Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

Longo, Luca; Brcic, Mario; Cabitza, Federico; Choi, Jaesik; Confalonieri, Roberto; Ser, Javier Del; Guidotti, Riccardo; Hayashi, Yoichi; Herrera, Francisco; Holzinger, Andreas; Jiang, Richard; Khosravi, Hassan; Lecue, Freddy; Malgieri, Gianclaudio; Páez, Andrés; Samek, Wojciech; Schneider, Johannes; Speith, Timo; Stumpf, Simone

doi:10.1016/j.inffus.2024.102301

Cited by 47 publications

(16 citation statements)

References 137 publications

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“…Machine and deep learning-based applications have been widely adopted for solving supervised AD detection with EEG data analysis [18,[23][24][25]. For example, Convolutional Neural Networks (CNNs) have been trained on functional brain connectivity features to detect AD and other neurological disorders automatically [26].…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer’s Disease with Electroencephalography in Resting-State Adults

Lal,

Chikkankod,

Longo

2024

Brain Sciences

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Early-stage Alzheimer’s disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis and the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identifying AD and FTD biomarkers from electroencephalographic (EEG) signals. We developed an optimised machine learning architecture that integrates sliding windowing, feature extraction, and supervised learning to distinguish between AD and FTD patients, as well as from healthy controls (HCs). Our model, with a 90% overlap for sliding windowing, SVD entropy for feature extraction, and K-Nearest Neighbors (KNN) for supervised learning, achieved a mean F1-score and accuracy of 93% and 91%, 92.5% and 93%, and 91.5% and 91% for discriminating AD and HC, FTD and HC, and AD and FTD, respectively. The feature importance array, an explainable AI feature, highlighted the brain lobes that contributed to identifying and distinguishing AD and FTD biomarkers. This research introduces a novel framework for detecting and discriminating AD and FTD using EEG signals, addressing the need for accurate early-stage diagnostics. Furthermore, a comparative evaluation of sliding windowing, multiple feature extraction, and machine learning methods on AD/FTD detection and discrimination is documented.

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Section: Related Workmentioning

confidence: 99%

A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer’s Disease with Electroencephalography in Resting-State Adults

Lal,

Chikkankod,

Longo

2024

Brain Sciences

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“…In yet another recent survey on XAI methods, including LIME and SHAP, for use in the detection of Alzheimer's disease [20], the authors also highlighted the limitations and open challenges with XAI methods. A number of open issues with XAI have been discussed under nine categories providing research directions in a recently published work [21].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI

Pendyala,

Kim

2024

Electronics

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Machine learning is increasingly and ubiquitously being used in the medical domain. Evaluation metrics like accuracy, precision, and recall may indicate the performance of the models but not necessarily the reliability of their outcomes. This paper assesses the effectiveness of a number of machine learning algorithms applied to an important dataset in the medical domain, specifically, mental health, by employing explainability methodologies. Using multiple machine learning algorithms and model explainability techniques, this work provides insights into the models’ workings to help determine the reliability of the machine learning algorithm predictions. The results are not intuitive. It was found that the models were focusing significantly on less relevant features and, at times, unsound ranking of the features to make the predictions. This paper therefore argues that it is important for research in applied machine learning to provide insights into the explainability of models in addition to other performance metrics like accuracy. This is particularly important for applications in critical domains such as healthcare.

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“…Rudin [18] pointed out the limitations of some approaches to explainable machine learning, suggesting that interpretable models should be used instead of black box models for making high stakes decisions. Recently, XAI has entered a new phase with the provisional agreement of the AI Act aimed at explaining AI [19]. This is important because black box machine learning applications remain challenging in several domains, such as health care and finance.…”

Section: Introductionmentioning

confidence: 99%

Why Do Tree Ensemble Approximators Not Outperform the Recursive-Rule eXtraction Algorithm?

Onishi,

Nishimura,

Fujimura

et al. 2024

MAKE

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Although machine learning models are widely used in critical domains, their complexity and poor interpretability remain problematic. Decision trees (DTs) and rule-based models are known for their interpretability, and numerous studies have investigated techniques for approximating tree ensembles using DTs or rule sets, even though these approximators often overlook interpretability. These methods generate three types of rule sets: DT based, unordered, and decision list based. However, very few metrics exist that can distinguish and compare these rule sets. Therefore, the present study proposes an interpretability metric to allow for comparisons of interpretability between different rule sets and investigates the interpretability of the rules generated by the tree ensemble approximators. We compare these rule sets with the Recursive-Rule eXtraction algorithm (Re-RX) with J48graft to offer insights into the interpretability gap. The results indicate that Re-RX with J48graft can handle categorical and numerical attributes separately, has simple rules, and achieves a high interpretability, even when the number of rules is large. RuleCOSI+, a state-of-the-art method, showed significantly lower results regarding interpretability, but had the smallest number of rules.

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Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

Cited by 47 publications

References 137 publications

A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer’s Disease with Electroencephalography in Resting-State Adults

A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer’s Disease with Electroencephalography in Resting-State Adults

Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI

Why Do Tree Ensemble Approximators Not Outperform the Recursive-Rule eXtraction Algorithm?

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