Racial bias in legal language

Rice, Douglas; Rhodes, Jesse H.; Nteta, Tatishe M.

doi:10.1177/2053168019848930

Cited by 23 publications

(10 citation statements)

References 24 publications

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“…It is possible that such models could be improved using foundation models and applied to help judges draft decisions by flagging obvious mistakes in their opinion, as has been discussed in the context of adjudicative agencies Ray and Lubbers 2014]. They can also be used to identify racial biases in legal opinions and help judges revise their opinions accordingly [Rice et al 2019].…”

Section: Opportunities In Lawmentioning

confidence: 99%

On the Opportunities and Risks of Foundation Models

Bommasani¹,

Hudson²,

Adeli³

et al. 2021

Preprint

649

652

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AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles (e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities, and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

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Section: Opportunities In Lawmentioning

confidence: 99%

On the Opportunities and Risks of Foundation Models

Bommasani¹,

Hudson²,

Adeli³

et al. 2021

Preprint

649

652

View full text Add to dashboard Cite

show abstract

“…Garg et al (2018) train separate embeddings by decade using the Google Books corpus and show that gender associations track demographic and occupational shifts. The closest analysis to ours is Rice et al (2019), who detect a racial slant in a corpus of U.S. state and federal court opinions. We build on this literature to construct author-specific measures of slant that can be linked to real-world behaviors.…”

Section: Introductionmentioning

confidence: 94%

Stereotypes in High-Stakes Decisions: Evidence from U.S. Circuit Courts

Ash¹,

Chen²,

Ornaghi³

2019

AEA Randomized Controlled Trials

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“…They also found that the fairness gap was particular severe for more serious crimes. Another line of work (Rice et al, 2019;Baker Gillis, 2021;Gumusel et al, 2022) explores representational bias with respect to race and gender analyzing word latent representations trained in legal text corpora. While we agree that representational bias can potentially reinforce unfortunate biases, these may not impact the treatment of individuals (or groups).…”

Section: Related Workmentioning

confidence: 99%

FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing

Chalkidis¹,

Pasini²,

Zhang³

et al. 2022

Preprint

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We present a benchmark suite of four datasets for evaluating the fairness of pre-trained language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Switzerland, and China), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, region, language, and legal area). In our experiments, we evaluate pretrained language models using several grouprobust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.

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Racial bias in legal language

Cited by 23 publications

References 24 publications

On the Opportunities and Risks of Foundation Models

On the Opportunities and Risks of Foundation Models

Stereotypes in High-Stakes Decisions: Evidence from U.S. Circuit Courts

FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing

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