CausalBioCF: Causalidade e otimização bioinspirada para geração de contrafactuais factíveis em tempo real
Resumo
Considerando-se que métodos de aprendizado de máquina vem sendo amplamente utilizados para embasar tomadas de decisões, fornecer explicações sobre o resultado gerado por eles torna-se importante. Isso é particularmente valioso em cenários de decisões de alto risco, a fim de proteger todas as partes envolvidas. Neste trabalho é abordada uma parte importante da explicabilidade de aprendizado de máquina, sendo discutida a paralelização de diferentes tipos de algoritmos para encontrar candidatos a contrafactuais.Referências
Cantú-Paz, E. et al. (1998). A survey of parallel genetic algorithms. Calculateurs paralleles, reseaux et systems repartis, 10(2):141–171.
Fatima, M., Pasha, M., et al. (2017). Survey of machine learning algorithms for disease diagnostic. Journal of Intelligent Learning Systems and Applications, 9(01):1.
Gomez-Uribe, C. A. and Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4):1–19.
Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1):66–73.
Jin, W. (2020). Research on machine learning and its algorithms and development. In Journal of Physics: Conference Series, volume 1544, page 012003. IOP Publishing.
Karaboga, D. et al. (2005). An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.
Muthiah, A. and Rajkumar, R. (2014). A comparison of artificial bee colony algorithm and genetic algorithm to minimize the makespan for job shop scheduling. Procedia Engineering, 97:1745–1754.
Narasimhan, H. (2009). Parallel artificial bee colony (pabc) algorithm. In 2009 World congress on nature & biologically inspired computing (NaBIC), pages 306–311. IEEE.
Wachter, S., Mittelstadt, B., and Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the gdpr. Harv. JL & Tech., 31:841.
Yang, F., Alva, S. S., Chen, J., and Hu, X. (2021). Model-based counterfactual synthesizer for interpretation. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pages 1964–1974.
Fatima, M., Pasha, M., et al. (2017). Survey of machine learning algorithms for disease diagnostic. Journal of Intelligent Learning Systems and Applications, 9(01):1.
Gomez-Uribe, C. A. and Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4):1–19.
Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1):66–73.
Jin, W. (2020). Research on machine learning and its algorithms and development. In Journal of Physics: Conference Series, volume 1544, page 012003. IOP Publishing.
Karaboga, D. et al. (2005). An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.
Muthiah, A. and Rajkumar, R. (2014). A comparison of artificial bee colony algorithm and genetic algorithm to minimize the makespan for job shop scheduling. Procedia Engineering, 97:1745–1754.
Narasimhan, H. (2009). Parallel artificial bee colony (pabc) algorithm. In 2009 World congress on nature & biologically inspired computing (NaBIC), pages 306–311. IEEE.
Wachter, S., Mittelstadt, B., and Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the gdpr. Harv. JL & Tech., 31:841.
Yang, F., Alva, S. S., Chen, J., and Hu, X. (2021). Model-based counterfactual synthesizer for interpretation. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pages 1964–1974.
Publicado
16/05/2024
Como Citar
FURLANETTO, Gabriel Covello; BALDASSIN, Alexandro; MANACERO, Aleardo.
CausalBioCF: Causalidade e otimização bioinspirada para geração de contrafactuais factíveis em tempo real. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DE SÃO PAULO (ERAD-SP), 15. , 2024, Rio Claro/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 77-80.
DOI: https://doi.org/10.5753/eradsp.2024.239909.
