Columbia University Libraries Journals
Not a member yet
6467 research outputs found
Sort by
Generative AI as Writing or Speaking Partners in L2 Learning: Implications for Learning-Oriented Assessments
Get PDFThe advent of generative AI (GenAI) technology has impacted second language (L2) learning and assessment, offering new opportunities for learners to practice and improve their skills. One approach gaining interest is employing GenAI tools as writing or speaking partners to provide personalized, real-time feedback and assistance to learners. These interactions allow learners to practice their writing and speaking skills while receiving assessment information on various aspects of language, including grammar, vocabulary, and pronunciation. Considering the potential of GenAI tools to enrich assessment and learning experiences, it is worth examining recent research on the use of this technology for this purpose. This paper reviews the literature on the use of GenAI as writing and speaking partners through the lens of the Learning-Oriented Assessment (LOA) framework (Purpura, 2024; Turner & Purpura, 2016) to explore how assessment data from GenAI tools could be leveraged to further learning. 
Virtual English as a Lingua Franca: Investigating the Discourse of Digital Exchanges and Understanding Technology-Enhanced Learning
Get PDFThe edited volume Virtual English as a Lingua Franca: Investigating the Discourse of Digital Exchanges and Understanding Technology-Enhanced Learning edited by Pineda and Bosso (2023) is the first book ever to be published with a focus on the study of virtual English as a lingua franca. This collection of empirical studies seeks to present a comprehensive account of the development of intercultural communication strategies through virtual English as a lingua franca, reflecting on the ways in which we make pragmatic meaning in today’s technology-informed, globalized world. This work emphasizes analyzing transmodal, trans-semiotic, and transcultural discourse practices in online spaces, providing a counterpoint to existing English as a lingua franca (ELF) research which has leaned towards unpacking formal features of ELF communication in face-to-face interactions. 
“We Need to Think about the Grammar”: Practices for Opening Explanations on Language and Changing their Linguistic Focus
Get PDFAlthough the role of grammar instruction is still highly debated within the field of second language acquisition and language pedagogy (Nassaji, 2017), explanations have emerged as fruit-bearing interactional phenomena that can illustrate the “how” of explicit grammar instruction (Fasel Lauzon, 2015; Hudson, 2011; Majlesi, 2018; Matsumoto & Dobs, 2017; Ro, 2021; Romig & Horan, 2023; Rosborough, 2011; Smotrova, 2014). A key feature of explanations is their sequential organization, described by Fasel Lauzon (2015) as consisting of an opening, a core, and a closing. In a nutshell, openings involve some problematization of prior talk, cores provide a candidate solution to said problem, and closings involve acceptance of that candidate solution. Researchers have revealed much about how cores are delivered, particularly focusing on how grammatical concepts can be illustrated through a variety of multimodal resources (Hudson, 2011; Matsumoto & Dobs, 2017; Romig & Horan, 2023; Rosborough, 2011; Smotrova, 2014), but less attention has been paid to openings and closings. This is perhaps unsurprising given that the bulk of content is delivered in an explanation core, but knowing how to open grammar explanations can be of particular importance for teachers in training who may not know when an explanation is due or how to initiate one themselves. Additionally, there does not seem to be any research detailing how to make clear that an explanation of a particular language point is grammatical, and not about any other linguistic issue (e.g., meaning, pronunciation, etc.). Thus, this paper adopts a conversation analytic framework to examine how a teacher opens an explanation sequence on the use of “so” and ensures that it is about its grammatical role as a coordinating conjunction, not on its meaning
A Tale of Two Fashion Nations: Comparative Fashion IP Laws in the United States and China
Get PDFThis Article compares intellectual property (IP) protection for fashion designs in the United States and China. As the two largest fashion markets in the world, these countries both have controversies over the optimal IP protection against knockoffs. This study reveals that similar disputes have occurred in both countries because IP laws are not primarily designed for fastchanging fashion products. Although courts in both countries have handled identical legal issues, such as separability in copyright law, distinctiveness in trademark law, and patentability for designs, their approaches are quite different. While the U.S. doctrines, such as functionality in three-dimensional trademarks and trade dress protection for product packaging, are more developed in precedents, the Chinese doctrines, such as copyrightability in garment designs, are sometimes led by industrial policies. Since the Chinese government has been determined to develop its fashion industry, these two major economies will continue to compete to be not only the largest fashion economy around the globe but also the best legal environment to foster fashion creativity. The broader implication of this Article is that IP issues in the fashion industry have demonstrated how two distinct legal frameworks, characterized by comparable regulations yet varying social and economic standings, address analogous legal challenges
