Evaluating Allocational Harms in Large Language Models Blog post written by Hannah Chen
Our work considers allocational harms that arise when model predictions are used to distribute scarce resources or opportunities.
Current Bias Metrics Do Not Reliably Reflect Allocation Disparities Several methods have been proposed to audit large language models (LLMs) for bias when used in critical decision-making, such as resume screening for hiring. Yet, these methods focus on predictions, without considering how the predictions are used to make decisions.
MIMIR logo. Image credit: GPT-4 + DALL-E Paper Code Data Membership inference attacks (MIAs) attempt to predict whether a particular datapoint is a member of a target model’s training data. Despite extensive research on traditional machine learning models, there has been limited work studying MIA on the pre-training data of large language models (LLMs).
We perform a large-scale evaluation of MIAs over a suite of language models (LMs) trained on the Pile, ranging from 160M to 12B parameters.
Post by Jason Briegel and Hannah Chen
Because NLP models are trained with human corpora (and now, increasingly on text generated by other NLP models that were originally trained on human language), they are prone to inheriting common human stereotypes and biases. This is problematic, because with their growing prominence they may further propagate these stereotypes (Sun et al., 2019). For example, interest is growing in mitigating bias in the field of machine translation, where systems such as Google translate were observed to default to translating gender-neutral pronouns as male pronouns, even with feminine cues (Savoldi et al.