Artificial Intelligence and Robotics Commons^™

Artificial Intelligence And Procedural Due Process, Brandon L. Garrett

Faculty Scholarship

Artificial intelligence (AI) violates procedural due process rights if the government uses it to deprive people of life, liberty, and property without adequate notice or an opportunity to be heard. A wide range of government agencies deploy AI systems, including in courts, law enforcement, public benefits administration, and national security. If the government refuses to disclose the reasons why it denied a person bail, public benefits, or immigration status, serious due process concerns arise. If the government delegates such tasks to an AI system, the due process analysis does not change. One asks whether a person received adequate notice and …

The Evolving Role Of Copyright Law In The Age Of Ai-Generated Works, James Hutson

Faculty Scholarship

Objective: to identify the prospects and directions of copyright law development associated with the increasing use of generative artificial intelligence.

Methods: the study is based on the formal-legal, comparative, historical methods, doctrinal analysis, legal forecasting and modeling.

Results:the article states that the emergence of generative artificial intelligence makes one rethink the processes occurring in the field of creative activity and the traditional copyright system, which becomes inadequate to modern realities. The author substantiates the necessity of legal reassessment of copyright and emphasizes the urgent need for updated means of copyright protection. Unlike previous digital tools, which expanded …

Intellectual Property Liability For Businesses In The Age Of Ai: What New Liabilities Businesses Using Ai Could Face And The Possible Methods Of Self-Protection, Elizabeth Anne Henderson

Michigan Business & Entrepreneurial Law Review

The invention of Artificial Intelligence (“AI”) has triggered a wave of copyright and trademark litigation that will likely shape the intellectual property laws governing AI for the foreseeable future. Lawsuits against AI giants like Meta and OpenAI stand to declare popular uses of AI as actionable infringement as well as possibly reshape how copyright and trademark law view concepts, such as fair use and derivative works in the age of technology. Meanwhile, businesses are pushing forward rapidly with adopting AI and implementing its use in everyday functions. For many of these businesses, AI is a highly desirable but poorly understood …

Regulating Robo-Advisors In An Age Of Generative Artificial Intelligence, Daniel Schwarcz, Tom Baker

Law & Economics Working Papers

New generative Artificial Intelligence (AI) tools can increasingly engage in personalized, sustained and natural conversations with users. This technology has the capacity to reshape the financial services industry, making customized expert financial advice broadly available to consumers. However, AI’s ability to convincingly mimic human financial advisors also creates significant risks of large-scale financial misconduct. Which of these possibilities becomes reality will depend largely on the legal and regulatory rules governing “robo-advisors” that supply fully automated financial advice to consumers. This Article consequently critically examines this evolving regulatory landscape, arguing that current U.S. rules fail to adequately limit the risk that …

Between Copyright And Computer Science: The Law And Ethics Of Generative Ai, Devin R. Desai, Mark Riedl

Northwestern Journal of Technology and Intellectual Property

Copyright and computer science continue to intersect and clash, but they can coexist. The advent of new technologies such as digitization of visual and aural creations, sharing technologies, search engines, social media offerings, and more, challenge copyright-based industries and reopen questions about the reach of copyright law. Breakthroughs in artificial intelligence research, especially Large Language Models that leverage copyrighted material as part of training, are the latest examples of the ongoing tension between copyright and computer science. The exuberance, rush-to-market, and edge problem cases created by a few misguided companies now raises challenges to core legal doctrines and may shift …

The Unfairness Of Fair Machine Learning: Leveling Down And Strict Egalitarianism By Default, Brent Mittelstadt, Sandra Wachter, Chris Russell

Michigan Technology Law Review

In recent years, fairness in machine learning (ML), artificial intelligence (AI), and algorithmic decision-making systems has emerged as a highly active area of research and development. To date, most measures and methods to mitigate bias and improve fairness in algorithmic systems have been built in isolation from policymaking and civil societal contexts and lack serious engagement with philosophical, political, legal, and economic theories of equality and distributive justice. Many current measures define “fairness” in simple terms to mean narrowing gaps in performance or outcomes between demographic groups while preserving as much of the original system’s accuracy as possible. This oversimplified …

The Implications Of Chatgpt For Legal Services And Society, Andrew Perlman

Michigan Technology Law Review

On November 30, 2022, OpenAI released a chatbot called ChatGPT.1 To demonstrate the chatbot’s sophistication and its potential implications, both for legal services and society more generally, most of this paper was generated in about an hour through prompts within ChatGPT. Only this abstract, the preface, the outline headers, the footnotes, the epilogue, and the prompts were written by a person. ChatGPT generated the rest of the text with no human editing. To be clear, the responses generated by ChatGPT were imperfect and at times problematic, and the use of an AI tool for law-related services raises a host of …

