征稿期刊

Technological Forecasting and Social Change

期刊级别

SSCI (JCR 2023)

IF 12.9

Q1 (BUSINESS 4/302)

Q1 (REGIONAL & URBAN PLANNING 1/54)

征稿主题

Advancing Responsible AI Composition: Embracing eXplainability for Practical Implementation

细分领域

Techniques for enhancing the transparency of neural network models

Case studies on the application of explainable AI in different sectors, e.g. healthcare, finance, and criminal justice

Methods for incorporating experiential learning into AI systems

Approaches for contextual learning and understanding in AI models

Strategies for aligning AI systems with organizational values and ethics

Frameworks for integrating user feedback and interactions into AI models

Comparative analyses of rule-based systems and modern AI models in terms of eXplainability

Ethical considerations and challenges in the deployment of explainable AI

Theoretical advancements in explainable AI (XAI) and Responsible AI (RAI)

Practical implementation of responsible AI in various industries

The role of big data, LLM attributes in enhancing AI transparency

Impact of machine learning models on AI eXplainability

User-centric approaches to responsible AI development and deployment

Organizational dependencies and their influence on responsible AI systems

Future directions for responsible AI research and practice

重要时间

Submission Deadline: 30 October 2025

原文：https://www.sciencedirect.com/journal/technological-forecasting-and-social-change/about/call-for-papers#advancing-responsible-ai-composition-embracing-explainability-for-practical-implementation