Perspective.
The World According to Generative Artificial Intelligence
Large language models are transforming how humans acquire and interpret information, raising pressing ethical concerns. To mitigate the related risks, policymakers should promote digital AI literacy and develop tools to understand the inherent biases of generative AI tools.
I sense an uptick in the number of articles on the topic of AI authored/invented IP. Could we have some resolution in my lifetime? Perhaps defining the author (or inventor) as the human who asked AI the question?
https://academic.oup.com/jiplp/advance-article/doi/10.1093/jiplp/jpae119/7965768
Understanding authorship in Artificial Intelligence-assisted works
The advent of generative Artificial Intelligence (AI) has brought about a significant shift in the way works are created, with the blurring of boundaries between human and machine-driven creation processes becoming a prominent challenge. This leads to the question of whether authorship in such works exists and, if so, whom it should be attributed to.
This article focusses on an analysis of existing case law of the Court of Justice of the European Union and selected EU Member State courts, in order to find indications about what to consider when examining the authorship of AI-assisted works in the European copyright system.
Ultimately, a four-step test is proposed which aids in assessing whether there is authorship in concrete works and whom it should be attributed to. The first step asks what persons are involved in the creation process before determining—as second step—the kind of AI system used. The third step analyses whether the persons involved exercised a sufficient subjective judgment in the composition of the work; the final step determines whether they had an adequate control over the execution.
(Related)
https://search.informit.org/doi/abs/10.3316/informit.T2025011900005201175533311
'AI is not an inventor': 'Thaler v Comptroller of Patents, Designs and Trademarks' and the patentability of AI
The increasing use of Artificial Intelligence (AI) technologies in inventive processes raises numerous patent law issues, including whether AI can be an inventor under law and who owns the AI-generated inventions. The UK Supreme Court decision in 'Thaler v Comptroller of Patents, Designs and Trademarks' has provided an ultimate answer to this question: AI cannot be an inventor for the purposes of patent law. This note argues, first, that while such a human-centric approach to inventorship might discourage the use and development of AI technologies with autonomous invention capabilities, it will help retain an active human involvement in technologically supported inventive processes and continuously foster human ingenuity. Second, despite the Court focusing on what patent law is and not on what the law should be, the decision will be influential in the ongoing discussions on the future of patent law and will make it more difficult to expand patent law to incorporate non-human inventors. Third, the decision has opened, or revealed, the gaps in patent law that the emergence of AI technologies have created and for which new legal solutions will be needed, especially with relation to the ownership of AI-assisted inventions and the validation of inventorship claims.
Can AI be trusted? An ongoing question.
https://ejournal.bamala.org/index.php/yudhistira/article/view/251
Digital Epistemology: Evaluating The Credibility Of Knowledge Generated By Ai
The rise of Artificial Intelligence (AI) as a key player in knowledge production has transformed traditional epistemological frameworks, necessitating a critical evaluation of its credibility and trustworthiness. This paper investigates the emerging domain of digital epistemology, focusing on how AI challenges established notions of validity, reliability, and trust in knowledge generation. By examining philosophical perspectives and interdisciplinary insights, we identify three primary challenges to AI-generated knowledge: algorithmic biases, the dependence on flawed or incomplete datasets, and the opacity of decision-making processes. These challenges raise significant concerns about the ethical and epistemological implications of relying on AI in contexts such as healthcare, law, and policy-making. Furthermore, this study explores the mechanisms required to evaluate the credibility of AI systems, emphasizing the importance of transparency, explainability, and accountability in fostering trust. We argue that the epistemological relationship between AI and its human users hinges on balancing technological capabilities with ethical considerations, ensuring that AI serves as a tool to complement rather than undermine human autonomy. The findings underscore the need for a robust digital epistemology that adapts classical principles of knowledge to the complexities of the digital era. This framework can guide the development of AI systems that prioritize ethical decision-making and credible knowledge outputs, addressing both theoretical and practical concerns. By bridging philosophy and technology, this paper offers critical insights into the evolving role of AI in shaping how knowledge is produced, validated, and trusted in the digital age.
(Related)
https://jurnal.fs.umi.ac.id/index.php/alpamet/article/view/855
Artificial Intelligence and Lokean Epistemology
This research explores the intersection of artificial intelligence (AI) and John Locke’s epistemology, examining how advancements in AI challenge traditional notions of knowledge and the subject of knowledge. The increasing sophistication of AI systems, which simulate human-like reasoning and learning processes, blurs the boundaries between human cognition and machine intelligence. This study investigates the potential connections between AI and Locke's theory of knowledge, which emphasizes that knowledge arises from sensory experience and reflection. Beginning with a review of Locke’s epistemological principles, including the role of empirical data and the distinction between primary and secondary qualities, the research evaluates how AI’s reliance on vast datasets, machine learning algorithms, and neural networks aligns—or diverges—from Locke’s framework. It questions whether AI systems can possess knowledge in the Lockean sense and examines the epistemic status of AI-generated outputs in terms of reliability, trustworthiness, and biases in training data. The role of human oversight in validating AI-generated insights is also critically assessed. Ultimately, this study contributes to the ongoing discourse on the nature and limits of knowledge in the AI era, challenging traditional epistemological frameworks. By integrating Locke’s principles with contemporary AI developments, it advances the debate on what it means to "know" in a world increasingly mediated by artificial agents, offering a nuanced perspective on the implications of AI for human understanding and the evolving landscape of knowledge.
Useful?
https://digitalcommons.wcl.american.edu/facsch_lawrev/2285/
A Stepwise Approach to Copyright and Generative Artificial Intelligence
In order to understand whether generative AI may infringe copyrights, one must first have a sound grounding in the technical complexities of the “generative AI supply chain.” This Article not only explains the technology in terms accessible to a legal audience, but also explores the doctrinal complexities of how generative AI maps onto existing copyright law. The authors do an admirable job in accomplishing both goals.
First I’ve seen on this topic.
Artificial Intelligence and the HIPAA Privacy Rule: A Primer
Consider a medical chatbot that a hospital makes available to patients scheduled for colonoscopies.1 The chatbot uses artificial intelligence (AI)2 to conduct online conversations via text or text-to-speech in lieu of providing patients direct contact with a live person.3 The chatbot, which was designed to improve patient compliance with unpleasant bowel preparation, has been shown to increase the number of people who have successful colonoscopies and decrease the number of people who fail to show for their procedures.4 Given that patients do share sensitive, bowel-related information with the chatbot, one question is whether federal or state laws protect the privacy and security of their information.
Further consider an AI-driven symptom checker that a health system makes available on its website.5
… Consider, too, a physician who uses ChatGPT 8 to generate automated summaries of medical histories and patient interactions.9
… Further consider a health insurer that uses AI to review and, more frequently than not, deny Medicare Advantage claims for elderly beneficiaries notwithstanding their physicians’ documentation showing that their health care services are medically necessary. 13
… Finally, consider the number of large technology companies and startups that are working with health industry participants, including hospitals and health insurers, to research, create, and deploy machine learning healthcare solutions.16
