Air contaminants can be found outside and inside our houses and automobiles. We used autos for transportation daily. A vehicle was also used by some people as an extension of their home. According to the World Health ...
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Future all-electric ships will likely use a Medium Voltage DC (MVDC) power distribution system, which has several advantages over MVAC, such as higher power density and higher reliability. As key components for power ...
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Due to its importance in studying people's thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low...
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Millimeter-wave radar has become one of the mainstream sensors applied in the field of target tracking research because of its high accuracy, low cost, and non-intrusive nature. Millimeter-wave radar can solve the use...
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To achieve high performance with FPGA-equipped heterogeneous compute systems, it is crucial to co-optimize data placement and compute scheduling to maximize data reuse and bandwidth utilization for both on- and off-ch...
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The ion clustering impact on the output performance of an Er3+-doped ZBLAN fiber laser for 790- and 980-nm pumping was investigated. 790-nm is better at low cluster levels, whereas 980-nm is better at higher levels. &...
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A new color filter array (CFA) uses nanofabricated metasurfaces to map four focusing filters to nine image pixels. This approach potentially offers higher stability, single-step fabrication, improved sensitivity, and ...
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Addressing the challenge of easy inverse kinematics solution but difficult forward kinematics solution for Delta parallel robots, a kinematic model for the Delta robot was established. By improving the fitness functio...
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The world is transitioning to utilise distributed generation (DG) to reduce social, economic and environmental effects. As DG penetration is increasing, the need of determining the associated impacts on distribution n...
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In high-stakes domains such as healthcare and hiring, the role of machine learning (ML) in decision-making raises significant fairness concerns. This work focuses on Counterfactual Fairness (CF), which posits that an ...
ISBN:
(纸本)9798331314385
In high-stakes domains such as healthcare and hiring, the role of machine learning (ML) in decision-making raises significant fairness concerns. This work focuses on Counterfactual Fairness (CF), which posits that an ML model's outcome on any individual should remain unchanged if they had belonged to a different demographic group. Previous works have proposed methods that guarantee CF. Notwithstanding, their effects on the model's predictive performance remain largely unclear. To fill this gap, we provide a theoretical study on the inherent trade-off between CF and predictive performance in a model-agnostic manner. We first propose a simple but effective method to cast an optimal but potentially unfair predictor into a fair one with minimal performance degradation. By analyzing the excess risk incurred by perfect CF, we quantify this inherent trade-off. Further analysis on our method's performance with access to only incomplete causal knowledge is also conducted. Built upon this, we propose a practical algorithm that can be applied in such scenarios. Experiments on both synthetic and semi-synthetic datasets demonstrate the validity of our analysis and methods.
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