AI Governance

Artificial Intelligence Liability

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Artificial Intelligence Liability

By Susan-Caitlyn Seavey

1. Who is Responsible for Harm flowing from AI?   

Most people can easily recognize the immense impact technological developments have had in the recent decade, affecting practically every sector. While the laws and regulations governing our society have somewhat lagged behind these technological advances, we have still managed to create a framework that seems to effectively govern these modern tools. With the implementation and widespread usage of AI, our current legal and regulatory parameters do not neatly fit anymore. We are left with questions about who is ultimately responsible for harms that stem from AI. The issue of liability does not likely have a one size fits all solution, and our government and courts are working to understand and produce the new standards and guidelines AI requires. Stanford Law Fellow, Thomas Weber, says it well: “Generative AI is developing at a stunning speed, creating new and thorny problems in well-established legal areas, disrupting long-standing regimes of civil liability—and outpacing the necessary frameworks, both legal and regulatory, that can ensure the risks are anticipated and accounted for.”[1] Until there is substantial court precedent and more promulgated AI laws, scholars and professionals are limited to discussing different theories of liability that may be suitable for AI, such as strict liability and negligence law.

            In 2023, a man in Belgium ended his life after apparently becoming emotionally dependent on an AI-powered chatbot, leaving behind his wife and two children.[2] Also in 2023, Stanford’s Director of Law, Science and Technology, Professor Lemley, asked chatbot GPT-4 to provide information about himself.[3]> The algorithm offered defamatory information, believing Professor Lemley’s research to actually be a misappropriation of trade secrets.[4] In both of these cases, it is unclear who would and/or could be held liable for the death of the father and for the defamatory information. Traditional liability is long-established with laws and regulations in place and ample case law to support the structure we have created for it. However, AI transcends many of the boxes we have fit other technology into, including the liability framework.

For Professor Lemley to establish the requisite elements of a defamation claim, he would have to prove the bad actor’s intent to defame; the standard requires that a reasonable person should have known that the information was false or exhibited a reckless disregard as to the truth or falsity of the published statement.[5] But how does one show that a robot possesses such requisite intent? It would follow that liability may fall to the developers if intent cannot be apportioned to the AI technology at issue. The apparent irrelevance of intent with AI requires an alternative option to account for liability. A guide of best practices may be helpful to direct AI. “Professor Lemley suggests [that by] implementing best practices, companies and developers could shoulder less liability for harms their programs may cause.”[6] While not specifically broken down, this concept is supported by the Cybersecurity and Infrastructure Security Agency’s (CISA) work to develop “best practices and guidance for secure and resilient AI software development and implementation.”[7]

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Addressing the Vectors for Attack on Artificial Intelligence Systems Used in Clinical Healthcare through a Robust Regulatory Framework: A Survey

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Addressing the Vectors for Attack on Artificial Intelligence Systems Used in Clinical Healthcare through a Robust Regulatory Framework: A Survey

By Benjamin Clark

Introduction and Overview

Artificial intelligence has captivated the current interest of the general public and academics alike, bringing closer attention to previously unexplored aspects of these algorithms, such as how they have been implemented into critical infrastructure, ways they can be secured through technical defensive measures, and how they can best be regulated to reduce risk of harm. This paper will discuss vulnerabilities common to artificial intelligence systems used in clinical healthcare and how bad actors exploit them before weighing the merits of current regulatory frameworks proposed by the U.S. and other nations for how they address the cybersecurity threats of these systems.

Primarily, artificial intelligence systems used in clinical research and healthcare settings involve either machine learning or deep learning algorithms.[1] Machine learning algorithms automatically learn and improve themselves without needing to be specifically programmed for each intended function. [2] However, these algorithms require that input data be pre-labeled by programmers to train algorithms to associate input features and best predict the labels for output, which involves some degree of human intervention.[3] The presence of humans in this process is referred to as “supervised machine learning” and is most often observed in systems used for diagnostics and medical imaging, in which physicians set markers for specific diagnoses as the labels and algorithms are able to categorize an image as a diagnosis based off the image’s characteristics.[4] Similarly, deep learning is a subset of machine learning characterized by its “neural network” structure in which input data is transmitted through an algorithm through input, output, and “hidden” layers to identify patterns in data.[5] Deep learning algorithms differ from those that utilize machine learning in that they require no human intervention after being trained; instead, deep learning algorithms process unlabeled data by determining what input is most important to create its own labels.[6]

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Generative AI Algorithms: The Fine Line Between Speech and Section 230 Immunity

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Generative AI Algorithms: The Fine Line Between Speech and Section 230 Immunity

 By Hannah G. Babinski

ABSTRACT

Russian-American writer and philosopher Ayn Rand once observed, “No speech is ever considered, but only the speaker. It’s so much easier to pass judgment on a man than on an idea.”[1] But what if the speaker is not a man, woman, or a human at all? Concepts of speech and identities of speakers have been the focal points of various court cases and debates in recent years. The Supreme Court and various district courts have faced complex and first-of-their-kind questions concerning emerging technologies, namely algorithms and recommendations, and contemplated whether their outputs constitute speech on behalf of an Internet service provider (“Internet platform”) that would not be covered by Section 230 of the Communications Decency Act (“Section 230”).  In this piece, I will examine some of the issues arising from the questions posed by Justice Gorsuch in Gonzalez v. Google, LLC, namely whether generative AI algorithms and their relative outputs constitute speech that is not immunized under Section 230. I will provide an overview of the technology behind generative AI algorithms and then examine the statutory language and interpretation of Section 230, applying that language and interpretive case law to generative AI. Finally, I will provide demonstrative comparisons between generative AI technology and human content creation and foundational Copyright Law concepts to illustrate how generative AI technologies and algorithmic outputs are akin to unique, standalone products that extend beyond the protections of Section 230.

 

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