93
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Contextual knowledge graph approach to bias-reduced decision support systems

ORCID Icon & ORCID Icon
Received 06 Feb 2024, Accepted 22 Apr 2024, Published online: 05 May 2024

References

  • Abowd, G.D., & Mynatt, E.D. (2000). Charting past, present, and future research in ubiquitous computing. ACM Transactions on Computer-Human Interaction (TOCHI), 7(1), 29–58. https://doi.org/10.1145/344949.344988
  • Brézillon, P. (2005, July). Task-realization models in contextual graphs. In International and Interdisciplinary Conference on Modeling and Using Context (pp. 55–68). Springer
  • Burstein, F., Brézillon, P., & Zaslavsky, A. (Eds.). (2010). Supporting real time decision-making: The role of context in decision support on the move (Vol. 13). Springer Science & Business Media.
  • Burstein, F., Brézillon, P., & Zaslavsky, A. (2011). Introducing context into decision support on the move. Annals of Information Systems, (13), xxxiii–xxxix. https://doi.org/10.1007/978-1-4419-7406-8_3
  • Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, 5(2), 153–163. https://doi.org/10.1089/big.2016.0047
  • Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017, August). Algorithmic decision making and the cost of fairness. KDD ‘17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax NS Canada, August 13–17, 2017 (pp. 797–806).
  • Dieterich, W., Mendoza, C., & Brennan, T. (2016). COMPAS risk scales: Demonstrating accuracy equity and predictive parity. Northpointe Inc, 7(4), 1–36.
  • Feine, J., Gnewuch, U., Morana, S., & Maedche, A. (2020). Gender bias in chatbot design. In A. Folstad, T. Araujo, S. Papadopoulos, E. L. C. Law, O. C. Granmo, E. Luger, & P. B Brandtzaeg (Eds.), Chatbot Research and design: Third international workshop, conversations 2019, Amsterdam, the Netherlands, November 19–20, 2019, revised selected papers 3 (pp. 79–93). Springer International Publishing.
  • Gnanaprakash, V., Kanthimathi, N., & Saranya, N. (2021, March). Automatic number plate recognition using deep learning. IOP Conference series: materials science and engineering, Tamil Nadu, India, December 11–12, 2020 (Vol. 1084. pp. 012027). IOP Publishing.
  • Houser, K.A. (2019). Can AI solve the diversity problem in the tech industry: Mitigating noise and bias in employment decision-making. Stanford Technology Law Review, 22, 290. https://ssrn.com/abstract=3344751
  • Huang, G.L., Deng, K., & He, J. (2020). Cognitive traffic anomaly prediction from GPS trajectories using visible outlier indexes and meshed spatiotemporal neighborhoods. Cognitive Computation, 12(5), 967–978. https://doi.org/10.1007/s12559-020-09735-3
  • Huang, G.L., Deng, K., Xie, Z., & He, J. (2020). Intelligent pseudo‐location recommendation for protecting personal location privacy. Concurrency and Computation: Practice and Experience, 32(2), e5435. https://doi.org/10.1002/cpe.5435
  • Huang, G.L., He, J., Xu, Z., & Huang, G. (2020). A combination model based on transfer learning for waste classification. Concurrency and Computation: Practice and Experience, 32(19), e5751. https://doi.org/10.1002/cpe.5751
  • Huang, G.L., Zaslavsky, A., Loke, S.W., Abkenar, A., Medvedev, A., & Hassani, A. (2022). Context-aware machine learning for intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 24(1), 17–36. https://doi.org/10.1109/TITS.2022.3216462
  • Kaur, P., Kumar, Y., Ahmed, S., Alhumam, A., Singla, R., & Ijaz, M.F. (2022). Automatic license plate recognition system for vehicles using a CNN. Computers Materials & Continua, 71(1), 35–50. https://doi.org/10.32604/cmc.2022.017681
  • Liu, W.Y., Shen, C.Y., Wang, X.F., Jin, B., Lu, X.J., Wang, X.L. …& He, J.F. (2021). Survey on fairness in trustworthy machine learning Ruan Jian Xue Bao. Journal of Software, 32(5), 1404–1426.
  • Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6), 1–35. https://doi.org/10.1145/3457607
  • Pan, J.Z., Vetere, G., Gomez-Perez, J.M., & Wu, H. (Eds.). (2017). Exploiting linked data and knowledge graphs in large organisations. Springer.
  • Pujol, D., McKenna, R., Kuppam, S., Hay, M., Machanavajjhala, A., & Miklau, G. (2020, January). Fair decision making using privacy-protected data. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, (pp. 189–199).
  • Robinson, J.P., Livitz, G., Henon, Y., Qin, C., Fu, Y., & Timoner, S. (2020). Face recognition: Too bias, or not too bias? Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Glasgow, UK, August 23–28, 2020 (pp. 0–1).
  • Shashirangana, J., Padmasiri, H., Meedeniya, D., & Perera, C. (2020). Automated license plate recognition: A survey on methods and techniques. IEEE Access, 9, 11203–11225. https://doi.org/10.1109/ACCESS.2020.3047929
  • Shrestha, G. (2002). Radar charts: A tool to demonstrate gendered share of resources. Gender, Technology and Development, 6(2), 197–213. https://doi.org/10.1080/09718524.2002.11910044
  • Verma, S., & Rubin, J. (2018, May). Fairness definitions explained. Proceedings of the international workshop on software fairness, Gothenburg Sweden (pp. 1–7).
  • Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017). Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 29(12), 2724–2743. https://doi.org/10.1109/TKDE.2017.2754499
  • Weber, M., & Perona, P. (2022). Caltech cars 1999 (1.0) [data set]. CaltechDATA. https://doi.org/10.22002/D1.20084
  • Xu, R., Cui, P., Kuang, K., Li, B., Zhou, L., Shen, Z., & Cui, W. (2020, August). Algorithmic decision making with conditional fairness. KDD ‘20: 26th ACM SIGKDD international conference on knowledge discovery & data mining July 6 –10, 2020 Virtual Event CA USA (pp. 2125–2135).
  • Zhang, J., & Bareinboim, E. (2018, April). Fairness in decision-making—the causal explanation formula. Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32).