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The integration of AI in education holds immense potential for personalizing learning experiences and transforming instructional practices.
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Zhang, W.: Online and customizable fairness-aware learning. Knowledge and Information Systems, 1–28 (2025)
2025
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Yin, Z., Wang, Z., Palikhea, A., Liu, Z., Liu, J., Zhang, W.: Amcr: A framework for assessing and mitigating copyright risks in generative models. In: 28th European Conference on Artificial Intelligence, (2025)
2025
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Wang, Z., Yin, Z., Zhang, W.: A unified framework for fair graph generation: Theoretical guarantees and empirical advances. Advances in Neural Information Processing Systems (2025)
2025
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Wang, Z., Wu, A., Moniz, N., Hu, S., Knijnenburg, B., Zhu, X., Zhang, W.: Towards fairness with limited demographics via disentangled learning, 565–573 (2025) https://doi.org/10.24963/ijcai.2025/64 . Main Track
2025
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Wang, Z., Liu, F., Pan, S., Liu, J., Saeed, F., Qiu, M., Zhang, W.: fairgnn-wod: Fair graph learning without complete demographics, 556–564 (2025) https://doi.org/10.24963/ijcai.2025/63 . Main Track
2025
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2025
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2025
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Yin, Z., Wang, Z., Palikhe, A., Zhang, W.: Uncertain boundaries: A tutorial on copyright challenges and cross-disciplinary solutions for generative ai. In: Proceedings of the 34th ACM International Conference on Information and Knowledge Management (2025)
2025
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Amon, A., Yin, Z., Wang, Z., Palikhe, A., Zhang, W.: Uncertain boundaries: Multidisciplinary approaches to copyright issues in generative ai. ACM SIGKDD Explorations Newsletter (2025)
2025
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Wang, Z., Yin, Z., Yap, R., Zhang, W.: Ai fairness beyond complete demographics: Current achievements and future directions. In: 28th European Conference on Artificial Intelligence (2025)
2025
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