Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis

• URL: http://arxiv.org/abs/2406.03064v1
• Date: Wed, 5 Jun 2024 08:47:30 GMT
• Title: Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis
• Authors: Dacao Zhang, Kun Zhang, Le Wu, Mi Tian, Richang Hong, Meng Wang,
• Abstract summary: We design a novel Path-Specific Causal Reasoning Framework (PSCRF) to eliminate sensitive attributes of students. Extensive experiments over real-world datasets (e.g., PISA dataset) demonstrate the effectiveness of our proposed PSCRF.
• Score: 45.935488572673215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cognitive Diagnosis~(CD), which leverages students and exercise data to predict students' proficiency levels on different knowledge concepts, is one of fundamental components in Intelligent Education. Due to the scarcity of student-exercise interaction data, most existing methods focus on making the best use of available data, such as exercise content and student information~(e.g., educational context). Despite the great progress, the abuse of student sensitive information has not been paid enough attention. Due to the important position of CD in Intelligent Education, employing sensitive information when making diagnosis predictions will cause serious social issues. Moreover, data-driven neural networks are easily misled by the shortcut between input data and output prediction, exacerbating this problem. Therefore, it is crucial to eliminate the negative impact of sensitive information in CD models. In response, we argue that sensitive attributes of students can also provide useful information, and only the shortcuts directly related to the sensitive information should be eliminated from the diagnosis process. Thus, we employ causal reasoning and design a novel Path-Specific Causal Reasoning Framework (PSCRF) to achieve this goal. Specifically, we first leverage an encoder to extract features and generate embeddings for general information and sensitive information of students. Then, we design a novel attribute-oriented predictor to decouple the sensitive attributes, in which fairness-related sensitive features will be eliminated and other useful information will be retained. Finally, we designed a multi-factor constraint to ensure the performance of fairness and diagnosis performance simultaneously. Extensive experiments over real-world datasets (e.g., PISA dataset) demonstrate the effectiveness of our proposed PSCRF.

Related papers

• How are Prompts Different in Terms of Sensitivity? [50.67313477651395]
  We present a comprehensive prompt analysis based on the sensitivity of a function. We use gradient-based saliency scores to empirically demonstrate how different prompts affect the relevance of input tokens to the output. We introduce sensitivity-aware decoding which incorporates sensitivity estimation as a penalty term in the standard greedy decoding.
  arXiv  Detail & Related papers  (2023-11-13T10:52:01Z)
• Automatic Sensor-free Affect Detection: A Systematic Literature Review [0.0]
  This paper provides a comprehensive literature review on sensor-free affect detection. Despite the field's evident maturity, demonstrated by the consistent performance of the models, there is ample scope for future research. There is also a need to refine model development practices and methods.
  arXiv  Detail & Related papers  (2023-10-11T13:24:27Z)
• NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants [73.85768093666582]
  We propose an explainable geometric deep network dubbed NeuroExplainer. NeuroExplainer is used to uncover altered infant cortical development patterns associated with preterm birth.
  arXiv  Detail & Related papers  (2023-01-01T12:48:12Z)
• Differentiating Student Feedbacks for Knowledge Tracing [28.669001606806525]
  We propose a framework to reweight the contribution of different responses based on their discrimination in training. We also introduce an adaptive predictive score fusion technique to maintain accuracy on less discriminative responses.
  arXiv  Detail & Related papers  (2022-12-16T13:55:07Z)
• Do Deep Neural Networks Always Perform Better When Eating More Data? [82.6459747000664]
  We design experiments from Identically Independent Distribution(IID) and Out of Distribution(OOD) Under IID condition, the amount of information determines the effectivity of each sample, the contribution of samples and difference between classes determine the amount of class information. Under OOD condition, the cross-domain degree of samples determine the contributions, and the bias-fitting caused by irrelevant elements is a significant factor of cross-domain.
  arXiv  Detail & Related papers  (2022-05-30T15:40:33Z)
• Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets [0.7388859384645262]
  We study the problem of predicting the presence of transient noise artifacts in a gravitational wave detector. We introduce models that reduce the error rate by over 60% compared to the previous state of the art.
  arXiv  Detail & Related papers  (2022-02-27T23:41:23Z)
• Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations [21.74631969428855]
  Interpretable Knowledge Tracing (IKT) is a simple model that relies on three meaningful latent features. IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes (TAN) IKT has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems.
  arXiv  Detail & Related papers  (2021-12-15T19:05:48Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• Bounding Information Leakage in Machine Learning [26.64770573405079]
  This paper investigates fundamental bounds on information leakage. We identify and bound the success rate of the worst-case membership inference attack. We derive bounds on the mutual information between the sensitive attributes and model parameters.
  arXiv  Detail & Related papers  (2021-05-09T08:49:14Z)
• Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach [54.32266555843765]
  This paper studies a model that protects the privacy of the individuals sensitive information while also allowing it to learn non-discriminatory predictors. The method relies on the notion of differential privacy and the use of Lagrangian duality to design neural networks that can accommodate fairness constraints.
  arXiv  Detail & Related papers  (2020-09-26T10:50:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.