SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Renato Miyaji(re.miyaji@usp.br)
2 Pedro Corrêa(pedro.correa@usp.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Arrieta, A. B., Díaz-Rodríguez, N., Ser, J. D., Bennetot, A., Tabik, S., Barbado, A., Gar- cía, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2020). Explainable artificial intelligence (xai): : Concepts, taxonomies, opportunities and challenges toward re- sponsible ai. Information Fusion, 58(C).
2 Becker, B. and Kohavi, R. (1996). Adult. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5XW20.
3 Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J., and Eckersley, P. (2020). Explainable machine learning in deployment. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency.
4 Borys, K., Schmitt, Y., Nauta, M., Seifert, C., Krämer, N., Friedrich, C., and Nensa, F. (2023). Explainable ai in medical imaging: An overview for clinical practitioners – saliency-based xai approaches. European Journal of Radiology, 162.
5 Burton, J., Moubayed, N., and Enshaei, A. (2023). Natural language explanations for machine learning classification decisions. Proceedings of the 2023 International Joint Conference on Neural Networks (IJCNN).
6 Caseli, H. and Nunes, M. (2023). Processamento de Linguagem Natural: Conceitos, Téc- nicas e Aplicações em Português. Brasileiras - Processamento de Linguagem Natural.
7 Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
8 Chroma (2025). Chroma. Available on: https://www.trychroma.com/. Ac- cessed in 23 March 2025.
9 Dafali, S. M., Kissi, M., and El Beggar, O. (2023). Comparative study between global and local explainable models. In 2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA), pages 1–8.
10 Doran, D., Schulz, S., and Besold, T. R. (2017). What does explainable ai really mean? a new conceptualization of perspectives. In Proceedings of the First International Workshop on Comprehensibility and Explanation in AI and ML.
11 Girhepuje, S. (2023). Identifying and examining machine learning biases on adult dataset. ArXiv.
12 Gu, J., Jiang, X., Shi, Z., Tan, H., Zhai, X., Xu, C., Li, W., Shen, Y., Ma, S., Liu, H., Wang, S., Zhang, K., Wang, Z., Gao, W., Ni, L., and Guo, J. (2024). A survey on llm-as-a-judge. ArXiv.
13 James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer, Londres.
14 LangGraph (2025). Langgraph. Available on: https://langchain-ai.github. io/langgraph/tutorials/introduction/. Accessed in 23 March 2025.
15 Lundberg, S. (2018). Benchmark xgboost explanations. Available on: https://shap. readthedocs.io/en/latest/example_notebooks/benchmarks/ tabular/Benchmark%20XGBoost%20explanations.html. Accessed in 23 March 2025.
16 Lundberg, S. M. and Lee, S. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems.
17 Mindlin, D., Robrecht, A., Morasch, M., and Cimiano, P. (2024). Measuring user under- standing in dialogue-based xai systems. Proceedings of the 27th European Conference on Artificial Intelligence.
18 OpenAI (2025a). Gpt-4o mini: advancing cost-efficient intel- ligence. Available on: https://openai.com/index/ gpt-4o-mini-advancing-cost-efficient-intelligence/. Ac- cessed in 23 March 2025.
19 OpenAI (2025b). Openai o3-mini. Available on: https://openai.com/index/ openai-o3-mini/. Accessed in 23 March 2025.
20 Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). "why should i trust you?": Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
21 Richmond, K., Muddamsetty, S., Gammeltoft-Hansen, T., Olsen, H., and Moeslund, T. (2024). Explainable ai and law: An evidential survey. Digital Society, 3(1).
22 Ryo, M., Angelov, B., Mammola, S., Kass, J. M., Benito, B. M., and F, H. (2021). Ex- plainable artificial intelligence enhances the ecological interpretability of black-box species distribution models. Ecography, 44:199–205.
23 Sena, L. and Machado, J. (2024). Evaluation of fairness in machine learning models using the uci adult dataset. In Anais do XXXIX Simpósio Brasileiro de Bancos de Dados. SBC.
24 Spitzer, P., Celis, S., Martin, D., Kühl, N., and Satzger, G. (2024). Looking through the deep glasses: How large language models enhance explainability of deep learning models. Proceedings of Mensch und Computer 2024.
25 Tavily (2025). Tavily. Available on: https://docs.tavily.com/welcome. Ac- cessed in 23 March 2025.
26 Wang, B., Li, Y., Zhou, J., and Chen, F. (2025). Can llm assist in the evaluation of the quality of machine learning explanations? ArXiv.
27 Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., Zhao, W., Wei, Z., and Wen, J. (2023). A survey on large language model based autonomous agents. Frontiers Comput. Sci.
28 Zhu, Y., Yuan, H., Wang, S., Liu, S., Liu, W., Deng, C., Chen, H., Liu, Z., Dou, Z., and Wen, J. (2024). Large language models for information retrieval: A survey. ArXiv.
29 Zytek, A., Pidò, S., and Veeramachaneni, K. (2024). Llms for xai: Future directions for explaining explanations. ACM CHI Workshop on Human-Centered Explainable AI.
30 Cerneviˇcien˙e, J. and Kabašinskas, A. (2024). Explainable artificial intelligence (xai) in finance: a systematic literature review. Artificial Intelligence Review, 57(216).