The Role of Black Artists in the Reconceptualization of U.S. Resale Royalty Rights
Get PDFThe issue of resale royalties for artists has been a topic of contentious debate in the United States for decades. A key aspect in the consideration of this provision today rests within the evaluation of the role of Black visual artists to the U.S. art market. Between 2008 and 2021, the market for work by Black artists grew by close to 400%. While on its face, this increase appears to represent tremendous growth in the recognition of Black artists, more careful analysis reveals that there is still great inequity given the structure of the U.S. art market with respect to royalties. Resale royalties, also known as droit de suite, are recognized in legislation by more than seventy countries abroad. In practice, these provisions recognize a so-called “natural right” of artists to benefit from their work long term by providing artists with a reasonable percentage of the profits from the sale of their work in the secondary market. In the United States, the legislative implementation of droit de suite has been largely unsuccessful on both the state and federal level. In countries where droit de suite has been implemented, there is no shortage of criticism regarding the merits of the policy, as many view its shortcomings as an additional harm in the plethora of inequities faced by visual artists.
Focusing on more recent advocacy for resale royalties in the United States, there is a clear connection to the work of Black artists at the center of the conversation. Modern attempts to ensure a legal right to resale royalties have taken place through more targeted models including, but not limited to, mandatory terms for public auctions of Black art, specialized contract provisions in private art sales, and the use of Blockchain technology in the development of smart contracts. Furthermore, many successful models of securing resale royalties for artists have been developed as a result of the work of organizations committed to supporting the work of contemporary Black artists, such as the Souls Grown Deep Foundation and the Dean Collection.
This Note will examine the unique position of Black artists in the establishment of a legal right to resale royalties in the United States and the broader implications of this effort on the enhancement of equity for all American visual artists. Part I reviews the history of droit de suite, focusing on the history of resale royalty rights in the United States and pertinent factors that have contributed to the failed implementation of droit de suite thus far. Part II analyzes the harms perpetuated by an absence of resale royalty rights for visual artists in the United States, with a particular focus on the impact on Black artists despite an increased popularity of Black art. Part III examines the methodologies through which resale royalty rights have been reframed in the United States and the consideration of Black art in particular within these models. Overall, given the various dimensions of the American art market, the primary focus will be on public art transactions; however, the merging of contractual solutions with technological advancements is presumably adaptable to private sales as well. Ultimately, this Note argues that the reconceptualization of resale royalty rights toward an approach of individual implementation presents the most promising solution for securing a legal right to resale royalties in the U.S. art market, and increasing equity in the U.S. art market for all visual artists
The Private Litigation Impact of New York’s Green Amendment
Get PDFThe increasing urgency of climate change, combined with federal environmental inaction under the Trump Administration, inspired a wave of environmental action at the state and local level. Building on the environmental movement of the 1970s, activists have pushed to amend more than a dozen state constitutions to include “green amendments”—self-executing individual rights to a clean environ-ment. In 2022, New York activists succeeded, and New York’s Green Amendment (the NYGA) now provides that “Each person shall have a right to clean air and water, and a healthful environment.”
However, the power of the NYGA and similar green amendments turns on judicial interpretations of their scope. In the first decision to reach the issue, a New York trial court held, with little analysis, that the NYGA provides no private rights against private polluters. This conclusion could severely limit the reach and significance of state envi-ronmental rights.
