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Found 11 papers, 3 papers with code
Does Moral Code have a Moral Code? Probing Delphi’s Moral Philosophy
In an effort to guarantee that machine learning model outputs conform with human moral values, recent work has begun exploring the possibility of explicitly training models to learn the difference between right and wrong.
Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers
Classifiers tend to learn a false causal relationship between an over-represented concept and a label, which can result in over-reliance on the concept and compromised classification accuracy.
This Prompt is Measuring <MASK>: Evaluating Bias Evaluation in Language Models
Bias research in NLP seeks to analyse models for social biases, thus helping NLP practitioners uncover, measure, and mitigate social harms.
Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information
For a multi-class toxic language classifier, we leverage a concept-based explanation framework to calculate the sensitivity of the model to the concept of sentiment, which has been used before as a salient feature for toxic language detection.
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models
In this paper, we briefly review trends in explainability and fairness in NLP research, identify the current practices in which explainability methods are applied to detect and mitigate bias, and investigate the barriers preventing XAI methods from being used more widely in tackling fairness issues.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Does Moral Code Have a Moral Code? Probing Delphi's Moral Philosophy
In an effort to guarantee that machine learning model outputs conform with human moral values, recent work has begun exploring the possibility of explicitly training models to learn the difference between right and wrong.
Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection
We present a novel feature attribution method for explaining text classifiers, and analyze it in the context of hate speech detection.
Tensors over Semirings for Latent-Variable Weighted Logic Programs
Semiring parsing is an elegant framework for describing parsers by using semiring weighted logic programs.
Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets
Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion.
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Sentence Entailment in Compositional Distributional Semantics
In categorical compositional distributional semantics, phrase and sentence representations are functions of their grammatical structure and representations of the words therein.
Distributional Sentence Entailment Using Density Matrices
Categorical compositional distributional model of Coecke et al. (2010) suggests a way to combine grammatical composition of the formal, type logical models with the corpus based, empirical word representations of distributional semantics.
