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Found 4 papers, 0 papers with code
Multi-Level Explanations for Generative Language Models
To address the challenges of text as output and long text inputs, we propose a general framework called MExGen that can be instantiated with different attribution algorithms.
Algorithmic Arbitrariness in Content Moderation
Our findings also contribute to content moderation and intermediary liability laws being discussed and passed in many countries, such as the Digital Services Act in the European Union, the Online Safety Act in the United Kingdom, and the Fake News Bill in Brazil.
Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing
Here, the sample complexity for estimating the worst-case performance gap across groups (e. g., the largest difference in error rates) increases exponentially with the number of group-denoting sensitive attributes.
Word-Level Explanations for Analyzing Bias in Text-to-Image Models
We introduce a method for computing scores for each word in the prompt; these scores represent its influence on biases in the model's output.
