arXiv subjects

Yiyang Mei

Publications and source records attributed to Yiyang Mei.

9 recordsLinked to original sources

arXiv2026

The Disintegration of Free Speech

This Article examines the constitutional status of AI-mediated communication under the First Amendment. Social media platforms, increasingly integrated with generative AI systems, now function as core public communication infrastructures. Within this environment, AI-generated pornography and large-scale political misinformation have produced significant dignitary and democratic harms. In response, states have enacted regulations requiring platforms to remove certain content, disclose recommendation practices, or redesign moderation systems. These measures, however, collide with prevailing First Amendment doctrine. The Article argues that under existing jurisprudence, AI-generated content is protected speech, and regulations targeting platform moderation practices are likely unconstitutional. Since the 1970s, the Supreme Court has shifted from a structural concern with the free circulation of information toward a strong protection of editorial autonomy, understood as control over authorship, expressive identity, and freedom from compelled attribution. Once content moderation is characterized as editorial judgment, regulatory mandates that compel or restrict such practices presumptively violate the Free Speech Clause. The Article concludes that this doctrinal trajectory risks severing the First Amendment from its democratic foundations and calls for a reconstruction attentive to automated content production, platform infrastructure, and concentrated communicative power.

cs.CY↗

arXiv2025

The Illusory Normativity of Rights-Based AI Regulation

Whether and how to regulate AI is now a central question of governance. Across academic, policy, and international legal circles, the European Union is widely treated as the normative leader in this space. Its regulatory framework, anchored in the General Data Protection Regulation, the Digital Services and Markets Acts, and the AI Act, is often portrayed as a principled model grounded in fundamental rights. This Article challenges that assumption. We argue that the rights-based narrative surrounding EU AI regulation mischaracterizes the logic of its institutional design. While rights language pervades EU legal instruments, its function is managerial, not foundational. These rights operate as tools of administrative ordering, used to mitigate technological disruption, manage geopolitical risk, and preserve systemic balance, rather than as expressions of moral autonomy or democratic consent. Drawing on comparative institutional analysis, we situate EU AI governance within a longer tradition of legal ordering shaped by the need to coordinate power across fragmented jurisdictions. We contrast this approach with the American model, which reflects a different regulatory logic rooted in decentralized authority, sectoral pluralism, and a constitutional preference for innovation and individual autonomy. Through case studies in five key domains -- data privacy, cybersecurity, healthcare, labor, and disinformation -- we show that EU regulation is not meaningfully rights-driven, as is often claimed. It is instead structured around the containment of institutional risk. Our aim is not to endorse the American model but to reject the presumption that the EU approach reflects a normative ideal that other nations should uncritically adopt. The EU model is best understood as a historically contingent response to its own political conditions, not a template for others to blindly follow.

cs.CY↗

arXiv2025

Reclaiming Constitutional Authority of Algorithmic Power

Whether and how to govern AI is no longer a question of technical regulation. It is a question of constitutional authority. Across jurisdictions, algorithmic systems now perform functions once reserved to public institutions: allocating welfare, determining legal status, mediating access to housing, employment, and healthcare. These are not merely administrative operations. They are acts of rule. Yet the dominant models of AI governance fail to confront this reality. The European approach centers on rights-based oversight, presenting its regulatory framework as a principled defense of human dignity. The American model relies on decentralized experimentation, treating fragmentation as a proxy for democratic legitimacy. Both, in different ways, evade the structural question: who authorizes algorithmic power, through what institutions, and on what terms. This Article offers an alternative. Drawing from early modern Reformed political thought, it reconstructs a constitutional framework grounded in covenantal authority and the right of lawful resistance. It argues that algorithmic governance must rest on three principles. First, that all public power must be lawfully delegated through participatory authorization. Second, that authority must be structured across representative communities with the standing to consent, contest, or refuse. Third, that individuals retain a constitutional right to resist systems that impose orthodoxy or erode the domain of conscience. These principles are then operationalized through doctrinal analysis of federalism, nondelegation, compelled speech, and structural accountability. On this view, the legitimacy of algorithmic governance turns not on procedural safeguards or policy design, but on whether it reflects a constitutional order in which power is authorized by the governed, constrained by law, and answerable to those it affects.

cs.CY↗

arXiv2025

Don't Kill the Baby: The Case for AI in Arbitration

Since the introduction of Generative AI (GenAI) in 2022, its ability to simulate human intelligence and generate content has sparked both enthusiasm and concern. While much criticism focuses on AI's potential to perpetuate bias, create emotional dissonance, displace jobs, and raise ethical questions, these concerns often overlook the practical benefits of AI, particularly in legal contexts. This article examines the integration of AI into arbitration, arguing that the Federal Arbitration Act (FAA) allows parties to contractually choose AI-driven arbitration, despite traditional reservations. The article makes three key contributions: (1) It shifts the focus from debates over AI's personhood to the practical aspects of incorporating AI into arbitration, asserting that AI can effectively serve as an arbitrator if both parties agree; (2) It positions arbitration as an ideal starting point for broader AI adoption in the legal field, given its flexibility and the autonomy it grants parties to define their standards of fairness; and (3) It outlines future research directions, emphasizing the importance of empirically comparing AI and human arbitration, which could lead to the development of distinct systems. By advocating for the use of AI in arbitration, this article underscores the importance of respecting contractual autonomy and creating an environment that allows AI's potential to be fully realized. Drawing on the insights of Judge Richard Posner, the article argues that the ethical obligations of AI in arbitration should be understood within the context of its technological strengths and the voluntary nature of arbitration agreements. Ultimately, it calls for a balanced, open-minded approach to AI in arbitration, recognizing its potential to enhance the efficiency, fairness, and flexibility of dispute resolution

cs.AI↗

arXiv2024

"ChatGPT, Don't Tell Me What to Do": Designing AI for Context Analysis in Humanitarian Frontline Negotiations