This article examines a single question: Does the NYGA grant private rights that are enforceable against private parties? In answering this question, we examine the 50-year history of private litigation under green amendments, the substance and historical context of the NYGA, and the broader structure of New York’s constitution and environmental law. We conclude that the New York trial court got it wrong, and that the NYGA does provide a private cause of action against private parties. We further assess the indirect impact of constitutional envi-ronmental rights on private litigation, and conclude that the NYGA will have an enormous impact on private litigation generally, irrespective of whether New York’s courts reject private litigation under the NYGA. This discussion provides a novel evaluation of the shadow that consti-tutional changes cast on non-constitutional law
Healthcare Capacity Building in Northwest Syria: Challenges, Successes, and Lessons Learned
No full textThe conflict in Syria has caused significant loss of life and widespread displacement. Northwest Syria (NWS) has been heavily impacted, leading to challenges in providing healthcare services. Attacks on healthcare workers and facilities have worsened the situation. Healthcare students and professionals have been specifically targeted, disrupting their education and resulting in migration and a shortage of skilled healthcare workers. To address these challenges, local and international organizations and institutions have supported long-term projects to improve healthcare facilities and provide a trained healthcare workforce. Collaborations with multiple stakeholders have been established to ensure comprehensive and effective training opportunities, enabling healthcare workers to better serve the population's healthcare needs. A range of undergraduate, postgraduate, and research programs have been developed to enhance healthcare capacity building. These programs aim to strengthen the knowledge and skills of healthcare professionals in NWS. Efforts have been made to strengthen the health system and build the capacity of policy makers in utilizing evidence-based knowledge for informed policy decisions. Global and regional partnerships, along with adequate funding, have played a significant role in the successful enhancement of capacity building activities at all levels.
Building healthcare and health research capacity in underdeveloped and conflict-affected parts of NWS presents numerous challenges. Underdeveloped infrastructure, inadequate teaching and service delivery tools, gender disparities, and the sustainability of funding create obstacles to effective capacity building. The political context, coupled with security concerns further complicate efforts. The accreditation of education, and the brain drain of skilled healthcare professionals, add to the difficulties in strengthening the healthcare system in NWS. Addressing these challenges requires comprehensive and collaborative approaches that prioritize stability, security, gender equity, sustainable funding, and improved coordination and resources for education and service delivery. The lessons learned from capacity-building efforts in the Syrian conflict have broader implications for other regions facing similar challenges
The Epistemological Consequences of Artificial Intelligence, Precision Medicine, and Implantable Brain-Computer Interfaces
Get PDFABSTRACT
I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments.
INTRODUCTION
In contrast to the classic model of randomized-control trials, often with a large number of subjects enrolled, precision medicine attempts to optimize therapeutic outcomes by focusing on the individual.[i] A recent publication highlights the strengths and weakness of both traditional evidence-based medicine and precision medicine.[ii] Plus, it outlines a tension in the shift from evidence-based medicine’s inductive reasoning style (the collection of data to postulate general theories) to precision medicine’s abductive reasoning style (the generation of an idea from the limited data available).[iii] The paper’s main example is the application of precision medicine for the treatment of cancer.[iv] I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too.
As the name suggests, brain-computer interfaces are a significant advancement in neurotechnology that directly connects someone’s brain to external or implanted devices.[v] Among the various kinds of brain-computer interfaces, adaptive deep brain stimulation devices require numerous personalized adjustments to their settings during the implantation and computation stages in order to provide adequate relief to patients with treatment-resistant disorders. What makes these devices unique is how adaptive deep brain stimulation integrates a sensory component to initiate the stimulation. While not commonly at the level of sophistication as self-supervising or generative large language models,[vi] they currently allow for a semi-autonomous form of neuromodulation. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments.[vii]
ANALYSIS
I. The State of Precision Medicine in Oncology and the Epistemological Shift
