Physical Sciences and Mathematics Commons^™

Health Benefits And Adverse Effects Of Kratom: A Social Media Text-Mining Approach, Abdullah Wahbeh, Mohammad A. Al-Ramahi, Omar El-Gayar, Tareq Nasralah, Ahmed El Noshokaty

All Faculty Scholarship

Background: Kratom is a substance that alters one’s mental state and is used for pain relief, mood enhancement, and opioid withdrawal, despite potential health risks. In this study, we aim to analyze the social media discourse about kratom to provide more insights about kratom’s benefits and adverse effects. Also, we aim to demonstrate how algorithmic machine learning approaches, qualitative methods, and data visualization techniques can complement each other to discern diverse reactions to kratom’s effects, thereby complementing traditional quantitative and qualitative methods. Methods: Social media data were analyzed using the latent Dirichlet allocation (LDA) algorithm, PyLDAVis, and t-distributed stochastic neighbor …

Dark Side Of Genai: A Blackbox Analysis Of X, Ahmed El Noshokaty, Tareq Nasralah, Omar El-Gayar, Mohammad A. Al-Ramahi, Abdullah Wahbeh

All Faculty Scholarship

Recent advancements in generative artificial intelligence (GenAI) have raised many fears, risks, and concerns (Kim 2023; Okey et al. 2023). To shed light on the dark side of GenAI, we collected 55,916 posts from X (formerly Twitter). Based on the content of these posts, we manually labeled a sample set with the corresponding dark side, then identified a short, comprehensive list of GenAI dark sides. Using this list, we trained the ReadMe classifier, a supervised learning algorithm on Brandwatch (“Crimson Hexagon and Brandwatch” 2020), to classify the remaining posts. Further analysis, including emotion analysis and analysis of professions and interests …

Recognition Of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, And Optical Character Recognition (Ocr) Techniques, Khalid Nahar, Izzat Alsmadi, Rabia Emhamed Al Mamlook, Ahmad Nasayreh, Hasan Gharaibeh, Ali Saeed Almuflih, Fahad Alasim

All Faculty Scholarship

Air writing is one of the essential fields that the world is turning to, which can benefit from the world of the metaverse, as well as the ease of communication between humans and machines. The research literature on air writing and its applications shows significant work in English and Chinese, while little research is conducted in other languages, such as Arabic. To fill this gap, we propose a hybrid model that combines feature extraction with deep learning models and then uses machine learning (ML) and optical character recognition (OCR) methods and applies grid and random search optimization algorithms to obtain …

U2-Net: A Very-Deep Convolutional Neural Network For Detecting Distracted Drivers, Nawaf Alsrehin, Mohit Gupta, Izzat Alsmadi, Saif Addeen Alrababah

All Faculty Scholarship

In recent years, the number of deaths and injuries resulting from traffic accidents has been increasing dramatically all over the world due to distracted drivers. Thus, a key element in developing intelligent vehicles and safe roads is monitoring driver behaviors. In this paper, we modify and extend the U-net convolutional neural network so that it provides deep layers to represent image features and yields more precise classification results. It is the basis of a very deep convolution neural network, called U2-net, to detect distracted drivers. The U2-net model has two paths (contracting and expanding) in addition to a fully-connected dense …

Self-Supervised Learning Application On Covid-19 Chest X- Ray Image Classification Using Masked Autoencoder, Xin Xing, Gongbo Liang, Chris Wang, Nathan Jacobs, Ai-Ling Lin

All Faculty Scholarship

The COVID-19 pandemic has underscored the urgent need for rapid and accurate diagnosis facilitated by artificial intelligence (AI), particularly in computer-aided diagnosis using medical imag- ing. However, this context presents two notable challenges: high diagnostic accuracy demand and limited availability of medical data for training AI models. To address these issues, we proposed the implementation of a Masked AutoEncoder (MAE), an innovative self-supervised learning approach, for classifying 2D Chest X-ray images. Our approach involved performing imaging reconstruction using a Vision Transformer (ViT) model as the feature encoder, paired with a custom-defined decoder. Additionally, we fine-tuned the pretrained ViT encoder using …

Regulating Machine Learning: The Challenge Of Heterogeneity, Cary Coglianese

All Faculty Scholarship

Machine learning, or artificial intelligence, refers to a vast array of different algorithms that are being put to highly varied uses, including in transportation, medicine, social media, marketing, and many other settings. Not only do machine-learning algorithms vary widely across their types and uses, but they are evolving constantly. Even the same algorithm can perform quite differently over time as it is fed new data. Due to the staggering heterogeneity of these algorithms, multiple regulatory agencies will be needed to regulate the use of machine learning, each within their own discrete area of specialization. Even these specialized expert agencies, though, …

