Core Concepts
The author explores the prevalence of sociodemographic bias in language models, highlighting the potential negative impacts and proposing strategies for measurement and mitigation.
Abstract
The content delves into the issue of sociodemographic bias in language models, emphasizing its harmful effects and the need for effective solutions. It provides a detailed survey of existing literature, categorizing bias research into types, quantifying bias, and debiasing techniques. The analysis reveals limitations in current approaches and offers a checklist to guide future research towards more reliable methods for addressing bias.
The paper discusses the evolution of investigations into LM bias over the past decade, tracking trends, limitations, and potential future directions. It emphasizes interdisciplinary approaches to combine works on LM bias with an understanding of potential harms. The content also highlights different methods for measuring bias such as distance-based metrics, performance-based metrics, prompt-based metrics, and probing metrics.
Furthermore, it addresses debiasing methods during finetuning and training phases to make models fairer and more accurate. The analysis points out limitations in current approaches such as reliability issues with bias metrics, overemphasis on gender bias, lack of sociotechnical understanding of bias, and superficial debiasing practices. The paper concludes by suggesting future directions focusing on intersectional bias and more effective strategies for mitigating biases.
Stats
Figure 1 shows a rise in publications related to bias in NLP over the past decade.
Table 1 displays the distribution of papers on various types of biases.
The content surveyed 273 relevant works on sociodemographic bias in NLP.
Various metrics like WEAT score, ECT, RIPA were used to quantify biases.
Different debiasing methods during finetuning and training were discussed.
Quotes
"The urgency to understand and mitigate bias in LMs is growing."
"Debiasing methods aim to make models more fair and accurate."
"A deeper exploration into the nature and consequences of LM bias is needed."