An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives

Nagahisarchoghaei, Mohammad; Nur, Nasheen; Cummins, Logan; Nur, Nashtarin; Karimi, Mirhossein Mousavi; Nandanwar, Shreya; Bhattacharyya, Siddhartha; Rahimi, Shahram

doi:10.3390/electronics12051092

Cited by 22 publications

(7 citation statements)

References 219 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 15 also reveals that despite the efforts of several studies (Nagahisarchoghaei et al, 2023;Das and Rad, 2020;Angelov et al, 2021) to clarify various definitions of XAI for users, questions about comparing and distinguishing these definitions persist, even though XAI is popular. This presents an opportunity for research to articulate these definitions more practically.…”

Section: Xai Researchersmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Black Box: Analyzing Explainable AI Challenges and Best Practices Through Stack Exchange Discussions

Sayyadnejad,

Asgari,

Sami

et al. 2024

Preprint

View full text Add to dashboard Cite

Explainable Artificial Intelligence (XAI) is a crucial domain within research and industry, aiming to develop AI models that provide human-understandable explanations for their decisions. While the challenges in AI, deep learning, and big data have been extensively explored, the specific concerns of XAI developers have received limited attention. To address this gap, we analyzed discussions on Stack Exchange websites to delve into these issues. Through a combination of automated and manual analysis, we identified 6 overarching categories, 10 distinct topics, and 40 sub-topics commonly discussed by developers. Our examination revealed a steady rise in discussions on XAI since late 2015, initially focusing on conceptualization and practical applications, with a notable surge in activity across all topic categories since 2019. Notably, Concepts and Applications, Tools Troubleshooting, and Neural Networks Interpretation emerged as the most popular topics. Troubleshooting challenges were commonly encountered with tools like Shap, Eli5, and Aif360, while Visualization issues were prevalent with Yellowbrick and Shap. Furthermore, our analysis suggests that addressing questions related to XAI poses greater difficulty compared to other machine-learning questions.

show abstract

Section: Xai Researchersmentioning

confidence: 99%

“…This presents an opportunity for research to articulate these definitions more practically. Prior research (Nagahisarchoghaei et al, 2023) has identified the applications of XAI technologies. The primary application of these technologies is to enhance AI system transparency for more practical reliability (Hanif et al, 2023).…”

Section: Xai Researchersmentioning

confidence: 99%

Exploring the Black Box: Analyzing Explainable AI Challenges and Best Practices Through Stack Exchange Discussions

Sayyadnejad,

Asgari,

Sami

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…To overcome this problem, XAI has emerged to tackle the void in deep learning models [41]. The dexterity of XAI methods to expound the comportment of the model and establish confidence in it has been demonstrated in several applications [42][43][44]. One such application is the identification of cancer biomarkers; our study aims to use the XAI-based feature selection approach to identify a limited set of biomarkers associated with OP-SCC.…”

Section: Explainable Ai For Model Interpretationmentioning

confidence: 99%

Dissecting Crucial Gene Markers Involved in HPV-Associated Oropharyngeal Squamous Cell Carcinoma from RNA-Sequencing Data through Explainable Artificial Intelligence

Sekaran,

Varghese,

Krishnan

et al. 2024

Front. Biosci. (Landmark Ed)

View full text Add to dashboard Cite

Background: The incidence rate of oropharyngeal squamous cell carcinoma (OPSCC) worldwide is alarming. In the clinical community, there is a pressing necessity to comprehend the etiology of the OPSCC to facilitate the administration of effective treatments. Methods: This study confers an integrative genomics approach for identifying key oncogenic drivers involved in the OPSCC pathogenesis. The dataset contains RNA-Sequencing (RNA-Seq) samples of 46 Human papillomavirus-positive head and neck squamous cell carcinoma and 25 normal Uvulopalatopharyngoplasty cases. The differential marker selection is performed between the groups with a log2FoldChange (FC) score of 2, adjusted p-value < 0.01, and screened 714 genes. The Particle Swarm Optimization (PSO) algorithm selects the candidate gene subset, reducing the size to 73. The state-of-the-art machine learning algorithms are trained with the differentially expressed genes and candidate subsets of PSO. Results: The analysis of predictive models using Shapley Additive exPlanations revealed that seven genes significantly contribute to the model’s performance. These include ECT2, LAMC2, and DSG2, which predominantly influence differentiating between sample groups. They were followed in importance by FAT1, PLOD2, COL1A1, and PLAU. The Random Forest and Bayes Net algorithms also achieved perfect validation scores when using PSO features. Furthermore, gene set enrichment analysis, protein-protein interactions, and disease ontology mining revealed a significant association between these genes and the target condition. As indicated by Shapley Additive exPlanations (SHAPs), the survival analysis of three key genes unveiled strong over-expression in the samples from “The Cancer Genome Atlas”. Conclusions: Our findings elucidate critical oncogenic drivers in OPSCC, offering vital insights for developing targeted therapies and enhancing understanding its pathogenesis.

show abstract

“…A detailed analysis of XAI research across various domains and applications is given in [7]. It provides an additional perspective on interpretability techniques as tools to give machine learning models the ability to explain or present their behavior understandably to humans.…”

Section: Related Work 21 Explainable Artificial Intelligencementioning

confidence: 99%

Business Purchase Prediction Based on XAI and LSTM Neural Networks

Predić,

Ćirić,

Stoimenov

2023

Electronics

View full text Add to dashboard Cite

The black-box nature of neural networks is an obstacle to the adoption of systems based on them, mainly due to a lack of understanding and trust by end users. Providing explanations of the model’s predictions should increase trust in the system and make peculiar decisions easier to examine. In this paper, an architecture of a machine learning time series prediction system for business purchase prediction based on neural networks and enhanced with Explainable artificial intelligence (XAI) techniques is proposed. The architecture is implemented on an example of a system for predicting the following purchases for time series using Long short-term memory (LSTM) neural networks and Shapley additive explanations (SHAP) values. The developed system was evaluated with three different LSTM neural networks for predicting the next purchase day, with the most complex network producing the best results across all metrics. Explanations generated by the XAI module are provided with the prediction results to the user to allow him to understand the system’s decisions. Another benefit of the XAI module is the possibility to experiment with different prediction models and compare input feature effects.

show abstract

An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives

Cited by 22 publications

References 219 publications

Exploring the Black Box: Analyzing Explainable AI Challenges and Best Practices Through Stack Exchange Discussions

Exploring the Black Box: Analyzing Explainable AI Challenges and Best Practices Through Stack Exchange Discussions

Dissecting Crucial Gene Markers Involved in HPV-Associated Oropharyngeal Squamous Cell Carcinoma from RNA-Sequencing Data through Explainable Artificial Intelligence

Business Purchase Prediction Based on XAI and LSTM Neural Networks

Contact Info

Product

Resources

About