Computational Intelligence and Neuroscience

Interpretable Methods of Artificial Intelligence Algorithms


Publishing date
01 Jan 2023
Status
Closed
Submission deadline
02 Sep 2022

Lead Editor
Guest Editors

1Beijing Jiaotong University, Beijing, China

2East China Jiaotong University, Nanchang, China

3Kookmin University, Seoul, Republic of Korea

This issue is now closed for submissions.

Interpretable Methods of Artificial Intelligence Algorithms

This issue is now closed for submissions.

Description

The application of artificial intelligence technology in the information field, represented by deep learning, has greatly improved the efficiency of information utilization and mining value and profoundly influenced the business form in various fields. From amongst the deeply debated issues of AI-related ethics, algorithmic discrimination, algorithmic correctness, and security, the issue of interpretability of AI algorithms represented by deep learning algorithms has arisen.

The development of human rationality has led us to believe that if a judgment or decision is interpretable, it will be easier for us to understand its strengths and weaknesses, to assess its risks, to know to what extent and in what contexts it can be trusted, and in what ways we can continuously improve it in order to maximize consensus, minimize risks, and promote the continuous development of the corresponding field. In actuality, the paradigm of reasoning and symbolic thinking that originated before the age of artificial intelligence, and evolved after it, can help in developing new interpretable methods for machine learning in order to grasp how these models can find answers. This will be crucial to building robust evidence-based and explainable models as well as to determine the performance and reliability of such models. Introducing interpretability techniques to deep learning models which use a variety of neural networks will provide explanations and empirical guidelines on how the deep learning model is generating decisions at every instance in time. These interpretable methods will shed light on the neural network black box and on how predictions are generated. However, interpretability techniques vary broadly from being machine learning specific methods to machine learning model agnostics or from being local to global. Interpretability techniques in all their varieties are crucial as they guard against embedded bias as well as preventing intensive debugging. These techniques play an increasingly important role in science, engineering, and society as they provide precise answers on how machine learning algorithms generate decisions.

This Special Issue covers several of the most common interpretability methods, their relative advantages and disadvantages, their taxonomy and application in various fields. We welcome original research and review articles.

Potential topics include but are not limited to the following:

  • Interpretability methods for explaining complex black-box machine models
  • Interpretability methods for creating white-box machine learning models
  • Interpretability methods for promoting fairness
  • Interpretability methods for analyzing the sensitivity of machine learning model predictions
  • Agnostics interpretability methods
  • Specific interpretability methods
  • Global and local interpretability methods
  • Transparency techniques for interpretability methods
  • Explainable techniques for interpretability methods
  • Retrospective approach to interpretability methods
  • Prospective approach to interpretability methods
  • Applications of interpretability machine learning methods (e.g., evidence-based healthcare, logistics)

Articles

  • Special Issue
  • - Volume 2023
  • - Article ID 7510419
  • - Research Article

ML-DSTnet: A Novel Hybrid Model for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning and Dempster–Shafer Theory

Mohsen Eftekharian | Ali Nodehi | Rasul Enayatifar
  • Special Issue
  • - Volume 2023
  • - Article ID 6271241
  • - Research Article

A Method for Predicting Production Costs Based on Data Fusion from Multiple Sources for Industry 4.0: Trends and Applications of Machine Learning Methods

Masoud Soleimani | Hossein Naderian | ... | Seyed Reza Agha Seyed Hosseini
  • Special Issue
  • - Volume 2023
  • - Article ID 9864372
  • - Retraction

Retracted: A Convolutional Neural Network-Based Model for Supply Chain Financial Risk Early Warning

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9761712
  • - Retraction

Retracted: Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9820360
  • - Retraction

Retracted: Robust Transmit Beamforming Algorithm for Low-Altitude Slow-Speed Small Target Detection

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9816924
  • - Retraction

Retracted: A Crop Growth Prediction Model Using Energy Data Based on Machine Learning in Smart Farms

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9841926
  • - Retraction

Retracted: Drought Assessment Based on Data Fusion and Deep Learning

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9805091
  • - Retraction

Retracted: Visual Identification of Mobile App GUI Elements for Automated Robotic Testing

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9873074
  • - Retraction

Retracted: Temporal and Spatial Differences of Urban Ecological Environment and Economic Development Based on Graph Neural Network

Computational Intelligence and Neuroscience
  • Special Issue
  • - Volume 2023
  • - Article ID 9875756
  • - Retraction

Retracted: Method for Quantum Denoisers Using Convolutional Neural Network

Computational Intelligence and Neuroscience

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