人工智能在法律发现中的应用

E-discovery is the process of identifying, collecting, reviewing, and producing electronically stored information in litigation. Predictive coding and other technology-assisted review approaches learn from reviewer-coded examples to rank likely relevant material; continuous active learning can update rankings as relevance feedback arrives. In Da Silva Moore, a federal district court approved the parties’ proposed predictive-coding protocol in that case; the order is not a universal mandate or blanket endorsement of every tool. Federal Rule of Civil Procedure 26(b)(1) frames discovery around relevance and proportionality to the needs of the action. Privilege determinations and waiver rules depend on the governing law and court orders. In Mata v. Avianca, a court sanctioned attorneys after fabricated AI-generated case citations were submitted, illustrating why legal authorities must be independently checked. Continuous active learning can iteratively use reviewer feedback to update document rankings and prioritize further review, but it does not guarantee exhaustive recall or monotonic improvement. Privilege-detection flags should identify potentially privileged material for qualified legal review under applicable rules and court orders; an algorithm does not decide privilege or waiver.