Explicações de Inteligência Artificial Conscientes de Contexto em Aplicações de Mercado de Trabalho
Resumo
Este projeto de pesquisa desenvolve um modelo de explicabilidade sensível ao contexto para aprimorar a satisfação, confiança e compreensão dos usuários em aplicações de IA no mercado de trabalho. Para superar as limitações do SHAP e LIME, o modelo integra variáveis específicas do domínio, reduzindo assimetrias informacionais. Seguindo a abordagem de Design Science Research (DSR), a pesquisa consiste em dois experimentos sequenciais, cujos resultados fornecerão evidências empíricas sobre o impacto da contextualização na explicabilidade da IA, promovendo transparência e usabilidade.Referências
Akkasi, A. (2024). Job description parsing with explainable transformer based ensemble models to extract the technical and non-technical skills. Natural Language Processing Journal, 9:100102.
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2020). Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai. Information fusion, 58:82–115.
Bhattacharya, P., Zuhair, M., Roy, D., Prasad, V. K., and Savaliya, D. (2022). Aajeevika: Trusted explainable ai based recruitment scheme in smart organizations. In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), pages 1002–1008. IEEE.
Das, S., Chakraborty, S., Sajjan, G., Majumder, S., Dey, N., and Tavares, J. M. R. (2022). Explainable ai for predictive analytics on employee attrition. In International Conference on Soft Computing and its Engineering Applications, pages 147–157. Springer.
Das Swain, V., Gao, L., Wood, W. A., Matli, S. C., Abowd, G. D., and De Choudhury, M. (2023). Algorithmic power or punishment: Information worker perspectives on passive sensing enabled ai phenotyping of performance and wellbeing. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pages 1–17.
Daudt, F., Cinalli, D., and Garcia, A. C. B. (2021). Research on explainable artificial intelligence techniques: An user perspective. In 2021 IEEE 24th international conference on computer supported cooperative work in design (CSCWD), pages 144–149. IEEE.
Gelman, A. (2005). Analysis of variance—why it is more important than ever.
Lashkari, M. and Cheng, J. (2023). “finding the magic sauce”: Exploring perspectives of recruiters and job seekers on recruitment bias and automated tools. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pages 1–16.
Linardatos, P., Papastefanopoulos, V., and Kotsiantis, S. (2020). Explainable ai: A review of machine learning interpretability methods. Entropy, 23(1):18.
Lundberg, S. (2017). A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874.
Makanga, C., Mukwaba, D., Agaba, C. L., Murindanyi, S., Joseph, T., Hellen, N., and Marvin, G. (2024). Explainable machine learning and graph neural network approaches for predicting employee attrition. In Proceedings of the 2024 Sixteenth International Conference on Contemporary Computing, pages 243–255.
Marra, T. and Kubiak, E. (2024). Addressing diversity in hiring procedures: a generative adversarial network approach. AI and Ethics, pages 1–25.
Maurya, A., Akoglu, L., Krishnan, R., and Bay, D. (2018). A lens into employee peer reviews via sentiment-aspect modeling. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 670–677. IEEE.
Naudé, M., Adebayo, K. J., and Nanda, R. (2023). A machine learning approach to detecting fraudulent job types. AI & SOCIETY, 38(2):1013–1024.
Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of management information systems, 24(3):45–77.
Peña, A., Serna, I., Morales, A., Fierrez, J., Ortega, A., Herrarte, A., Alcantara, M., and Ortega-Garcia, J. (2023). Human-centric multimodal machine learning: Recent advances and testbed on ai-based recruitment. SN Computer Science, 4(5):434.
Pessach, D., Singer, G., Avrahami, D., Ben-Gal, H. C., Shmueli, E., and Ben-Gal, I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134:113290.
Pinto, G. B. S., Mello, C. E., and Garcia, A. C. B. Explainable ai in labor market applications.
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). ”why should i trust you?”explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144.
Sampath, K., Devi, K., Ambuli, T., and Venkatesan, S. (2024). Ai-powered employee performance evaluation systems in hr management. In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT), volume 1, pages 703–708. IEEE.
Sánchez-Monedero, J., Dencik, L., and Edwards, L. (2020). What does it mean to’solve’the problem of discrimination in hiring? social, technical and legal perspectives from the uk on automated hiring systems. In Proceedings of the 2020 conference on fairness, accountability, and transparency, pages 458–468.
Sekaran, K. and Shanmugam, S. (2022). Interpreting the factors of employee attrition using explainable ai. In 2022 International Conference on Decision Aid Sciences and Applications (DASA), pages 932–936. IEEE.
Sogancioglu, G., Kaya, H., and Salah, A. A. (2023). Using explainability for bias mitigation: A case study for fair recruitment assessment. In Proceedings of the 25th International Conference on Multimodal Interaction, pages 631–639.
Sun, Y., Ji, Y., Zhu, H., Zhuang, F., He, Q., and Xiong, H. (2024). Market-aware long-term job skill recommendation with explainable deep reinforcement learning. ACM Transactions on Information Systems.
Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., and Waters, T. (2021). Recommending the most effective intervention to improve employment for job seekers with disability. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 3616–3626.
