{"id":"https://openalex.org/W7123499358","doi":"https://doi.org/10.1145/3769126.3769239","title":"Using Interpretability to Uncover Legal Petition Structures and Optimize Text Classification","display_name":"Using Interpretability to Uncover Legal Petition Structures and Optimize Text Classification","publication_year":2025,"publication_date":"2025-06-16","ids":{"openalex":"https://openalex.org/W7123499358","doi":"https://doi.org/10.1145/3769126.3769239"},"language":null,"primary_location":{"id":"doi:10.1145/3769126.3769239","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769126.3769239","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twentieth International Conference on Artificial Intelligence and Law","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3769126.3769239","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122948231","display_name":"Vitor Almeida","orcid":null},"institutions":[{"id":"https://openalex.org/I3125581668","display_name":"Universidade de Fortaleza","ror":"https://ror.org/02ynbzc81","country_code":"BR","type":"education","lineage":["https://openalex.org/I3125581668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Vitor Almeida","raw_affiliation_strings":["Universidade de Fortaleza, Fortaleza, Cear\u00e1, Brazil"],"raw_orcid":"https://orcid.org/0009-0005-3606-2304","affiliations":[{"raw_affiliation_string":"Universidade de Fortaleza, Fortaleza, Cear\u00e1, Brazil","institution_ids":["https://openalex.org/I3125581668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024097067","display_name":"Rilder S. Pires","orcid":"https://orcid.org/0000-0003-4873-5308"},"institutions":[{"id":"https://openalex.org/I3125581668","display_name":"Universidade de Fortaleza","ror":"https://ror.org/02ynbzc81","country_code":"BR","type":"education","lineage":["https://openalex.org/I3125581668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rilder S. Pires","raw_affiliation_strings":["Universidade de Fortaleza, Fortaleza, Cear\u00e1, Brazil"],"raw_orcid":"https://orcid.org/0000-0003-4873-5308","affiliations":[{"raw_affiliation_string":"Universidade de Fortaleza, Fortaleza, Cear\u00e1, Brazil","institution_ids":["https://openalex.org/I3125581668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122970623","display_name":"Jo\u00e3o A. Monteiro Neto","orcid":null},"institutions":[{"id":"https://openalex.org/I3125581668","display_name":"Universidade de Fortaleza","ror":"https://ror.org/02ynbzc81","country_code":"BR","type":"education","lineage":["https://openalex.org/I3125581668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jo\u00e3o A. Monteiro Neto","raw_affiliation_strings":["Universidade de Fortaleza, Fortaleza, Cear\u00e1, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-0690-2449","affiliations":[{"raw_affiliation_string":"Universidade de Fortaleza, Fortaleza, Cear\u00e1, Brazil","institution_ids":["https://openalex.org/I3125581668"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080437218","display_name":"Vasco Furtado","orcid":"https://orcid.org/0000-0001-8721-4308"},"institutions":[{"id":"https://openalex.org/I3125581668","display_name":"Universidade de Fortaleza","ror":"https://ror.org/02ynbzc81","country_code":"BR","type":"education","lineage":["https://openalex.org/I3125581668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Vasco Furtado","raw_affiliation_strings":["Universidade de Fortaleza, Fortaleza, Cear\u00e1, Brazil"],"raw_orcid":"https://orcid.org/0000-0001-8721-4308","affiliations":[{"raw_affiliation_string":"Universidade de Fortaleza, Fortaleza, Cear\u00e1, Brazil","institution_ids":["https://openalex.org/I3125581668"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3125581668"],"apc_list":null,"apc_paid":null,"fwci":6.2655,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.97547596,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"150","last_page":"158"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13643","display_name":"Artificial Intelligence in Law","score":0.4675999879837036,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13643","display_name":"Artificial Intelligence in Law","score":0.4675999879837036,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.09369999915361404,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T14013","display_name":"Legal Language and Interpretation","score":0.04560000076889992,"subfield":{"id":"https://openalex.org/subfields/3308","display_name":"Law"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9815999865531921},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.7045999765396118},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.6288999915122986},{"id":"https://openalex.org/keywords/economic-justice","display_name":"Economic