Moving Toward Personalized Law, Cary Coglianese

All Faculty Scholarship

Rules operate as a tool of governance by making generalizations, thereby cutting down on government officials’ need to make individual determinations. But because they are generalizations, rules can result in inefficient or perverse outcomes due to their over- and under-inclusiveness. With the aid of advances in machine-learning algorithms, however, it is becoming increasingly possible to imagine governments shifting away from a predominant reliance on general rules and instead moving toward increased reliance on precise individual determinations—or on “personalized law,” to use the term Omri Ben-Shahar and Ariel Porat use in the title of their 2021 book. Among the various technological, …

Algorithm Vs. Algorithm, Cary Coglianese, Alicia Lai

All Faculty Scholarship

Critics raise alarm bells about governmental use of digital algorithms, charging that they are too complex, inscrutable, and prone to bias. A realistic assessment of digital algorithms, though, must acknowledge that government is already driven by algorithms of arguably greater complexity and potential for abuse: the algorithms implicit in human decision-making. The human brain operates algorithmically through complex neural networks. And when humans make collective decisions, they operate via algorithms too—those reflected in legislative, judicial, and administrative processes. Yet these human algorithms undeniably fail and are far from transparent. On an individual level, human decision-making suffers from memory limitations, fatigue, …

Antitrust By Algorithm, Cary Coglianese, Alicia Lai

All Faculty Scholarship

Technological innovation is changing private markets around the world. New advances in digital technology have created new opportunities for subtle and evasive forms of anticompetitive behavior by private firms. But some of these same technological advances could also help antitrust regulators improve their performance in detecting and responding to unlawful private conduct. We foresee that the growing digital complexity of the marketplace will necessitate that antitrust authorities increasingly rely on machine-learning algorithms to oversee market behavior. In making this transition, authorities will need to meet several key institutional challenges—building organizational capacity, avoiding legal pitfalls, and establishing public trust—to ensure successful …

From Negative To Positive Algorithm Rights, Cary Coglianese, Kat Hefter

All Faculty Scholarship

Artificial intelligence, or “AI,” is raising alarm bells. Advocates and scholars propose policies to constrain or even prohibit certain AI uses by governmental entities. These efforts to establish a negative right to be free from AI stem from an understandable motivation to protect the public from arbitrary, biased, or unjust applications of algorithms. This movement to enshrine protective rights follows a familiar pattern of suspicion that has accompanied the introduction of other technologies into governmental processes. Sometimes this initial suspicion of a new technology later transforms into widespread acceptance and even a demand for its use. In this paper, we …

Regulating New Tech: Problems, Pathways, And People, Cary Coglianese

All Faculty Scholarship

New technologies bring with them many promises, but also a series of new problems. Even though these problems are new, they are not unlike the types of problems that regulators have long addressed in other contexts. The lessons from regulation in the past can thus guide regulatory efforts today. Regulators must focus on understanding the problems they seek to address and the causal pathways that lead to these problems. Then they must undertake efforts to shape the behavior of those in industry so that private sector managers focus on their technologies’ problems and take actions to interrupt the causal pathways. …

What Is The Relationship Between Language And Thought?: Linguistic Relativity And Its Implications For Copyright, Christopher S. Yoo

All Faculty Scholarship

To date, copyright scholarship has almost completely overlooked the linguistics and cognitive psychology literature exploring the connection between language and thought. An exploration of the two major strains of this literature, known as universal grammar (associated with Noam Chomsky) and linguistic relativity (centered around the Sapir-Whorf hypothesis), offers insights into the copyrightability of constructed languages and of the type of software packages at issue in Google v. Oracle recently decided by the Supreme Court. It turns to modularity theory as the key idea unifying the analysis of both languages and software in ways that suggest that the information filtering associated …

Administrative Law In The Automated State, Cary Coglianese

All Faculty Scholarship

In the future, administrative agencies will rely increasingly on digital automation powered by machine learning algorithms. Can U.S. administrative law accommodate such a future? Not only might a highly automated state readily meet longstanding administrative law principles, but the responsible use of machine learning algorithms might perform even better than the status quo in terms of fulfilling administrative law’s core values of expert decision-making and democratic accountability. Algorithmic governance clearly promises more accurate, data-driven decisions. Moreover, due to their mathematical properties, algorithms might well prove to be more faithful agents of democratic institutions. Yet even if an automated state were …

