TY - GEN
T1 - Unveiling Equity
T2 - 12th IEEE International Conference on Cloud Networking, CloudNet 2023
AU - Tang, Xuting
AU - Zhang, Mengjiao
AU - Khan, Abdul Rafae
AU - Yang, Steve Y.
AU - Xu, Jia
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - With the increasing use of big data, cloud computing, and machine learning in high-stake domains such as justice systems, financial institutions, and healthcare, concerns about fairness have become more prominent. This paper presents a novel approach to foster fair decision-making by tackling social bias and enhancing transparency in machine learning models. The proposed framework leverages quantum-inspired complex-valued neural networks and attention-based networks, offering improved transparency in modeling the decision process for interpreting feature importance and dependency. Furthermore, our approach tackles the challenges posed by imbalanced data through the incorporation of focal loss and oversampling techniques, resulting in reduced prediction errors. Through extensive experiments conducted on real-life datasets encompassing criminal charge prediction, financial fraud detection, and credit card default payment prediction, our approach consistently demonstrates reliable prediction precision and recall. Notably, our analysis of feature significance highlights the statistical importance of task-related features such as historical records of bank transactions or criminal charge history, while socially biased identifiers like race, gender, and age exhibit minimal significance. By excluding these biased features, our approach enhances fairness without compromising prediction accuracy, thereby contributing to the advancement of fair decision-making in big data and cloud computing across various high-stake domains.
AB - With the increasing use of big data, cloud computing, and machine learning in high-stake domains such as justice systems, financial institutions, and healthcare, concerns about fairness have become more prominent. This paper presents a novel approach to foster fair decision-making by tackling social bias and enhancing transparency in machine learning models. The proposed framework leverages quantum-inspired complex-valued neural networks and attention-based networks, offering improved transparency in modeling the decision process for interpreting feature importance and dependency. Furthermore, our approach tackles the challenges posed by imbalanced data through the incorporation of focal loss and oversampling techniques, resulting in reduced prediction errors. Through extensive experiments conducted on real-life datasets encompassing criminal charge prediction, financial fraud detection, and credit card default payment prediction, our approach consistently demonstrates reliable prediction precision and recall. Notably, our analysis of feature significance highlights the statistical importance of task-related features such as historical records of bank transactions or criminal charge history, while socially biased identifiers like race, gender, and age exhibit minimal significance. By excluding these biased features, our approach enhances fairness without compromising prediction accuracy, thereby contributing to the advancement of fair decision-making in big data and cloud computing across various high-stake domains.
KW - Data Analysis
KW - Fair Machine Learning in Cloud Computing
KW - Model Interpretability
UR - http://www.scopus.com/inward/record.url?scp=85191262377&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85191262377&partnerID=8YFLogxK
U2 - 10.1109/CloudNet59005.2023.10490081
DO - 10.1109/CloudNet59005.2023.10490081
M3 - Conference contribution
AN - SCOPUS:85191262377
T3 - 2023 IEEE 12th International Conference on Cloud Networking, CloudNet 2023
SP - 256
EP - 264
BT - 2023 IEEE 12th International Conference on Cloud Networking, CloudNet 2023
Y2 - 1 November 2023 through 3 November 2023
ER -