As the field of Big Data analytics continues to evolve, ensuring fairness, transparency, and ethical considerations in the design and deployment of AI systems has become a critical challenge. This Research Topic focuses on advancing research in ethical AI and fairness-aware machine learning. The widespread integration of AI technologies in various industries requires that we address inherent biases and develop robust, transparent models that promote equitable outcomes for diverse populations.

The aim of this Research Topic is to provide a platform for cutting-edge research that explores the ethical dimensions of AI and big data, encouraging innovative solutions that mitigate biases, enhance model fairness, and improve the overall trustworthiness of AI systems. This issue will bring together contributions that focus on the theoretical, technical, and practical aspects of ethical AI, with particular attention to real-world applications.

We welcome submissions of original research articles and comprehensive reviews that explore ethical considerations in big data analytics. Our goal is to foster interdisciplinary discussions on how to build and deploy AI systems that are not only accurate and efficient but also fair and socially responsible. By focusing on these critical issues, this curated collection aims to inspire the next generation of AI systems that are powerful, reliable, and ethical, shaping a future where technology can be trusted to work for the benefit of all.

In this themed article collection, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Algorithmic fairness and bias in classifying and clustering big data;
• Human-in-the-loop for ethical-aware machine learning;
• Ethical recommender systems and diversity in recommendation;
• Learning ethical-aware representation of heterogeneous data domains;
• Causality-based fairness in high-dimensional data;
• Integration of observation for causality-based bias control;
• Preserving fairness in graph embedding;
• Novel visualization techniques to facilitate the query and analysis of data bias;
• Robustness and generalization of large language models;
• Bias mitigation and fairness of large language models;
• Explainability, interpretability, privacy and security of large language models;
• First-hand experience creating or with company practices for ethical AI;

And with particular focuses but not limited to these application domains:

• Application of ethical AI methods in large-scale data mining;
• Computer vision (fairness in face recognition, object relation; debiasing in image processing and video);
• Natural language processing (fair text generation, semantic parsing);
• Reinforcement learning (fairness-aware multi-agent learning, compositional imitation learning);
• Social science (racial profiling, institutional racism).

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