From Biased Data Inputs To Your Discriminatory Diagnosis Outputs: A Review Of Legal Liability For Artificial Intelligence In Healthcare, Amber Bolden

Michigan Technology Law Review

While health disparities in America occur due to non-medical circumstances, certain protected classes experience healthcare disparities due to the biases of medical professionals. Biased diagnoses, both intentional or unintentional, have existed throughout the history of the medical profession. That those biases are becoming data for training algorithms raises concerns as the medical field increasingly incorporates and standardizes artificial and augmented intelligence in patient diagnosis and treatment. Currently unregulated but with lifedetermining potential, artificial intelligence (AI) when used in patient treatment leads to important questions: should the doctor, the provider, or the AI developers be liable, and for what? Section II …

Algorithmic Reason-Giving, Arbitrary And Capricious Review, And The Need For A Clear Normative Baseline, Cameron Averill

University of Cincinnati Law Review

Federal agencies have caught the artificial intelligence (AI) bug. A December 2023 report by the Government Accountability Office found that twenty of twenty-three federal agencies surveyed reported using some form of AI, with about two hundred current use cases for algorithms and about one thousand more in the planning phase. These agencies are using algorithms in all aspects of administration, including rulemaking, adjudication, and enforcement. The risks of AI are well-documented. Previous work has shown that algorithms can be, among other things, biased and prone to error. However, perhaps no problem poses a more serious threat to the use of …

Using Machine Learning To Predict State Compliance With International Legal Obligations For Registration Of Space Objects: Comparative Performance Of Logistic Regression And Dense Neural Network Models, Jonathan K. Sawmiller

Student Publications

Approximately 12% of satellites and other objects launched into outer space have not been registered with the United Nations (UN) as required by international law. To predict whether States will register a launched space object and understand what factors influence a registration decision, data from a UN online index of space objects was used to train and select the best machine learning model. After preparation, the dataset had 1938 datapoints with 11 features, with categorical features simplified and converted to binary.

Multiple variations of classical logistic regression models were compared to multiple variations of dense neural network models. The best …

The Psychological Impacts Of Algorithmic And Ai-Driven Social Media On Teenagers: A Call To Action, Sunil Arora, Sahil Arora, John Hastings

Research & Publications

This study investigates the meta-issues surrounding social media, which, while theoretically designed to enhance social interactions and improve our social lives by facilitating the sharing of personal experiences and life events, often results in adverse psychological impacts. Our investigation reveals a paradoxical outcome: rather than fostering closer relationships and improving social lives, the algorithms and structures that underlie social media platforms inadvertently contribute to a profound psychological impact on individuals, influencing them in unforeseen ways. This phenomenon is particularly pronounced among teenagers, who are disproportionately affected by curated online personas, peer pressure to present a perfect digital image, and the …

Antitrust After The Coming Wave, Daniel A. Crane

Articles

A coming wave of general-purpose technologies, including artificial intelligence ("AI"), robotics, quantum computing, synthetic biology, energy expansion, and nanotechnology, is likely to fundamentally reshape the economy and erode the assumptions on which the antitrust order is predicated. First, AI-driven systems will vastly improve firms' ability to detect (and even program) consumer preferences without the benefit of price signals, which will undermine the traditional information-producing benefit of competitive markets. Similarly, these systems will be able to determine comparative producer efficiency without relying on competitive signals. Second, AI systems will invert the salient characteristics of human managers, whose intentions are opaque but …

Can Federated Learning Solve Ai’S Data Privacy Problem?: A Legal Analysis, Warren B. Chik, Florian Gamper

Research Collection Yong Pung How School Of Law

Federated learning (FL) is a method of training AI systems on different datasets without sharing data. The promise of FL is to enable AI systems to be trained on data, including personal data, while preserving data privacy and confidentiality, and thus, inter alia, facilitate compliance with data protection legislation. FL has generated a considerable interest amongst the computer science community, yet there is a dearth of legal analysis of FL. This is a problem because the question of whether FL facilitates compliance with data protection legislation is a legal question. This article will fill this lacuna by providing a comprehensive …

Trust And Robotics: A Multi-Staged Decision-Making Approach To Robots In Community, Wenxi Zhang, Willow Wong, Mark Findlay

Research Collection Yong Pung How School Of Law

With the desired outcome of social good within the wider robotics ecosystem, trust is identified as the central adhesive of the human–robot interaction (HRI) interface. However, building trust between humans and robots involves more than improving the machine’s technical reliability or trustworthiness in function. This paper presents a holistic, community-based approach to trust-building, where trust is understood as a multifaceted and multi-staged looped relation that depends heavily on context and human perceptions. Building on past literature that identifies dispositional and learned stages of trust, our proposed decision to trust model considers more extensively the human and situational factors influencing how …

