The Mismeasure of Man and Models
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. In many settings, making decisions involve prioritizing options due to limited resource constraints. We find that prediction-based evaluation methods, which measure bias as the average performance gap (δ) in prediction outcomes, do not reliably reflect disparities in allocation decision outcomes.
Adjectives Can Reveal Gender Biases Within NLP Models
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., 2021).