Zhang, Q., Zhu, H., Sun, Y., Liu, H., Zhuang, F., and Xiong, H. (2021). Talent demand forecasting with attentive neural sequential model. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 3906–3916.
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2020). Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai. Information fusion, 58:82–115.
Bhattacharya, P., Zuhair, M., Roy, D., Prasad, V. K., and Savaliya, D. (2022). Aajeevika: Trusted explainable ai based recruitment scheme in smart organizations. In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), pages 1002–1008. IEEE.
Das, S., Chakraborty, S., Sajjan, G., Majumder, S., Dey, N., and Tavares, J. M. R. (2022). Explainable ai for predictive analytics on employee attrition. In International Conference on Soft Computing and its Engineering Applications, pages 147–157. Springer.
Das Swain, V., Gao, L., Wood, W. A., Matli, S. C., Abowd, G. D., and De Choudhury, M. (2023). Algorithmic power or punishment: Information worker perspectives on passive sensing enabled ai phenotyping of performance and wellbeing. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pages 1–17.
Daudt, F., Cinalli, D., and Garcia, A. C. B. (2021). Research on explainable artificial intelligence techniques: An user perspective. In 2021 IEEE 24th international conference on computer supported cooperative work in design (CSCWD), pages 144–149. IEEE.
Gelman, A. (2005). Analysis of variance—why it is more important than ever.
Lashkari, M. and Cheng, J. (2023). “finding the magic sauce”: Exploring perspectives of recruiters and job seekers on recruitment bias and automated tools. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pages 1–16.
Linardatos, P., Papastefanopoulos, V., and Kotsiantis, S. (2020). Explainable ai: A review of machine learning interpretability methods. Entropy, 23(1):18.
Lundberg, S. (2017). A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874.
Makanga, C., Mukwaba, D., Agaba, C. L., Murindanyi, S., Joseph, T., Hellen, N., and Marvin, G. (2024). Explainable machine learning and graph neural network approaches for predicting employee attrition. In Proceedings of the 2024 Sixteenth International Conference on Contemporary Computing, pages 243–255.
Marra, T. and Kubiak, E. (2024). Addressing diversity in hiring procedures: a generative adversarial network approach. AI and Ethics, pages 1–25.
Maurya, A., Akoglu, L., Krishnan, R., and Bay, D. (2018). A lens into employee peer reviews via sentiment-aspect modeling. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 670–677. IEEE.
Naudé, M., Adebayo, K. J., and Nanda, R. (2023). A machine learning approach to detecting fraudulent job types. AI & SOCIETY, 38(2):1013–1024.
Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of management information systems, 24(3):45–77.
Peña, A., Serna, I., Morales, A., Fierrez, J., Ortega, A., Herrarte, A., Alcantara, M., and Ortega-Garcia, J. (2023). Human-centric multimodal machine learning: Recent advances and testbed on ai-based recruitment. SN Computer Science, 4(5):434.
Pessach, D., Singer, G., Avrahami, D., Ben-Gal, H. C., Shmueli, E., and Ben-Gal, I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134:113290.
Pinto, G. B. S., Mello, C. E., and Garcia, A. C. B. Explainable ai in labor market applications.
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). ”why should i trust you?”explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144.
Sampath, K., Devi, K., Ambuli, T., and Venkatesan, S. (2024). Ai-powered employee performance evaluation systems in hr management. In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT), volume 1, pages 703–708. IEEE.
Sánchez-Monedero, J., Dencik, L., and Edwards, L. (2020). What does it mean to’solve’the problem of discrimination in hiring? social, technical and legal perspectives from the uk on automated hiring systems. In Proceedings of the 2020 conference on fairness, accountability, and transparency, pages 458–468.
Sekaran, K. and Shanmugam, S. (2022). Interpreting the factors of employee attrition using explainable ai. In 2022 International Conference on Decision Aid Sciences and Applications (DASA), pages 932–936. IEEE.
Sogancioglu, G., Kaya, H., and Salah, A. A. (2023). Using explainability for bias mitigation: A case study for fair recruitment assessment. In Proceedings of the 25th International Conference on Multimodal Interaction, pages 631–639.
Sun, Y., Ji, Y., Zhu, H., Zhuang, F., He, Q., and Xiong, H. (2024). Market-aware long-term job skill recommendation with explainable deep reinforcement learning. ACM Transactions on Information Systems.
Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., and Waters, T. (2021). Recommending the most effective intervention to improve employment for job seekers with disability. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 3616–3626.
Zhang, Q., Zhu, H., Sun, Y., Liu, H., Zhuang, F., and Xiong, H. (2021). Talent demand forecasting with attentive neural sequential model. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 3906–3916.
Publicado
02/06/2025
Como Citar
PINTO, Gabriel B. S.; MELLO, Carlos E. R. de; GARCIA, Ana Cristina B..
Explicações de Inteligência Artificial Conscientes de Contexto em Aplicações de Mercado de Trabalho. In: DESENHO DE PESQUISA - SIMPÓSIO BRASILEIRO DE SISTEMAS COLABORATIVOS (SBSC), 20. , 2025, Manaus/AM.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 29-34.
DOI: https://doi.org/10.5753/sbsc_estendido.2025.7036.