Contracting For Algorithmic Accountability, Cary Coglianese, Erik Lampmann

All Faculty Scholarship

As local, state, and federal governments increase their reliance on artificial intelligence (AI) decision-making tools designed and operated by private contractors, so too do public concerns increase over the accountability and transparency of such AI tools. But current calls to respond to these concerns by banning governments from using AI will only deny society the benefits that prudent use of such technology can provide. In this Article, we argue that government agencies should pursue a more nuanced and effective approach to governing the governmental use of AI by structuring their procurement contracts for AI tools and services in ways that …

Deploying Machine Learning For A Sustainable Future, Cary Coglianese

All Faculty Scholarship

To meet the environmental challenges of a warming planet and an increasingly complex, high tech economy, government must become smarter about how it makes policies and deploys its limited resources. It specifically needs to build a robust capacity to analyze large volumes of environmental and economic data by using machine-learning algorithms to improve regulatory oversight, monitoring, and decision-making. Three challenges can be expected to drive the need for algorithmic environmental governance: more problems, less funding, and growing public demands. This paper explains why algorithmic governance will prove pivotal in meeting these challenges, but it also presents four likely obstacles that …

The Ai Author In Litigation, Yvette Joy Liebesman, Julie Cromer Young

All Faculty Scholarship

Many scholars have posited whether a computer possessing Artificial Intelligence (AI) could be considered an author as defined per the Copyright Act of 1976. What was once a thought experiment is now becoming reality. To date, scholarship has focused primarily been on whether an AI meets the requirements of authorship from a purely objective legal framework or whether an AI could be an author based on the doctrines of incentives, independent creation, and creativity.

However, a burden inherent in the rights and liabilities of authorship is the ability to be held liable if that author’s expressive work is infringing on …

Regulation Of Algorithmic Tools In The United States, Christopher S. Yoo, Alicia Lai

All Faculty Scholarship

Policymakers in the United States have just begun to address regulation of artificial intelligence technologies in recent years, gaining momentum through calls for additional research funding, piece-meal guidance, proposals, and legislation at all levels of government. This Article provides an overview of high-level federal initiatives for general artificial intelligence (AI) applications set forth by the U.S. president and responding agencies, early indications from the incoming Biden Administration, targeted federal initiatives for sector-specific AI applications, pending federal legislative proposals, and state and local initiatives. The regulation of the algorithmic ecosystem will continue to evolve as the United States continues to search …

Transactional Scripts In Contract Stacks, Shaanan Cohney, David A. Hoffman

All Faculty Scholarship

Deals accomplished through software persistently residing on computer networks—sometimes called smart contracts, but better termed transactional scripts—embody a potentially revolutionary contracting innovation. Ours is the first precise account in the legal literature of how such scripts are created, and when they produce errors of legal significance.

Scripts’ most celebrated use case is for transactions operating exclusively on public, permissionless, blockchains: such exchanges eliminate the need for trusted intermediaries and seem to permit parties to commit ex ante to automated performance. But public transactional scripts are costly both to develop and execute, with significant fees imposed for data storage. Worse, bugs …

Transparency And Algorithmic Governance, Cary Coglianese, David Lehr

All Faculty Scholarship

Machine-learning algorithms are improving and automating important functions in medicine, transportation, and business. Government officials have also started to take notice of the accuracy and speed that such algorithms provide, increasingly relying on them to aid with consequential public-sector functions, including tax administration, regulatory oversight, and benefits administration. Despite machine-learning algorithms’ superior predictive power over conventional analytic tools, algorithmic forecasts are difficult to understand and explain. Machine learning’s “black-box” nature has thus raised concern: Can algorithmic governance be squared with legal principles of governmental transparency? We analyze this question and conclude that machine-learning algorithms’ relative inscrutability does not pose a …

Paul Baran, Network Theory, And The Past, Present, And Future Of Internet, Christopher S. Yoo

All Faculty Scholarship

Paul Baran’s seminal 1964 article “On Distributed Communications Networks” that first proposed packet switching also advanced an underappreciated vision of network architecture: a lattice-like, distributed network, in which each node of the Internet would be homogeneous and equal in status to all other nodes. Scholars who have subsequently embraced the concept of a lattice-like network approach have largely overlooked the extent to which it is both inconsistent with network theory (associated with the work of Duncan Watts and Albert-László Barabási), which emphasizes the importance of short cuts and hubs in enabling networks to scale, and the actual way, the Internet …

Lowering Legal Barriers To Rpki Adoption, Christopher S. Yoo, David A. Wishnick

All Faculty Scholarship

Across the Internet, mistaken and malicious routing announcements impose significant costs on users and network operators. To make routing announcements more reliable and secure, Internet coordination bodies have encouraged network operators to adopt the Resource Public Key Infrastructure (“RPKI”) framework. Despite this encouragement, RPKI’s adoption rates are low, especially in North America.

