{"id":"AIBD-0991","title":"The Supreme Court of British Columbia refused to make Acciona redo its machine-learning review of about 7 million documents","summary":"In July 2025 a British Columbia court refused to make Acciona redo its TAR 1.0 review of about 7 million documents. It found that a TAR 2.0 review would have cost more than $10.6 million.","date_observed":"2025-07-07","date_basis":"source publication","date_precision":"day","benefit_status":"mixed","source_collection":null,"source_entry":null,"account":"Acciona sued the Greater Vancouver Sewerage and Drainage District after the district ended its contract for the North Shore Wastewater Treatment Plant. Under an agreed protocol, TransPerfect Legal ran two TAR 1.0 predictive coding models on about 7 million of Acciona's documents. Dentons lawyers coded 3,812 English and 2,295 Spanish documents to train them. The models reached 75% recall, with precision of 81% in English and 71% in Spanish. The district said that Acciona's production of almost 4 million documents included some 750,000 irrelevant documents, and it asked for an amended list. On 7 July 2025 Justice Elwood dismissed the application. He accepted the evidence that the models were trained to the point of diminishing returns. Based on what Deloitte billed the district for a smaller set, he found that a TAR 2.0 review would have cost Acciona more than $10.6 million. He held that a model's precision rate is not an appropriate measure of compliance. He accepted that the production holds a large number of irrelevant documents. He did not address a late concern about underproduction.","category":"benefit created","unexpected":false,"purpose":"TransPerfect Legal built the TAR 1.0 models to predict which of Acciona's documents were relevant, after training on documents that Dentons lawyers coded.","system":"TAR 1.0 predictive coding models (one English, one Spanish)","model_version":null,"developer":"TransPerfect Legal, which built and ran the models (the judgment does not name the TAR software)","deployer":"Acciona Wastewater Solutions LP, with its counsel Dentons","location":"Vancouver, British Columbia, Canada","domain":"Legal services / e-discovery","segment":null,"benefit_type":"Reduced Task Cost","work_activities":["4.A.2.a.2","4.A.1.a.1"],"taxonomy_note":null,"registry_ids":{},"ratings":[{"group":"organizational","depth":1,"reason":"The court upheld a review that needed less manual work than a TAR 2.0 review costing more than $10.6 million, while the production still held many irrelevant documents."}],"ai_role":{"status":"unassessed","statement":"The court weighed how the models were trained and their recall and precision, and it did not compare them with manual review. 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Acciona Wastewater Solutions LP v. Greater Vancouver Sewerage and Drainage District, 2025 BCSC 1256, Reasons for Judgment, 7 July 2025, paras 13-16, 24-36 and 96-103. BC Courts text, read through the Internet Archive.","title":"Acciona Wastewater Solutions LP v. Greater Vancouver Sewerage and Drainage District, 2025 BCSC 1256","url":"https://web.archive.org/web/20250806185330id_/https://www.bccourts.ca/jdb-txt/sc/25/12/2025BCSC1256.htm","date":"2025-07-07","written_by":"independent","quote":"In the circumstances, it would be a mistake in my view to reason backwards from the large number of irrelevant documents in the lists of documents to a conclusion that Acciona adopted a dilatory or casual attitude to training the TAR 1.0 models."}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["evidence"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sources[].upstream_sources":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"strategic_assessments[]":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"strategic assessments"},"summary":{"state":"value_present","value":{"kind":"record_fields","fields":{"summary":"In July 2025 a British Columbia court refused to make Acciona redo its TAR 1.0 review of about 7 million documents. It found that a TAR 2.0 review would have cost more than $10.6 million.","account":"Acciona sued the Greater Vancouver Sewerage and Drainage District after the district ended its contract for the North Shore Wastewater Treatment Plant. Under an agreed protocol, TransPerfect Legal ran two TAR 1.0 predictive coding models on about 7 million of Acciona's documents. Dentons lawyers coded 3,812 English and 2,295 Spanish documents to train them. The models reached 75% recall, with precision of 81% in English and 71% in Spanish. The district said that Acciona's production of almost 4 million documents included some 750,000 irrelevant documents, and it asked for an amended list. On 7 July 2025 Justice Elwood dismissed the application. He accepted the evidence that the models were trained to the point of diminishing returns. Based on what Deloitte billed the district for a smaller set, he found that a TAR 2.0 review would have cost Acciona more than $10.6 million. He held that a model's precision rate is not an appropriate measure of compliance. He accepted that the production holds a large number of irrelevant documents. He did not address a late concern about underproduction."