Machine learning learns patterns from data to improve the performance of the decision-making systems through computing, and gradually affects people’s lives. However, it shows that in current research machine learning algorithms may reinforce human discrimination, and exacerbate negative impacts on unprivileged groups. To mitigate potential unfairness in machine learning classifiers, we propose a fair classification approach by quantifying the difference in the prediction distribution with the idea of correlation alignment in transfer learning, which improves fairness efficiently by minimizing the second-order statistical distance of the prediction distribution. We evaluate the validity of our approach on four real-world datasets. It demonstrates that our approach significantly mitigates bias w.r.t demographic parity, equality of opportunity, and equalized odds across different groups in a classification setting, and achieves better trade-off between accuracy and fairness than previous work. In addition, our approach can further improve fairness and mitigate the fair conflict problem in debiased networks.

Deep neural networks (DNNs) have shown powerful performance in various applications and are increasingly being used in decisionmaking systems. However, concerns about fairness in DNNs always persist. Some efficient white-box fairness testing methods about individual fairness have been proposed. Nevertheless, the development of black-box methods has stagnated, and the performance of existing methods is far behind that of white-box methods. In this paper, we propose a novel black-box individual fairness testing method called Model-Agnostic Fairness Testing (MAFT). By leveraging MAFT, practitioners can effectively identify and address discrimination in DL models, regardless of the specific algorithm or architecture employed. Our approach adopts lightweight procedures such as gradient estimation and attribute perturbation rather than non-trivial procedures like symbol execution, rendering it significantly more scalable and applicable than existing methods. We demonstrate that MAFT achieves the same effectiveness as state-of-the-art white-box methods whilst improving the applicability to large-scale networks. Compared to existing black-box approaches, our approach demonstrates distinguished performance in discovering fairness violations w.r.t effectiveness (~ 14.69×) and efficiency (~ 32.58×).

Snow depth or snow water equivalent in mountainous region is crucial for hydrology, water resources management, meteorological and climate research. Remote sensing can be used for snow depth monitoring in regional scale or global scale. However, the spaceborne remote sensing of snow depth in mountain is challenging because of the sensor sensitivity and spatial resolution problems. Recently, time series Sentinel-1 is used for snow depth retrieval in mountains, which shows encouraging accuracy. In this study, an algorithm to estimate the snow depth in mountainous regions using optical and passive microwave remote sensing observations is proposed, which can be applied in periods before and after the launch of Sentinel-1. The optical and passive microwave remote sensing observations and the Sentinel-1 derived snow depth in 2016-2021 are used to train the snow depth retrieval algorithm using the Extreme gradient boosting (XGBoost) machine learning algorithm. Validations are performed using cross validation method and independentin-situsnow depth data. The cross validation shows correlation coefficient of 0.81 and mean absolute error (MAE) of 0.17m. The correlation coefficient and MAE of predicted snow depth andin-situsnow depth in 2002-2016 are 0.61 and 0.33 m, respectively, which shows significant higher accuracy compared with AMSR-E/AMSR2 snow depth products. The site dependence of the machine learning method is also discussed. The machine learning based snow depth retrieval presented in this study can be applied to mountains globally to the optical and passive microwave remote sensing era.