Privacy Risks And Regulatory Challenges In Smart Grids And Renewable Energy Systems, Mikołaj Rajca

internetowy Kwartalnik Antymonopolowy i Regulacyjny (internet Quarterly on Antitrust and Regulation)

Smart grid technologies are central to the global shift towards a more efficient and sustainable energy infrastructure, integrating advanced digital systems with traditional power networks. While these technologies offer significant benefits, including enhanced energy management and the seamless integration of renewable energy sources, they also introduce complex privacy challenges. The extensive data collection and real-time communication capabilities inherent in smart grids raise concerns over consumer privacy, data breaches, and cybersecurity threats. This paper critically examines these privacy risks within the context of evolving regulatory frameworks such as the GDPR, NIS2 Directive, and the forthcoming EU AI Act. The discussion emphasizes …

The Limits Of Generative Ai In Administrative Law Research, Susan Azyndar

Journal Articles

The author recounts an administrative law classroom experience using generative AI. She considers the complexities of administrative law, AI training, professional responsibility, and traditional resources.

Revolutionizing Public Safety And Criminal Justice Through Ai, Alan Saquella

Publications

Artificial Intelligence (AI) is rapidly transforming public safety, criminal justice and security by fundamentally changing how crimes are committed, investigated and prevented. As AI tools become increasingly sophisticated, law enforcement and corporate security professionals are utilizing these advancements to enhance their capabilities. However, integrating AI into these sectors also brings significant challenges, including ethical concerns, recruitment difficulties, and the surge in crime rates. This article examines the transformative impact of AI, the ongoing efforts to unify AI applications across public safety and security sectors, and expert advice on overcoming the associated challenges.

Regulating Algorithmic Harms, Sylvia Lu

Law & Economics Working Papers

In recent years, the rapid expansion of artificial intelligence (AI) innovations has led to a rise in algorithmic harms—harms emerging from AI operations that pose significant threats to civil rights and democratic values in today’s technological landscape. A facial recognition system for improving criminal detection wrongly collected sensitive personal data and flagged racial minorities as shoplifters. A risk-prediction algorithm adopted to identify patients denied medical treatment to Black individuals with poor health conditions. A social media algorithm intended to boost social engagement exacerbated addictive behavior and mental illness in teenagers. These harms are becoming increasingly ubiquitous yet often manifest in …

An Artificial Intelligence Report Card For Judicial Review, Zoe E. Niesel

Michigan Journal of Environmental & Administrative Law

The rapid advancement of technology, including artificial intelligence (AI), is creating new challenges for judicial review under the Administrative Procedure Act (APA). In late 2023, federal administrative agencies publicly disclosed over 700 use cases of AI that employ sophisticated techniques like machine learning and natural language processing. While the APA's flexible judicial review framework certainly allows agencies to utilize new technologies, the APA also requires explainability of agency decisions; thus, agencies must be able to articulate the reasoning and methodology behind AI-enabled decisions for the purpose of judicial review. This Article examines APA judicial review as it applies to agency …

Generative Artificial Intelligence: The Protection Of Personal Data And Countering False Narratives About The Person, Warren B. Chik

Research Collection Yong Pung How School Of Law

Generative artificial intelligence (“Gen AI”) has rapidly become ubiquitous on online platform services, from chatbots and virtual assistants to search engines and social media. This generated concerns over potentially harmful effects from its use in both social and professional settings, including the added threats to personal data privacy and accuracy of personal information. In this article, the author will explain how Gen AI operates and why it gives rise to these issues, examine the policy and law relating to Gen AI, both existent and anticipated, and suggest possible solutions to the problems in the form of legal and non-legal measures.

Liability For Use Of Artificial Intelligence In Medicine, Nicholson W. Price Ii, Sara Gerke, I. Glenn Cohen

Book Chapters

While artificial intelligence (AI) has substantial potential to improve medical practice, errors will certainly occur, sometimes resulting in injury. Who will be liable? Questions of liability for AI-related injury raise not only immediate concerns for potentially liable parties but also broader systemic questions about how AI will be developed and adopted. The landscape of liability is complex, involving healthcare providers and institutions and the developers of AI systems. In this chapter, we consider these three principal loci of liability. At the outset, we note a few issues that shape our analysis.