Justice","score":0.3952000141143799},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3440999984741211}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9815999865531921},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.7045999765396118},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.699400007724762},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.6288999915122986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5630000233650208},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4453999996185303},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.426800012588501},{"id":"https://openalex.org/C139621336","wikidata":"https://www.wikidata.org/wiki/Q3190382","display_name":"Economic Justice","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3628000020980835},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.311599999666214},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2874000072479248},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.2680000066757202}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3769126.3769239","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769126.3769239","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twentieth International Conference on Artificial Intelligence and Law","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3769126.3769239","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769126.3769239","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twentieth International Conference on Artificial Intelligence and Law","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.41343531012535095}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1601324317","https://openalex.org/W2077504940","https://openalex.org/W2251411520","https://openalex.org/W2898171081","https://openalex.org/W2963341956","https://openalex.org/W2979826702","https://openalex.org/W3091147850","https://openalex.org/W4402670856","https://openalex.org/W4402683925"],"related_works":[],"abstract_inverted_index":{"We":[0,43,196],"propose":[1],"a":[2,10,52,67,76,155],"methodology":[3],"that":[4,64,129,176,199,205],"combines":[5],"Integrated":[6],"Gradients":[7],"interpretability":[8,178],"with":[9],"Large":[11],"Language":[12],"Model":[13],"to":[14,19,29,191,209],"provide":[15],"an":[16],"analytical":[17],"framework":[18],"understand":[20,192],"the":[21,32,45,85,99,102,110,117,122,127,131,136,143,146,149,162,166,171,177,182,189,193,200],"structure":[22],"of":[23,35,47,56,87,94,145],"legal":[24],"documents,":[25],"as":[26,28],"well":[27],"assess":[30],"how":[31],"model\u2019s":[33],"choice":[34],"context":[36,69],"window":[37,70,78],"impacts":[38],"performance":[39],"in":[40,81,98,116,126,134],"topics":[41],"classification.":[42,210],"examine":[44],"classification":[46],"300,000":[48],"initial":[49],"petitions":[50],"from":[51],"large":[53],"Brazilian":[54],"Court":[55],"Justice":[57],"into":[58],"307":[59],"classes.":[60],"Our":[61],"results":[62],"suggest":[63],"models":[65],"using":[66,75],"longer":[68],"(4096":[71],"tokens)":[72,80],"outperform":[73],"those":[74],"shorter":[77],"(512":[79],"overall":[82],"performance,":[83],"although":[84],"extent":[86],"this":[88],"improvement":[89],"varies":[90],"across":[91],"different":[92],"branches":[93],"law.":[95],"For":[96],"example,":[97],"\u201cHealth":[100],"Law\u201d,":[101],"accuracy":[103,172],"improved":[104],"by":[105,188],"5.44":[106],"percentage":[107],"points,":[108],"while":[109],"increase":[111],"was":[112],"only":[113],"0.48":[114],"points":[115],"\u201cCivil":[118],"Procedure":[119],"Law\u201d":[120],"and":[121,170],"\u201cLabor":[123],"Law\u201d.":[124],"Moreover,":[125],"documents":[128],"showed":[130],"greatest":[132],"gains":[133],"accuracy,":[135],"most":[137,183],"relevant":[138,184],"parts":[139],"usually":[140],"appeared":[141],"near":[142],"end":[144],"text,":[147],"unlike":[148],"other":[150],"documents.":[151],"Consequently,":[152],"we":[153],"identified":[154],"negative":[156],"correlation":[157],"(\u03c1":[158],"=":[159],"\u22120.68)":[160],"between":[161],"importance":[163],"placed":[164],"on":[165],"first":[167],"512":[168],"tokens":[169],"gain.":[173],"This":[174],"shows":[175],"method":[179,201],"can":[180,202],"highlight":[181],"text":[185],"segments":[186],"used":[187],"model":[190],"main":[194],"topic.":[195],"also":[197],"show":[198],"identify":[203],"subtopics":[204],"pose":[206],"greater":[207],"challenges":[208]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-01-14T00:00:00"}