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Found 12 papers, 8 papers with code
Interpreting Emoji with Emoji
We study the extent to which emoji can be used to add interpretability to embeddings of text and emoji.
Exploring Disparity-Accuracy Trade-offs in Face Recognition Systems: The Role of Datasets, Architectures, and Loss Functions
Due to the ubiquity of applications, it is extremely important to understand the impact of the three components -- model architecture, loss function and face image dataset on the accuracy-disparity trade-off to design better, unbiased platforms.
IndMask: Inductive Explanation for Multivariate Time Series Black-Box Models
In this paper, we introduce IndMask, a framework for explaining decisions of black-box time series models.
Recommendation Fairness in Social Networks Over Time
We further study how interventions on network properties influence fairness by examining counterfactual scenarios with alternative evolution outcomes and differing network properties.
IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers
Continuous-time models such as Neural ODEs and Neural Flows have shown promising results in analyzing irregularly sampled time series frequently encountered in electronic health records.
A Review of the Role of Causality in Developing Trustworthy AI Systems
This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models.
SensePOLAR: Word sense aware interpretability for pre-trained contextual word embeddings
To address this limitation, we present SensePOLAR, an extension of the original POLAR framework that enables word-sense aware interpretability for pre-trained contextual word embeddings.
Properties of Group Fairness Metrics for Rankings
By virtue of their diverse application contexts, we argue that such a comparative analysis is not straightforward.
Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks
We hope our findings raise awareness about this issue in our community and lay a foundation for the future development of GNN models that are more robust to such attacks.
Integrated Directional Gradients: Feature Interaction Attribution for Neural NLP Models
In contrast, our formulation is inspired by axioms satisfied by characteristic functions as well as solution concepts in cooperative game theory literature.
The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings
We introduce POLAR - a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials.
StRE: Self Attentive Edit Quality Prediction in Wikipedia
Wikipedia can easily be justified as a behemoth, considering the sheer volume of content that is added or removed every minute to its several projects.