Confronting Algorithms: Conscience Catching In The Criminal Trial And Beyond, Sherman J. Clark

University of Michigan Journal of Law Reform

Using the question of how to treat algorithmic evidence under the Confrontation Clause as an entry point, I argue that the use of AI in ethically salient situations presents a risk. It may cause us to avoid confronting our own responsibility. This matters because facing up to what we do, including what we delegate, can help us grow and thrive. Bearing responsibility can help us nurture vital capacities, including forms of empathy, honesty, and dignity. In the language of ethics, these are eudaimonist virtues—traits and capacities that can help us live well and fully. We should thus find ways of …

D-Hacking, Emily Black, Talia B. Gillis, Zara Hall

Faculty Scholarship

Recent regulatory efforts, including Executive Order 14110 and the AI Bill of Rights, have focused on mitigating discrimination in AI systems through novel and traditional application of anti-discrimination laws. While these initiatives rightly emphasize fairness testing and mitigation, we argue that they pay insufficient attention to robust bias measurement and mitigation — and that without doing so, the frameworks cannot effectively achieve the goal of reducing discrimination in deployed AI models. This oversight is particularly concerning given the instability and brittleness of current algorithmic bias mitigation and fairness optimization methods, as highlighted by growing evidence in the algorithmic fairness literature. …

Ai Employment Decision-Making: Integrating The Equal Opportunity Merit Principle And Explainable Ai, Gary Kok Yew Chan

Research Collection Yong Pung How School Of Law

Artificial intelligence (AI) tools used in employment decision-making cut across the multiple stages of job advertisements, shortlisting, interviews and hiring, and actual and potential bias can arise in each of these stages. One major challenge is to mitigate AI bias and promote fairness in opaque AI systems. This paper argues that the equal opportunity merit principle is an ethical approach for fair AI employment decision-making. Further, explainable AI can mitigate the opacity problem by placing greater emphasis on enhancing the understanding of reasonable users (employing organisations) and affected persons (employees and job candidates) as to the AI output. Both the …

No Sword, No Shield, No Problem: Ai In Pro Se Section 1983 Suits, Michaela Calhoun

University of Colorado Law Review Forum

Originating during the Reconstruction era, 42 U.S.C. 1983 emerged as a legislative tool to safeguard individuals’ constitutional rights and liberties. Initially designed to combat state-sanctioned violence, its efficacy has been eroded over time by subsequent judicial and legislative action. Unfortunately, the current state of Section 1983 falls short of this envisioned role, particularly for incarcerated individuals who find themselves navigating the complexities of the federal court system as pro se litigants.

Faced with a landscape devoid of resources, incarcerated individuals struggle to realize their constitutional rights, further perpetuating their collective status as a second-class citizenry—a status imposed by their own …

Revolutionizing Access To Justice: The Role Of Ai-Powered Chatbots And Retrieval-Augmented Generation In Legal Self-Help, Ayyoub Ajmi

Faculty Works

Advancements in artificial intelligence (AI) present numerous opportunities to routinize and make the law more accessible to self-represented litigants, notably through AI chatbots employing natural language processing for conversational interactions. These chatbots exhibit legal reasoning abilities without explicit training on legal-specific datasets. However, they face challenges processing less common and more specific knowledge from their training data. Additionally, once trained, their static status makes them susceptible to knowledge obsolescence over time. This article explores the application of retrieval-augmented generation (RAG) to enhance chatbot accuracy, drawing insights from a real-world implementation developed for a court system to support self-help litigants.

Measuring Jury Perception Of Explainable Machine Learning And Demonstrative Evidence, Rachel Edie Sparks Rogers

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Subjective pattern comparison has been subject to increased scrutiny by the courts and by the general public, resulting in an increased interest in pattern comparison algorithms that provide quantitative assessments of similarity for use by forensic scientists. While these algorithms would mark an improvement over current subjective comparison methods, individuals without a statistical background may struggle with the statistical concepts and language necessary for describing algorithmic methods. If algorithms are to be used, examiners must be able to testify about their use in a way that is accessible to the jury. In a series of studies, we conduct an assessment …

Icolc Statement On Ai In Licensing, International Coalition Of Library Consortia

Copyright, Fair Use, Scholarly Communication, etc.

The International Coalition of Library Consortia (ICOLC) statement on artifical intelligence in licensing.

Generative Ai In Finance: Risks And Potential Solutions, Nydia Remolina Leon

Research Collection Yong Pung How School Of Law

Generative Artificial Intelligence captured the attention of academics, policymakers, the private sector, and some regulators in 2022 after the launch of ChatGPT and its widespread adoption worldwide. Then, during the World Economic Forum session in 2023, Microsoft Chairman and Chief Executive Officer Satya Nadella said that the ‘golden age’ of AI is underway and generative AI is set to play a big role in it. Accordingly, Microsoft has invested billions of dollars into OpenAI, the company behind the launch of ChatGPT. Additionally, Google and Meta have also created their own generative AI models. Given the multiplicity of potential use cases …

Promises And Risks Of Applying Ai Medical Imaging To Early Detection Of Cancers, And Regulation For Ai Medical Imaging, Yiyao Zhang

The Journal of Purdue Undergraduate Research

No abstract provided.