Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction

Job Search, Glassdoor, and so on, (3) gender, to predict the The rapid development of technology not only provides a risk of personal insolvency. It is not hard to know by common lot of convenience to people's production and life, but also sense that unemployed employment status can be the real brings a lot of potential risks (Li et al. 2022; Chakraborty cause of an increase in personal insolvency risk among these et al. 2018; Guan et al. 2023a,b; Chu et al. 2023b), such as three predictors. Gender is also not directly related to the business risks, financial risks, medical risks, industry risks, personal insolvency risk. In addition, we also know that the credit risks, and so on. To prevent risks, a better way is to unemployed job status is more likely to increase the activity build an accurate risk prediction model before risks occur in job-hunting apps. Therefore, we can observe a correlation instead of finding a solution after the risk outbreak. Although rather than a causal relationship between the risk of personal artificial intelligence has seen tremendous recent successes in insolvency and the activity in job-hunting apps. Based on many areas (Luan and Tsai 2021; Zhu et al. 2023; Wang et al. this dataset, if we run a general prediction model, it is not 2023; Shi et al. 2023; Liu et al. 2023; Chen, Rezayi, and Li difficult to observe this result that the employment status and 2023), it is often unable to produce trustworthy results on risk the activity in job-hunting apps are relatively important features prediction tasks, mainly due to a lack of interpretability, no for the risk of personal insolvency due to the spurious insight into cause relationships, and low precision and recall.