While a thorough overview of precision medicine for the treatment of cancer is beyond the scope of this article, its practice can be roughly summarized as identifying clinically significant characteristics a patient possesses (e.g., genetic traits) to land on a specialized treatment option that, theoretically, should benefit the patient the most.[viii] However, in such a practice of stratification patients fall into smaller and smaller populations and the quality of evidence that can be applied to anyone outside these decreases in turn.[ix] As inductive logic helps to articulate, the greater the number of patients that respond to a particular therapy the higher the probability of its efficacy. By straying from this logical framework, precision medicine opens the treatment of cancer to more uncertainty about the validity of these approaches to the resulting disease subcategories.[x] Thus, while contemporary medical practices explicitly describe some treatments as “personalized”, they ought not be viewed as inherently better founded than other therapies.[xi]
A relevant contemporary case of precision medicine out of Norway focuses on the care of a patient with cancer between the ventricles of the heart and esophagus, which had failed to respond to the standard regimen of therapies over four years.[xii] In a last-ditch effort, the patient elected to pay out-of-pocket for an experimental immunotherapy (nivolumab) at a private hospital. He experienced marked improvements and a reduction in the size of the tumor. Understandably, the patient tried to pursue further rounds of nivolumab at a public hospital. However, the hospital initially declined to pay for it given the “lack of evidence from randomised clinical trials for this drug relating to this [patient’s] condition.”[xiii] In rebuttal to this claim, the patient countered that he was actually similar to a subpopulation of patients who responded in “open‐label, single arm, phase 2 studies on another immune therapy drug” (pembrolizumab).[xiv] Given this interpretation of the prior studies and the patient’s response, further rounds of nivolumab were approved. Had the patient not had improvements in the tumor’s size following a round of nivolumab, then pembrolizumab’s prior empirical evidence in isolation would have been insufficient, inductively speaking, to justify his continued use of nivolumab.[xv]
The case demonstrates a shift in reasoning from the traditional induction to abduction. The phenomenon of ‘cancer improvement’ is considered causally linked to nivolumab and its underlying physiological mechanisms.[xvi] However, “the weakness of abductions is that there may always be some other better, unknown explanation for an effect. The patient may for example belong to a special subgroup that spontaneously improves, or the change may be a placebo effect. This does not mean, however, that abductive inferences cannot be strong or reasonable, in the sense that they can make a conclusion probable.”[xvii] To demonstrate the limitations of relying on the abductive standard in isolation, commentators have pointed out that side effects in precision medicine are hard to rule out as being related to the initial intervention itself unless trends from a group of patients are taken into consideration.[xviii]
As artificial intelligence (AI) assists the development of precision medicine for oncology, this uncertainty ought to be taken into consideration. The implementation of AI has been crucial to the development of precision medicine by providing a way to combine large patient datasets or a single patient with a large number of unique variables with machine learning to recommend matches based on statistics and probability of success upon which practitioners can base medical recommendations.[xix] The AI is usually not establishing a causal relationship[xx] – it is predicting. So, as AI bleeds into medical devices, like brain-computer interfaces, the same cautions about using abductive reasoning alone should be carried over.
II. Responsive Neurostimulation, AI, and Personalized Medicine
Like precision medicine in cancer treatment, computer-brain interface technology similarly focuses on the individual patient through personalized settings. In order to properly expose the intersection of AI, precision medicine, abductive reasoning, and implantable neurotechnologies, the descriptions of adaptive deep brain stimulation systems need to deepen.[xxi] As a broad summary of adaptive deep brain stimulation, to provide a patient with the therapeutic stimulation, a neural signal, typically referred to as a local field potential,[xxii] must first be detected and then interpreted by the device. The main adaptive deep brain stimulation device with premarket approval, the NeuroPace Responsive Neurostimulation system, is used to treat epilepsy by detecting and storing “programmer-defined phenomena.”[xxiii] Providers can optimize the detection settings of the device to align with the patient’s unique electrographic seizures as well as personalize the reacting stimulation’s parameters.[xxiv] The provider adjusts the technology based on trial and error. One day machine learning algorithms will be able to regularly aid this process in myriad ways, such as by identifying the specific stimulation settings a patient may respond to ahead of time based on their electrophysiological signatures.[xxv] Either way, with AI or programmers, adaptive neurostimulation technologies are individualized and therefore operate in line with precision medicine rather than standard treatments based on large clinical trials.
