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 Fernanda Cirino(fernandarcpassos@gmail.com)
2 Carlos Maia(carlosdiasmaia@gmail.com)
3 Marcelo Balbino(marcelobalbino@gmail.com)
4 Cristiane Nobre(nobre@pucminas.br)

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

Reference
# Reference
1 Aggarwal, A., Lohia, P., Nagar, S., Dey, K., and Saha, D. Black box fairness testing of machine learning models. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ESEC/FSE 2019. Association for Computing Machinery, New York, NY, USA, pp. 625–635, 2019.
2 Balbino, M. d. S., Zárate, L. E. G., and Nobre, C. N. Csse - an agnostic method of counterfactual, selected, and social explanations for classification models. Expert Systems with Applications, 2023.
3 Chzhen, E., Denis, C., Hebiri, M., Oneto, L., and Pontil, M. Leveraging labeled and unlabeled data for consistent fair binary classification, 2020.
4 Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. Fairness through awareness, 2011.
5 Edor, J. John rawls’s concept of justice as fairness. PINISI Discretion Review vol. 4, pp. 179, 12, 2020.
6 Gomez, O., Holter, S., Yuan, J., and Bertini, E. Advice: Aggregated visual counterfactual explanations for machine learning model validation. 2021 IEEE Visualization Conference (VIS), 2021.
7 Guidotti, R., Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., and Turini, F. Factual and counter- factual explanations for black box decision making. IEEE Intelligent Systems 34 (6): 14–23, 2019.
8 Hardt, M., Price, E., and Srebro, N. Equality of opportunity in supervised learning, 2016.
9 Jain, A., Ravula, M., and Ghosh, J. Biased models have biased explanations, 2020.
10 Kim, M. P., Ghorbani, A., and Zou, J. Multiaccuracy: Black-box post-processing for fairness in classification, 2018.
11 Kusner, M. J., Loftus, J. R., Russell, C., and Silva, R. Counterfactual fairness, 2018.
12 Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. A survey on bias and fairness in machine learning. ACM Comput. Surv. 54 (6), jul, 2021.
13 Miller, T. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence vol. 267, pp. 1–38, 2019.
14 Oneto, L. and Chiappa, S. pp. 155–196. In L. Oneto, N. Navarin, A. Sperduti, e D. Anguita (Eds.), Fairness in Machine Learning. Springer International Publishing, Cham, pp. 155–196, 2020.
15 Petersen, F., Mukherjee, D., Sun, Y., and Yurochkin, M. Post-processing for individual fairness, 2021.
16 Saxena, N. A. Perceptions of fairness. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society.
17 AIES ’19. Association for Computing Machinery, New York, NY, USA, pp. 537–538, 2019.
18 Saxena, N. A., Huang, K., DeFilippis, E., Radanovic, G., Parkes, D. C., and Liu, Y. How do fairness definitions fare? testing public attitudes towards three algorithmic definitions of fairness in loan allocations. Artificial Intelligence vol. 283, pp. 103238, 2020.
19 Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viégas, F., and Wilson, J. The what-if tool: Interactive probing of machine learning models. IEEE Transactions on Visualization and Computer Graphics 26 (1): 56–65, 2020.
20 Symposium on Knowledge Discovery, Mining and Learning, KDMILE 2023.