an accuracy of 92.2% and an F1-score of 0.955 when distinguishing patients with AD from healthy controls. The proposed explanation method identified 12 PoS features as being important for distinguishing patients with AD from healthy controls. Of these 12 features, 3 (25%) have been identified by other researchers in previous works in psychology and natural language processing. The remaining 75% (9/12) of PoS features have not been previously identified. We believe that this is an interesting finding that can be used in creating tests that might aid in the diagnosis of AD. Note that although our method is focused on PoS features, it should be possible to extend it to more types of features, perhaps even those derived from other biomarkers, such as syntactic features. Conclusions The high classification accuracy of the proposed deep learner indicates that PoS features are strong clues in AD diagnosis. There are 12 PoS features that are strongly tied to AD, and because language is a noninvasive and potentially cheap method for detecting AD, this work shows some promising directions in this field.

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“…Feature Importance in Nonlinear Embeddings automatically determines the important features that revealed previously unknown scientific attributes. Li et al [16] showed that concepts similar to Kepler laws of planetary motion and the Newton law of universal gravitation can be obtained through XAI methods.…”

Section: Introductionmentioning

confidence: 99%

Revealing the Roles of Part-of-Speech Taggers in Alzheimer Disease Detection: Scientific Discovery Using One-Intervention Causal Explanation (Preprint)

Wen¹,

Wang²,

Subbalakshmi³

et al. 2022

Preprint

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BACKGROUND Machine learning-based Alzheimer's detection using natural language processing has drawn increasing attention because of its low cost compared with traditional methods. However, most of these models are black-boxes, and the decision mechanisms of the AI are obscure. In some fields like medicine, this obscurity gets in the way of widespread adoption. This has led to the development of a new class of techniques that are generally referred to as explainable AI (XAI). One approach to this problem is counter-factual explanations which answer “what if” questions like “What would have happened to Y, had I not done X?”. OBJECTIVE This study aims to improve the transparency of a the-state-of-art language-based Alzheimer’s disease (AD) detection model and discover linguistic biomarkers that are indicative of AD and hence can be used as tools for automated diagnosis of AD. METHODS In this paper, a new explainable artificial intelligence (XAI) method is proposed and named one-intervention counterfactual explanation (OICE). This method works on the state-of-the-art language-based, deep learning method for AD detection and provides an explanation of that method. The proposed OICE incorporates causal factors among the features used in the detection of AD, to provide more transparency of the AI’s decision. This is in contrast to conventional counterfactual explanation methods which do not incorporate causal mechanisms. An understanding of causal factors can go beyond mere statistical correlation to provide a better understanding of the underlying physical phenomenon. The proposed OICE generates counterfactual explanations from a predefined deep-based structural causal model (SCM). The proposed method generated explanations of the AI’s decision by only intervening on one feature at a time. Since OICE provides explanations for individual samples, we then analyze the counterfactual explanations statistically and define some metrics to quantify the effect of every feature. RESULTS We find 11 language level biomarkers for Alzheimer’s disease detection such as adverb, pronoun, noun, preposition, etc. Previous work in psychology and NLP points out adverbs, pronouns, and nouns as potential biomarkers. Our study concurs. We also find new biomarkers that were not reported in previous studies, such as preposition, predeterminer, etc. Our results also reveal how these biomarkers are involved in the diagnostic process from a causal perspective. For example, an on-average 20.2% increase in predeterminer, causes determiner, verb (present particle), and grammatical particles change, resulting in flipping in the diagnosis from control to Alzheimer’s disease. This implies that predeterminer is potentially a strong indicator of the individual’s health and can function as a strong biomarker. CONCLUSIONS Our findings show consistency with previous works in psychology and natural language processing (NLP). Additionally, we offer a new explanation about how intervening a feature can affect the model's decisions using the pre-defined SCM.

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From Kepler to Newton: Explainable AI for Science

Cited by 3 publications

References 35 publications

Classification of Kinematic Data Using Explainable Artificial Intelligence (XAI) for Smart Motion

Classification of Kinematic Data Using Explainable Artificial Intelligence (XAI) for Smart Motion

Revealing the Roles of Part-of-Speech Taggers in Alzheimer Disease Detection: Scientific Discovery Using One-Intervention Causal Explanation

Revealing the Roles of Part-of-Speech Taggers in Alzheimer Disease Detection: Scientific Discovery Using One-Intervention Causal Explanation (Preprint)

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