Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text
  Processing   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 pre-trained language
models using several group-robust 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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The categories of this paper are: cond-mat
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: stat
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ex
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
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The categories of this paper are: chem-ph
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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: eess