Frontline humanitarian negotiators are increasingly exploring ways to use AI tools in their workflows. However, current AI-tools in negotiation primarily focus on outcomes, neglecting crucial aspects of the negotiation process. Through iterative co-design with experienced frontline negotiators (n=32), we found that flexible tools that enable contextualizing cases and exploring options (with associated risks) are more effective than those providing direct recommendations of negotiation strategies. Surprisingly, negotiators demonstrated tolerance for occasional hallucinations and biases of AI. Our findings suggest that the design of AI-assisted negotiation tools should build on practitioners' existing practices, such as weighing different compromises and validating information with peers. This approach leverages negotiators' expertise while enhancing their decision-making capabilities. We call for technologists to learn from and collaborate closely with frontline negotiators, applying these insights to future AI designs and jointly developing professional guidelines for AI use in humanitarian negotiations.

cs.HC↗

arXiv2024

AffirmativeAI: Towards LGBTQ+ Friendly Audit Frameworks for Large Language Models

LGBTQ+ community face disproportionate mental health challenges, including higher rates of depression, anxiety, and suicidal ideation. Research has shown that LGBTQ+ people have been using large language model-based chatbots, such as ChatGPT, for their mental health needs. Despite the potential for immediate support and anonymity these chatbots offer, concerns regarding their capacity to provide empathetic, accurate, and affirming responses remain. In response to these challenges, we propose a framework for evaluating the affirmativeness of LLMs based on principles of affirmative therapy, emphasizing the need for attitudes, knowledge, and actions that support and validate LGBTQ+ experiences. We propose a combination of qualitative and quantitative analyses, hoping to establish benchmarks for "Affirmative AI," ensuring that LLM-based chatbots can provide safe, supportive, and effective mental health support to LGBTQ+ individuals. We benchmark LLM affirmativeness not as a mental health solution for LGBTQ+ individuals or to claim it resolves their mental health issues, as we highlight the need to consider complex discrimination in the LGBTQ+ community when designing technological aids. Our goal is to evaluate LLMs for LGBTQ+ mental health support since many in the community already use them, aiming to identify potential harms of using general-purpose LLMs in this context.

cs.HC↗

arXiv2024

Prompting the E-Brushes: Users as Authors in Generative AI

Since its introduction in 2022, Generative AI has significantly impacted the art world, from winning state art fairs to creating complex videos from simple prompts. Amid this renaissance, a pivotal issue emerges: should users of Generative AI be recognized as authors eligible for copyright protection? The Copyright Office, in its March 2023 Guidance, argues against this notion. By comparing the prompts to clients' instructions for commissioned art, the Office denies users authorship due to their limited role in the creative process. This Article challenges this viewpoint and advocates for the recognition of Generative AI users who incorporate these tools into their creative endeavors. It argues that the current policy fails to consider the intricate and dynamic interaction between Generative AI users and the models, where users actively influence the output through a process of adjustment, refinement, selection, and arrangement. Rather than dismissing the contributions generated by AI, this Article suggests a simplified and streamlined registration process that acknowledges the role of AI in creation. This approach not only aligns with the constitutional goal of promoting the progress of science and useful arts but also encourages public engagement in the creative process, which contributes to the pool of training data for AI. Moreover, it advocates for a flexible framework that evolves alongside technological advancements while ensuring safety and public interest. In conclusion, by examining text-to-image generators and addressing misconceptions about Generative AI and user interaction, this Article calls for a regulatory framework that adapts to technological developments and safeguards public interests

cs.CY↗

arXiv2024

Evaluating the Experience of LGBTQ+ People Using Large Language Model Based Chatbots for Mental Health Support

LGBTQ+ individuals are increasingly turning to chatbots powered by large language models (LLMs) to meet their mental health needs. However, little research has explored whether these chatbots can adequately and safely provide tailored support for this demographic. We interviewed 18 LGBTQ+ and 13 non-LGBTQ+ participants about their experiences with LLM-based chatbots for mental health needs. LGBTQ+ participants relied on these chatbots for mental health support, likely due to an absence of support in real life. Notably, while LLMs offer prompt support, they frequently fall short in grasping the nuances of LGBTQ-specific challenges. Although fine-tuning LLMs to address LGBTQ+ needs can be a step in the right direction, it isn't the panacea. The deeper issue is entrenched in societal discrimination. Consequently, we call on future researchers and designers to look beyond mere technical refinements and advocate for holistic strategies that confront and counteract the societal biases burdening the LGBTQ+ community.

cs.HC↗

arXiv2023

Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Support

Conversational agents powered by large language models (LLM) have increasingly been utilized in the realm of mental well-being support. However, the implications and outcomes associated with their usage in such a critical field remain somewhat ambiguous and unexplored. We conducted a qualitative analysis of 120 posts, encompassing 2917 user comments, drawn from the most popular subreddit focused on mental health support applications powered by large language models (u/Replika). This exploration aimed to shed light on the advantages and potential pitfalls associated with the integration of these sophisticated models in conversational agents intended for mental health support. We found the app (Replika) beneficial in offering on-demand, non-judgmental support, boosting user confidence, and aiding self-discovery. Yet, it faced challenges in filtering harmful content, sustaining consistent communication, remembering new information, and mitigating users' overdependence. The stigma attached further risked isolating users socially. We strongly assert that future researchers and designers must thoroughly evaluate the appropriateness of employing LLMs for mental well-being support, ensuring their responsible and effective application.

cs.HC↗