arXiv subjects

Chanhou Lou

Publications and source records attributed to Chanhou Lou.

4 recordsLinked to original sources

arXiv2026

ContextLens: Modeling Imperfect Privacy and Safety Context for Legal Compliance

Individuals' concerns about data privacy and AI safety are highly contextualized and extend beyond sensitive patterns. Addressing these issues requires reasoning about the context to identify and mitigate potential risks. Though researchers have widely explored using large language models (LLMs) as evaluators for contextualized safety and privacy assessments, these efforts typically assume the availability of complete and clear context, whereas real-world contexts tend to be ambiguous and incomplete. In this paper, we propose ContextLens, a semi-rule-based framework that leverages LLMs to ground the input context in the legal domain and explicitly identify both known and unknown factors for legal compliance. Instead of directly assessing safety outcomes, our ContextLens instructs LLMs to answer a set of crafted questions that span over applicability, general principles and detailed provisions to assess compliance with pre-defined priorities and rules. We conduct extensive experiments on existing compliance benchmarks that cover the General Data Protection Regulation (GDPR) and the EU AI Act. The results suggest that our ContextLens can significantly improve LLMs' compliance assessment and surpass existing baselines without any training. Additionally, our ContextLens can further identify the ambiguous and missing factors.

cs.CL↗

arXiv2026

Representative Litigation Settlement Agreements in Artificial Intelligence Copyright Infringement Disputes: A Comparative Reflection Based on the U.S

The high-density, decentralized copyright conflicts triggered by generative AI training require more than ad hoc solutions; they demand structural governance tools. This article argues that representative litigation settlement agreements offer a distinct institutional advantage. Beyond reducing the transaction costs associated with the "tragedy of the anticommons," these agreements generate market-visible evidence, specifically pricing signals and licensing practices, that validate the "potential market" under the fourth factor of fair use. This phenomenon constitutes procedural market-making. Through a comparative analysis of the U.S. Bartz class action settlement, this study reveals a dual motivation: a surface-level drive for risk aversion and remedy locking, and a deeper logic of constructing a training-licensing market. In the context of Chinese law, the feasibility of such agreements depends not on replicating foreign models, but on establishing three interpretive mechanisms: expanding the functional definition of "same category" claims; adopting a hybrid registration/confirmation system for indeterminate class membership; and converting the "consent" requirement under Article 57, Paragraph 3 of the Civil Procedure Law into a workable opt-out right subject to judicial scrutiny.

cs.CY↗

arXiv2025

Judging Data: Critical Discourse and the Rise of Data Intellectual Property Rights in Chinese Courts

This paper uses Critical Discourse Analysis (CDA) to show how Sino-judicial activism shapes Data Intellectual Property Rights (DIPR) in China. We identify two complementary judicial discourses. Local courts (exemplified by the Zhejiang High People's Court, HCZJ) use a judicial continuation discourse that extends intellectual property norms to data disputes. The Supreme People's Court (SPC) deploys a judicial linkage discourse that aligns adjudication with state policy and administrative governance. Their interaction forms a bidirectional conceptual coupling (BCC): an inside-out projection of local reasoning and an outside-in translation of policy into doctrine. The coupling both legitimizes and constrains courts and policymakers, balancing pressure for unified market standards with safeguards against platform monopolization. Through cases such as HCZJ's Taobao v. Meijing and the SPC's Anti-Unfair Competition Interpretation, the study presents DIPR as a testbed for doctrinal innovation and institutional coordination in China's evolving digital governance.

cs.CY↗

arXiv2025

Develop-Fair Use for Artificial Intelligence: A Sino-U.S. Copyright Law Comparison Based on the Ultraman, Bartz v. Anthropic, and Kadrey v. Meta Cases

Traditional fair use can no longer respond to the challenges posed by generative AI. Drawing on a comparative analysis of China's Ultraman and the U.S. cases Bartz v. Anthropic and Kadrey v. Meta, this article proposes "Develop-Fair Use" (DFU). DFU treats AI fair use (AIFU) not as a fixed exception but as a dynamic tool of judicial balancing that shifts analysis from closed scenarios to an evaluative rule for open-ended contexts. The judicial focus moves from formal classification of facts to a substantive balancing of competition in relevant markets. Although China and the U.S. follow different paths, both reveal this logic: Ultraman, by articulating a "four-context analysis," creates institutional space for AI industry development; the debate over the fourth factor, market impact, in the two U.S. cases, especially Kadrey's "market dilution" claim, expands review from substitution in copyright markets to wider industrial competition. The core of DFU is to recognize and balance the tension in relevant markets between an emerging AI industry that invokes fair use to build its markets and a publishing industry that develops markets, including one for "training licenses," to resist fair use. The boundary of fair use is therefore not a product of pure legal deduction but a case-specific factual judgment grounded in evolving market realities. This approach aims both to trim excess copyright scope and to remedy shortfalls in market competition.

cs.CY↗