This report presents the results of a year-long investigation into the hypothesis—widespread within the network operator community—that legal issues pose barriers to RPKI adoption and are one cause of the disparities between North America and other regions of the world. On the basis of interviews and analysis of …

Tactful Inattention: Erving Goffman, Privacy In The Digital Age, And The Virtue Of Averting One's Eyes, Elizabeth De Armond

All Faculty Scholarship

No abstract provided.

How Much Should We Spend To Protect Privacy?: Data Breaches And The Need For Information We Do Not Have, Richard Warner, Robert Sloan

All Faculty Scholarship

A cost/benefit approach to privacy confronts two tradeoff issues. One is making appropriate tradeoffs between privacy and many goals served by the collection, distribution, and use of information. The other is making tradeoffs between investments in preventing unauthorized access to information and the variety of other goals that also make money, time, and effort demands. Much has been written about the first tradeoff. We focus on the second. The issue is critical. Data breaches occur at the rate of over three a day, and the aggregate social cost is extremely high. The puzzle is that security experts have long explained …

Ispy: Threats To Individual And Institutional Privacy In The Digital World, Lori Andrews

All Faculty Scholarship

What type of information is collected, who is viewing it, and what law librarians can do to protect their patrons and institutions.

Harnessing Legal Complexity, Daniel Katz, J. Ruhl, M Bommarito

All Faculty Scholarship

No abstract provided.

Wireless Network Neutrality: Technological Challenges And Policy Implications, Christopher S. Yoo

All Faculty Scholarship

One key aspect of the debate over network neutrality has been whether and how network neutrality should apply to wireless networks. The existing commentary has focused on the economics of wireless network neutrality, but to date a detailed analysis of how the technical aspects of wireless networks affect the implementation of network neutrality has yet to appear in the literature. As an initial matter, bad handoffs, local congestion, and the physics of wave propagation make wireless broadband networks significantly less reliable than fixed broadband networks. These technical differences require the network to manage dropped packets and congestion in a way …

The Other Side Of Garcia:The Right Of Publicity And Copyright Preemption, Jennifer E. Rothman

All Faculty Scholarship

This essay is adapted from a talk that I gave on October 2, 2015 at Columbia Law School’s annual Kernochan Center Symposium. The all-day conference focused on Copyright Outside the Box. The essay considers the aftermath of Garcia v. Google, Inc., and the Ninth Circuit’s suggestion in that case that Garcia might have a right of publicity claim against the filmmakers, even though her copyright claim failed.

The essay provides a partial update of my prior work, Copyright Preemption and the Right of Publicity, 36 U.C. Davis L. Rev. 199 (2002), and suggests that despite numerous cases over …

Moore’S Law, Metcalfe’S Law, And The Theory Of Optimal Interoperability, Christopher S. Yoo

All Faculty Scholarship

Many observers attribute the Internet’s success to two principles: Moore’s Law and Metcalfe’s Law. These precepts are often cited to support claims that larger networks are inevitably more valuable and that costs in a digital environment always decrease. This Article offers both a systematic description of both laws and then challenges the conventional wisdom by exploring their conceptual limitations. It also explores how alternative mechanisms, such as gateways and competition, can permit the realization benefits typically attributed to Moore’s Law and Metcalfe’s Law without requiring increases in network size.

A Truly “Top Task”: Rulemaking And Its Accessibility On Agency Websites, Cary Coglianese

All Faculty Scholarship

Government websites provide an important location for public access and participation in the governmental process. However, despite a growing body of research on agency websites, researchers have so far ignored agency websites as a method of public contact over rulemaking. In this article, I report results from two systematic surveys conducted on regulatory agencies’ websites which reveal how much more agencies could do to improve public access to rulemaking. Agencies commonly succumb to pressures to organize their websites around their “top tasks”—but, regrettably, they too often define these key tasks in terms of the volume of user demand for information …

Competition Policy And The Technologies Of Information, Herbert J. Hovenkamp

All Faculty Scholarship

When we speak about information and competition policy we are usually thinking about oral or written communications that have an anticompetitive potential, and mainly in the context of collusion of exclusionary threats. These are important topics. Indeed, among the most difficult problems that competition policy has had to confront over the years is understanding communications that can be construed as either threats to exclude or as offers to collude or facilitators of collusion.

My topic here, however, is the relationship between information technologies and competition policy. Technological change can both induce and undermine the use of information to facilitate anticompetitive …