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Visual Legal Rhetoric in the Age of Generative AI and Deepfakes: Renaissance or Dark Ages?
Get PDFThe paradoxical development of visual generative AI tools, such as OpenAI’s DALL-E 3, Midjourney, and Stable Diffusion, simultaneously signals a renaissance and a potential dark age in visual rhetoric and communication. On the one hand, these tools democratize the creation of visual content, empowering attorneys and others to become artists and illustrators of their legal communications without needing to learn how to draw. These AI systems can simplify complex legal concepts, bridge language barriers, and enhance advocacy. But on the other hand, the proliferation of deepfakes presents significant challenges for visual rhetoric. Deepfakes can quickly and easily create realistic but false images, videos, and audio that exploit celebrities, distort facts, and facilitate various crimes. The negative implications of deepfakes include their association with fraud, misinformation, and emotional harm. This technological advancement undermines the credibility of genuine news photography and other highly representational media as the public struggles to distinguish real from fabricated content and begins to discount all visual media. The challenge lies in using the tools effectively while maintaining the verisimilitude and integrity of representational visual media, which traditionally relies on its status as an unembellished depiction of reality to achieve its rhetorical and communicative goals. The ethical and professional questions raised by manipulated images extend to the decision whether to edit or alter visual content to improve the communication of the message and enhance understanding, while still acknowledging the lurking risk of misleading or confusing the audience with altered or manufactured media. The article suggests best practices for using generative AI responsibly: Use Non-representational Visuals: Favor diagrams, charts, drawings, and illustrations over highly representational media to avoid the pitfalls of staged, manufactured, or altered representational imagery. Disclose Staged Images: Always inform the audience when an image has been staged or recreated to maintain transparency and trust. Provide Original and Enhanced Versions: Present the original im- age alongside any enhanced version to allow for critical examination and comparison. The article concludes by emphasizing the need for vigilance in working with manipulated visuals and detecting the possible deceptions of the works of others. Given the ease with which AI can alter images, lawyers and judges must remain aware of their biases and heuristics in assessing visual evidence, recognizing that even analog photographs and videos do not represent definitive “truths.” The advent of AI-generated visuals necessitates a reassessment of the ethical use of visual media in legal communications to preserve the power of visuals in legal rhetoric
Breaking Kayfabe
Get PDFThe membership of the Supreme Court affects how it decides cases. This maxim is well accepted among the public. But it is exceedingly rare for Supreme Court opinions to acknowledge this fact, even when it provides the best explanation for the Court’s behavior. And in the unusual instance in which Supreme Court opinions do refer to changes in the Court’s membership, it is jarring. This Article explores two questions that flow from these uncontroversial facts. First, why does it happen so rarely? Second, why does it happen at all?
To answer these questions, the Article looks to an unusual source: professional wrestling. Wrestlers have a term for the official story told to the audience, the fiction the performers maintain for the benefit of the show: kayfabe. While kayfabe was once a strict trade code of silence, nowadays just about everyone knows that wrestling is staged. Yet even today, because it is essential to the performance, wrestlers rarely “break” kayfabe—and betray the fiction—in the ring. Nevertheless, breaking kayfabe is more common than it once was, in part because performers can break kayfabe to advance their strategic goals in and out of the ring.
It is the same with judicial opinions. Judges adhere to a norm analogous to kayfabe when they refuse to explain the Court’s behavior by reference to changes in the Court’s composition. Judicial kayfabe demands that opinions explain the Court’s behavior according to legal rules and principles, even when criticizing it. This norm leads to some unusually artificial opinions that seem oblivious to the political forces that influence and constitute the Court’s membership. Kayfabe also causes readers to be jarred by Supreme Court opinions that break this norm. Although such opinions are unusual, they are most frequent in the most ideologically charged and high-profile cases.
Viewing judicial opinions through the lens of kayfabe helps explain the reasons for both the norm and its transgression. Most of the time, kayfabe promotes legitimacy and the public’s faith in the Court’s decision-making. But in the biggest cases, those benefits are outweighed by the costs that the fiction imposes. This Article therefore offers a qualified defense of breaking kayfabe, arguing that it is appropriate when the stakes are highest and alternative explanations for the Court’s behavior are weak
The Fourth Amendment\u27s Hidden Intrusion Doctrine
No full textThe Fourth Amendment’s concept of probable cause is the linchpin of legal standards governing law enforcement actions such as arrests, searches, and seizures. This article challenges the assumption that the same quantum of evidence can meet the probable cause standard regardless of whether law enforcement seek to conduct a search, to seize evidence, or to make an arrest, and regardless of the intrusiveness of such search or seizure. This paper demonstrates that the Supreme Court implicitly considers the degree of intrusion into privacy or liberty, not just the quantum or quality of evidence, when determining whether probable cause exists. In doing so, I bring to light the Supreme Court’s “hidden intrusion doctrine.”
By failing to explicitly state that degree of intrusion is a factor in the probable cause analysis, the Supreme Court injects ambiguity that has many consequences. Some lower courts and law enforcement agencies already balance the quantum or quality of evidence with the severity of intrusion, even without explicit Supreme Court guidance, but others do not. The ambiguity in the doctrine therefore fosters inconsistency and expands police discretion. Moreover, as technological advancements reshape investigative techniques, from facial recognition to digital searches, the need for a clear articulation of the probable cause standard is increasingly urgent.
This article suggests both doctrinal and policy-based proposals that would bring the Supreme Court’s intrusion doctrine out of the shadows and require deliberate consideration of the degree of intrusion in probable cause determinations. Such an approach would preserve law enforcement flexibility while safeguarding individual rights amidst evolving technological landscapes
Enhancing Network Security through Dual-Layer Log Analysis: Integrating Machine Learning Classifiers with Large Language Models for Intelligent Anomaly Detection
Get PDFThis paper presents an innovative approach to enhancing network security by integrating machine learning algorithms with fine-tuned large language models (LLMs) to provide an expert assistant querying. The proposed method utilizes machine learning for efficient preprocessing and feature extraction from log data, followed by the application of a fine-tuned LLM to analyze and interpret anomalies with greater accuracy. This dual-layer detection system is designed to improve the identification of subtle and sophisticated security threats. The research team’s extensive evaluation using real-world log datasets indicates that the combined approach increases detection rates and communicates results in an understandable manner, demonstrating its potential for improving overall network security management
Multi-Agent Translation Team (MATT): Enhancing Low-Resource Language Translation through Multi-Agent Workflow
Get PDFLike humans, large language models (LLMs) benefit from revision and refinement, especially for complex tasks requiring critical thinking. Inspired by human collaborative problem-solving, this study introduces a novel multi-agent workflow designed to enhance LLM translations from English to low-resource languages. Multi-Agent Translation Team (MATT) involves the collaboration of agents that are assigned specific roles, such as translator, evaluation coordinator, and various levels of editing, to refine the initial translation into the most desired version possible. The agents work collaboratively in an iterative loop until the translation loss meets a satisfactory threshold. It stands out from other multi-agent workflows by combining the strengths of LLMs and Google Translate (GT) to achieve higher translation quality. This approach shows promise in translating short sentences and long chunks from English to languages such as Vietnamese, Hindi, and Malayalam