}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["summary","account"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"systems[].autonomy_permissions":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].component_role":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].deployment_configuration":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].inputs_outputs":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].model_name":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].model_version":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].product_name":{"state":"value_present","value":{"kind":"record_fields","fields":{"system":"TAR 1.0 predictive coding models (one English, one Spanish)"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["system"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"systems[].provider":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"taxonomy_crosswalks[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"title":{"state":"value_present","value":{"kind":"record_fields","fields":{"title":"The Supreme Court of British Columbia refused to make Acciona redo its machine-learning review of about 7 million documents","headline":"BC court declines to make Acciona redo its *TAR 1.0* review"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["title","headline"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"version":{"state":"value_present","value":{"kind":"identifiers","identifiers":["3100f7183d7b771e426a40580f0494ed1104e87e490cdfeee0827ab98554150e"],"role":"SHA-256 of the complete public record and its assessments"},"provenance":{"kind":"curatorial","basis":"SHA-256 of the public record, its assessments, and its displayed answers and notes, excluding its own identifier and the derived coverage summary.","record_fields":[],"source_refs":[]},"review_state":"record_mapped","coverage":"complete"},"workflow":{"state":"value_present","value":{"kind":"record_fields","fields":{"account":"Acciona sued the Greater Vancouver Sewerage and Drainage District after the district ended its contract for the North Shore Wastewater Treatment Plant. Under an agreed protocol, TransPerfect Legal ran two TAR 1.0 predictive coding models on about 7 million of Acciona's documents. Dentons lawyers coded 3,812 English and 2,295 Spanish documents to train them. The models reached 75% recall, with precision of 81% in English and 71% in Spanish. The district said that Acciona's production of almost 4 million documents included some 750,000 irrelevant documents, and it asked for an amended list. On 7 July 2025 Justice Elwood dismissed the application. He accepted the evidence that the models were trained to the point of diminishing returns. Based on what Deloitte billed the district for a smaller set, he found that a TAR 2.0 review would have cost Acciona more than $10.6 million. He held that a model's precision rate is not an appropriate measure of compliance. He accepted that the production holds a large number of irrelevant documents. He did not address a late concern about underproduction.","replication":{"produced_by":["The parties agreed on nearly 500 shared search terms and on the use of TAR 1.0 or TAR 2.0.","Dentons lawyers coded 3,812 English and 2,295 Spanish documents to train the models.","TransPerfect trained the models until recall and precision could no longer be improved without diminishing returns."],"must_hold":["The document set is large and relatively rich in relevant documents.","Lawyers review by hand the documents that TAR cannot process and those flagged as possibly privileged.","The parties cooperate to cull irrelevant and duplicate documents further."],"echoes":[]}}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["account","replication"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"}},"summary":{"total":91,"addressed":91,"known":28,"reviewed":2,"reviewed_complete":2,"reviewed_partial":0,"record_mapped":26,"review_not_assessed":63,"not_assessed":0,"complete":false,"by_state":{"value_present":28,"not_reported":0,"unresolved":63,"not_assessed":0,"not_applicable":0,"withheld":0,"source_unavailable":0}}},"presentation_version":"6fc6dba735012d6ab0b8906014e8e14653fcd49b91a23ed41407aba252d56cbc"}