81 citations · 105 across the 9 of their papers we have counts for
9 papers
Learning Concept Bottleneck Models from Mechanistic Explanations
Antonio De Santis, Schrasing Tong, Marco Brambilla +1
Concept Bottleneck Models (CBMs) aim for ante-hoc interpretability by learning a bottleneck layer that predicts interpretable concepts before the decision. State-of-the-art approac…
Mitigating Bias in Concept Bottleneck Models for Fair and Interpretable Image Classification
Schrasing Tong, Antoine Salaun, Vincent Yuan +2
Ensuring fairness in image classification prevents models from perpetuating and amplifying bias. Concept bottleneck models (CBMs) map images to high-level, human-interpretable conc…
Measuring Perceptions of Fairness in AI Systems: The Effects of Infra-marginality
Schrasing Tong, Minseok Jung, Ilaria Liccardi +1
Differences in data distributions between demographic groups, known as the problem of infra-marginality, complicate how people evaluate fairness in machine learning models. We pres…
Towards Resource Efficient and Interpretable Bias Mitigation in Large Language Models
Schrasing Tong, Eliott Zemour, Jessica Lu +2
Although large language models (LLMs) have demonstrated their effectiveness in a wide range of applications, they have also been observed to perpetuate unwanted biases present in t…
Investigating Bias in Image Classification using Model Explanations
Schrasing Tong, Lalana Kagal
We evaluated whether model explanations could efficiently detect bias in image classification by highlighting discriminating features, thereby removing the reliance on sensitive at…
Clinical Text Summarization with Syntax-Based Negation and Semantic Concept Identification
Wei-Hung Weng, Yu-An Chung, Schrasing Tong
In the era of clinical information explosion, a good strategy for clinical text summarization is helpful to improve the clinical workflow. The ideal summarization strategy can pres…