The Public's Unmet Need for Legal Services &amp; What Law Schools Can Do about It

Perlman, Andrew M.

doi:10.1162/daed_a_00537

Cited by 2 publications

(3 citation statements)

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“…Our initial experiment explored how accurately CBI decisions could be predicted based on information available to the intake staff at the time the complaint was submitted. The complaint texts 3 were normalized by removing newlines and replacing each person's name with the token PERSON using the Stanford Named Entity Extraction (NER) tool. 4 We tested two feature representations for the texts:…”

Section: Predicting Decisions From Complainant Textsmentioning

confidence: 99%

“…In many nations across the world, access to justice is increasingly elusive for the majority of citizens who are not wealthy [1] [2]. In the United States, for example, "more than 80 percent of people living below the poverty line and a majority of middle-income Americans receive no meaningful assistance when facing important civil legal issues, such as child custody, debt collection, eviction, and foreclosure" [3]. A widely-acknowledged factor in this inaccessibility is the complexity of legal rules and procedures.…”

Section: Introductionmentioning

confidence: 99%

“…https://pypi.org/project/readability/3 We appended the connection text, if any, to each complaint text 4. https://nlp.stanford.edu/software/CRF-NER.html5 The topic models were constructed using gensim (https://radimrehurek.com/gensim/about.html).K.…”

mentioning

confidence: 99%

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Judges Are from Mars, Pro Se Litigants Are from Venus: Predicting Decisions from Lay Text

Branting

Balhana

Pfeifer

et al. 2020

Frontiers in Artificial Intelligence and Applications

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Access to justice could be significantly expanded if decision support systems were able to accurately interpret statements of fact by pro se (self-represented) litigants. Prior research, which has demonstrated that case decisions can often be predicted by machine-learning models trained on judges’ statements of facts, suggests the hypothesis that these same learning algorithms could be effectively applied to pro se litigants’ fact statements. However, there has been a dearth of corpora on which to test this hypothesis. This paper describes an experiment testing the ability to predict the outcome of pro se litigants’ complaints on a corpus of 5,842 cases initiated by citizen complaints. The results of this experiment were strikingly negative, suggesting that fact statements by unguided pro se litigants are far less amenable to simple machine-learning techniques than judges’ texts and appearing to disconfirm the hypothesis above.

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Section: Predicting Decisions From Complainant Textsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%