EU Law Implications of Generative AI: Liability, Privacy, IP, and Cybersecurity
Core Concepts
Generative AI poses legal challenges in EU law regarding liability, privacy, intellectual property, and cybersecurity.
Abstract
Overview:
Generative AI like Large Language Models (LLMs) introduces unpredictability in outputs.
EU legislation like the Artificial Intelligence Act (AIA) aims to regulate Generative AI.
Liability and AI Act:
33% of firms see "liability for damage" as a top obstacle for LLM adoption.
Proposed directives address fault-based liability for AI-related damages.
Defectiveness and Fault:
AIA requirements may be challenging for LLMs due to their nature.
Compliance with fault and defectiveness criteria needs alignment with technical nuances.
Disclosure of Evidence:
AILD and PLD require evidence disclosure but lack clarity on content specifics.
Disclosure mechanisms should include detailed incident reports and data transparency.
Privacy and Data Protection:
Generative AI models face privacy concerns due to data memorization and inversion attacks.
GDPR compliance is complex due to model inversion risks and right to erasure challenges.
Automated Decision-Making:
Use of LLMs in evaluation processes may constitute automated decision-making under GDPR.
Justification for using LLMs in such scenarios must align with GDPR regulations.
Intellectual Property:
Copyright issues arise from training datasets potentially containing copyrighted materials.
Web scraping practices raise concerns about reproducing protected content without permission.
Generative AI in EU Law
Stats
33% of firms view “liability for damage” as the top external obstacle to AI adoption, especially for LLMs.
A new efficient liability regime may address these concerns by securing compensation to victims.
The PLD acknowledges that an AI system can become defective based on knowledge acquired/learned post-deployment.
Both proposals acknowledge AI’s opacity and introduce disclosure mechanisms shifting the burden of proof to providers or deployers.
Webスクレイピング手法はトレーニングデータセット関連の著作権問題に大きな影響を与えます。多くの場合、オンラインで入手可能なコンテンツはパーミッシブ・ライセンス条件(例:一部のクリエイティブ・コモンズ・ライセンス)下で再生産および再利用が許可されています。しかし、ウェブサイト所有者はウェブサイト内コンテンツ全体または一部でも知的財産権者から事前予約されていない使用禁止条項等契約条項を含めることもあります。このような状況では、LLM(Large Language Models)はウェブサイト内規約分析能力を持ち合わせており、その内容から正当化された範囲内で資料使用差し控え及び無断公開資料同定能力も有しています。
生成AIモデルが自動意思決定プロセス時候補個人権利と社会的メリットバランスするため効果ある方法として以下挙げられます。
特別目指したターク実現性: AI技術活用目指したターク実現性強調し結果アプリカント向け信頼性高まり不偏心少量透明評価フロー提供
具体欧州連合国家或制度専門規則遵守: 自動決断操作媒介LLMs展開場面中これ方針更加重要点所在
合理承諾取得難易度: 所述文言通常存在電子商務業界或信用代理店等实施主体个人求職或信用授受相互間权益平衡问题,故此应对这些场景中数据处理是否符合特殊任务需要进行证据展示
以上方式絶対だけでは解决しきれません但し,これ些戦略结合并且完善可以有效增进个别和群集隐私维护同时运转于LLMs领域效率提升帮办。(These strategies alone may not be sufficient to address the issues, but a combination and enhancement of these strategies can effectively enhance individual and group privacy protection while improving efficiency in the field of LLMs.)
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EU Law Implications of Generative AI: Liability, Privacy, IP, and Cybersecurity