法律科技對台灣律師及企業法務的影響及因應 – 從大語言模型談起

Abstract

本研究以「法律科技對台灣律師及企業法務的影響及因應—從大型語言模型談起」為題，旨在探討 AI 技術快速發展下，台灣法律專業面臨的制度限制、採用心理與組織轉型挑戰。隨著生成式 AI 與大型語言模型逐漸融入法律檢索、契約審閱與合規管理流程，國際法律服務市場正迎來深度自動化與平台化的趨勢。然而，本研究發現台灣的法律科技發展並未如國際一致同步推進，反而呈現局部進展與整體停滯的矛盾現象。 本研究採用半結構式訪談法，邀集十二位具有豐富實務經驗的受訪者，包括律師、企業法務與法律科技新創高階經理人，共同探討法律 AI 對法律工作的實質影響，以及法律科技在台灣落地時面臨的結構性障礙。研究結果指出，台灣法律 AI 的發展困境並非源自技術本身，而是由商業模式限制、資料與著作權爭議、專業自治與倫理規範，以及市場採用心理等多重因素共同構成。其中，以律師法第 127 條、第129條，與律師倫理規範第 12 條形成的專業邊界，使雙邊平台型、案件媒合型及面向民眾的法律 AI 服務天然受阻，導致台灣法律科技無法平台化，也無法形成國際上常見的網路外部性與規模經濟。七法（Lawsnote）案進一步反映法律資料授權與著作權界定的不確定性，使 AI 模型缺乏合法且可持續的資料來源，阻斷本土 LLM 的成長動能。再者，專業主義文化與科技接受模型所揭示的高知覺風險，使法律人對 AI 採用呈高度戒慎態度，市場規模小與律所結構傳統亦更加深導入難度。值得關注的是，本研究完成之際，立法院於 2025 年 12 月正式通過人工智慧基本法，為上述困境提供了初步的治理框架與法制方向。 基於上述發現，本研究提出三項對應人工智慧基本法的改革建議：首先，重建法律資料供應鏈與法律確定性，包括法律資料開放、機器可讀格式、文本與資料探勘 (Text and Data Mining, TDM) 合理使用明確化與資料治理框架。其次，依據人工智慧基本法，鬆綁法律服務模式與律所結構，使台灣得以逐步探索法律科技沙盒、安全港制度與跨域合作的 Alternative Business Structure (ABS) 模式，協助法律科技脫離純工具化階段。最後，強化法律專業治理與能力轉型，使律師與法務能在審慎開放的前提下，發展 AI 素養、強化人機協作能力，並重新定位法律專業在 AI 時代的核心價值。 本研究總結指出，台灣法律科技的發展挑戰並非單一層面，而是制度、資料、專業文化與市場規模等因素交織形成的結構性困境。唯有透過制度改革、資料治理、組織轉型與專業角色再定位的多方協作，台灣法律 AI 才能突破目前的停滯狀態，邁向可持續、可採用且具規模性的發展路徑。

This study, titled “Legal Technology and Its Implications for Taiwanese Lawyers and In-House Counsel: Perspectives from Large Language Models”, investigates the structural, institutional, and professional challenges that arise as AI—particularly LLM—becomes increasingly embedded in legal workflows. While generative AI has demonstrated significant potential in legal research, contract review, and compliance analysis, Taiwan’s legal technology ecosystem has not progressed in parallel with global developments. Instead, it presents a paradoxical landscape of localized innovation but systemic stagnation. Using semi-structured interviews, this research engages twelve experienced professionals—including practicing lawyers, in-house counsel, and senior legaltech startup executives—to examine how legal AI influence legal work and why legal AI faces substantial barriers to adoption in Taiwan. The findings suggest that Taiwan’s legal AI challenges stem not from technological limitations but from intertwined structural factors: business model constraints, data access and copyright uncertainty, professional autonomy and ethical regulations, and adoption psychology shaped by risk aversion and market conditions. In particular, Article 127 and Article 129 of the Attorney Regulation Act and Article 12 of the Attorneys’ Ethics Rules create rigid professional boundaries that effectively prohibit platform-based or B2C-oriented legal AI services. This institutional logic prevents the emergence of scalable legal platforms and inhibits the development of network effects typical in other jurisdictions. Additionally, the Lawsnote copyright dispute highlights the legal ambiguity surrounding the use of curated legal data for AI training, thereby constraining the growth of LLM trained on Taiwanese legal corporation. Furthermore, Professionalism Theory and the Technology Acceptance Model（TAM）explain why legal professionals tend to overemphasize perceived risks while undervaluing technology benefits, resulting in limited adoption. Taiwan’s small and fragmented legal market further compounds these structural barriers. It is noteworthy that as this study reached its conclusion, the Legislative Yuan officially passed the "Artificial Intelligence Basic Act" in December 2025, providing a preliminary regulatory framework and legal direction to address the aforementioned challenges. Based on these findings, this study puts forward three reform proposals that correspond to the framework of the Artificial Intelligence Basic Act. First, rebuilding the legal data supply chain is essential, including open access to legal texts, machine-readable formats, clarified TDM（text and data mining）rules, and robust data governance frameworks. Second, according to Artificial Intelligence Basic Act, loosening restrictions on legal service models and law firm structures would allow Taiwan to explore legaltech sandboxes, safe-harbor provisions, and cross-disciplinary Alternative Business Structures（ABS）, thereby enabling the transition from tool-based solutions to platform-oriented innovation. Third, strengthening professional governance and capability transformation is necessary for lawyers and in-house counsel to operate effectively in an AI-enabled environment. This requires cultivating AI literacy, enhancing human-AI collaboration skills, and redefining the core professional value propositions that cannot be automated. This study concludes that the advancement of legal AI in Taiwan is constrained by multifaceted structural challenges rooted in regulatory frameworks, data accessibility, professional culture, and market scale. The sustainable development of legal AI will depend on coordinated reforms across institutional governance, data infrastructure, organizational transformation, and professional identity reconstruction. Only through such integrated efforts can Taiwan move beyond incremental tool-based solutions toward scalable, adoptable, and ecosystem-driven legal AI development.