Tech Law Insights

  I. Definitions of AI Availability and Resiliency Availability: This refers to the state where AI services are accessible without delay when needed. Beyond mere server uptime, it means the Inference capabilities of the AI model must function normally. Resiliency: The ability of a system to quickly recover and normalize operations following a failure, such as server downtime, data corruption, or hacking. Complexity in AI: Unlike traditional software, AI relies on massive data pipelines and GPU resources. This creates a higher risk of a Single Point of Failure , where a bottleneck in one specific area can paralyze the entire service. II. SLA (Service Level Agreement) and Legal Liabilities Core Elements of SLA: Contracts typically include an "Uptime Guarantee" (e.g., 99.9%). If this standard is not met, the agreement often mandates providing 'Service Credits' as compensation. Liability M...

  I. Definition and Necessity of AI Algorithm Auditing 1. What is Algorithm Auditing? Algorithm auditing is an independent verification procedure to ensure that AI systems operate as intended and comply with legal and ethical standards (e.g., bias, transparency). While general Quality Assurance (QA) focuses on 'functional integrity,' auditing evaluates 'social and legal risks.' 2. Why is it Essential? Due to the 'black box' nature of AI, even developers may fail to predict discriminatory outcomes. Auditing acts as an 'Early Warning System' that detects data contamination or logic distortions before they escalate into costly legal disputes. II. Two Types of Auditing: Internal vs. External Internal Audit: Advantages: Cost-effective and allows for frequent checks during development with a deep understanding of the business context. Limitations: May lack objectivity due to internal organizational bias, often ...

  I. Definition and Five Core Principles of AI Ethics Guidelines 1. Nature and Purpose AI Ethics Guidelines are internal corporate directives that provide moral standards for AI development and use, particularly in areas where legal enforcement is not yet established. Unlike technical standards focusing on "how to build," these guidelines focus on value judgments regarding "what to do." 2. The Five Universal Principles Transparency: Disclosing AI decision-making processes and data sources. Fairness: Preventing discriminatory outcomes based on race, gender, etc. Safety: Protecting humans from system errors or malicious hacking. Accountability: Clearly identifying the entities responsible for AI outcomes. Privacy: Ensuring the right to personal data self-determination. II. Legal Efficacy and the Concept of 'Soft Law' While ethics guidelines may seem abstract, they carry significant weight in legal and...

  I. Definition and Necessity of AI Data Governance 1. Defining AI Data Governance AI Data Governance is a set of policies and procedures that ensures data availability, integrity, and security throughout the development, deployment, and operation of AI systems. Unlike traditional IT governance, it primarily integrates and prioritizes ethical and legal objectives such as fairness, bias mitigation, explainability, and clear accountability . 2. Necessity and Risk Analysis Legal Risks: Failure to establish proper governance leads to risks like GDPR/CCPA fines for violating Data Minimization (DM) principles. Furthermore, poor data quality causing AI hallucinations or wrong decisions increases Product Liability Law risks. Ethical Risks: Data bias related to race or gender, resulting in discriminatory AI decisions , leads to severe reputational damage and potential large-scale litigation. II. Framework Essentials: Organization and...

  I. Legal Definition and Types of AI Hallucinations 1. Definition and Distinction Hallucination is defined as the phenomenon where an AI, particularly a Large Language Model (LLM), generates plausible but entirely false information that is not grounded in its training data. Unlike a simple 'Error' (e.g., data input mistake) or 'Bias' (e.g., data imbalance), the model essentially 'invents' a false statement. 2. Types of Legal Risks Enterprises face specific legal risks arising from AI hallucinations: Defamation/Slander: Spreading false information about a specific individual or corporation that damages their reputation. Copyright Infringement: If the LLM generates content that closely resembles existing copyrighted material based on learned patterns. Professional Negligence (Malpractice): Relying on hallucinated results in high-stakes fields like finance, medicine, or law to deliver incorrect advice or diagnose...

  I. The Legal Foundation and Risks of Data Minimization (DM) 1. Legal Definition and Sources Data Minimization (DM) is the principle that personal data processing must be "adequate, relevant, and limited to what is necessary" in relation to the specified, explicit, and legitimate purposes for which they are processed (e.g., GDPR Article 5(1)(c) ). This principle is a core requirement in major data protection laws, including GDPR (EU) and CCPA (California/US) . 2. Risks of Non-Compliance GDPR: Violating DM can lead to severe fines, reaching up to 4% of a company's global annual turnover. CCPA: DM violations can be used as a basis for Class Action lawsuits , as the law grants a Private Right of Action to consumers. II. The Paradox: AI's Data Thirst vs. Legal Restriction The fundamental challenge posed by the DM principle to AI development is a direct conflict between legal compliance and model performance. 1. The Conflict ...