Contemporary neurostimulation devices are not usually sophisticated enough to be prominent in AI discussions where the topics of neural networks, deep learning, generative models, and self-attention dominate the conversation. However, implantable high-density electrocorticography arrays (a much more sensitive version than adaptive deep brain stimulation systems use) have been used in combination with neural networks to help patients with neurologic deficits from a prior stroke “speak” through a virtual avatar.[xxvi] In some experimental situations, algorithms are optimizing stimulation parameters with increasing levels of independence.[xxvii] An example of neurostimulation that is analogous to the use of nivolumab in Norway surrounds a patient in the United States who was experiencing both treatment-resistant OCD and temporal lobe epilepsy.[xxviii]Given the refractory nature of her epilepsy, implantation of an adaptive deep brain stimulation system was indicated. As a form of experimental therapy, her treatment-resistant OCD was also indicated for the off-label use of an adaptive deep brain stimulation set-up. Another deep brain stimulation lead, other than the one implanted for epilepsy, was placed in the patient’s right nucleus accumbens and ventral pallidum region given the correlation these nuclei had with OCD symptoms in prior research. Following this, the patient underwent “1) ambulatory, patient-initiated magnet-swipe storage of data during moments of obsessive thoughts; (2) lab-based, naturalistic provocation of OCD-related distress (naturalistic provocation task); and (3) lab-based, VR [virtual reality] provocation of OCD-related distress (VR provocation task).”[xxix] Such signals were used to identify when to deliver the therapeutic stimulation in order to counter the OCD symptoms. Thankfully, following the procedure and calibration the patient exhibited marked improvements in their OCD symptoms and recently shared her results publicly.[xxx]
In both cases, there is a similar level of abductive justification for the efficacy of the delivered therapy. In the case study in which the patient was treated with adaptive deep brain stimulation, they at least had their neural activity tested in various settings to determine the optimum parameters for treatment to avoid them being based on guesswork. Additionally, the adaptive deep brain stimulation lead was already placed before the calibration trials were conducted, meaning that the patient had already taken on the bulk of the procedural risk before the efficacy could be determined. Such an efficacy test could have been replicated in the first patient’s cancer treatment, had it been biopsied and tested against the remaining immunotherapies in vitro. Yet, in the case of cancer with few options, one previous dose of a drug that appeared to work on the patient may justify further doses. However, as the Norwegian case presents, corroboration with known responses to a similar drug (from a clinical trial) could be helpful to validate the treatment strategy. (It should be noted that both patients were resigned to these last resort options regardless of the efficacy of treatment.)
There are some elements of inductive logic seen with adaptive deep brain stimulation research in general. For example, abductively the focus could be that patient X’s stimulation parameters are different from patient Y’s and patient Z’s. In contrast, when grouped as subjects who obtained personalized stimulation, patients X, Y, and Z demonstrate an inductive aspect to this approach’s safety and/or efficacy. The OCD case holds plenty of abductive characteristics in line with precision medicine’s approach to treating cancer and as more individuals try the method, there will be additional data. With the gradual integration of AI into brain-computer interfaces in the name of efficacy, this reliance on abduction will continue, if not grow, over time. Moving forward, if a responsive deep brain stimulation treatment is novel and individualized (like the dose of nivolumab) and there is some other suggestion of efficacy (like clinical similarities to other patients in the literature), then it may justify insurance coverage for the investigative intervention, absent other unrelated reasons to deny it.
III. Ethical Implications and Next Steps
While AI’s use in oncology and neurology is not yet as prominent as its use in other fields (e.g., radiology), it appears to be on the horizon for both.[xxxi] AI can be found in both the functioning of the neurotechnologies as well as the implementation of precision medicine. The increasing use of AI may serve to further individualize both oncologic and neurological therapies. Given these implications and the handful of publications cited in this article, it is important to have a nuanced evaluation of how these treatments, which heavily rely on abductive justification, ought to be managed.
The just use an abductive approach may be difficult as AI infused precision medicine is further pursued. At baseline, such technology relies on a level of advanced technology literacy among the general public and could exclude populations who lack access to basic technological infrastructure or know-how from participation.[xxxii] Even among nations with adequate infrastructure, as more patients seek out implantable neurotechnologies, which require robust healthcare resources, the market will favor patient populations that can afford this complex care.[xxxiii]
If patients already have the means to pay for an initial dose/use of a precision medicine product out of pocket, should insurance providers be required to cover subsequent treatments?[xxxiv] That is, if a first dose of a cancer drug or a deep brain stimulator over its initial battery life is successful, patients may feel justified in having the costs of further treatments covered. The Norwegian patient’s experience implies there is a precedent for the idea that some public insurance companies ought to cover successful cancer therapies, however, insurance companies may not all see themselves as obligated to cover neurotechnologies that rely on personalized settings or that are based on precision/abductive research more than on clinical trials.
CONCLUSION
The fact that the cases outlined above rely on abductive style of reasoning implies that there may not be as strong a justification for coverage by insurance, as they are both experimental and individualized, when compared to the more traditional large clinical trials in which groups have the same or a standardized protocol (settings/doses). If a study is examining the efficacy of a treatment with a large cohort of patients or with different experimental groups/phases, insurance companies may conclude that the resulting symptom improvements are more likely to be coming from the devices themselves. A preference for inductive justification may take priority when ruling in favor of funding someone’s continued use of an implantable neurostimulator. There are further nuances to this discussion surrounding the classifications of these interventions as research versus clinical care that warrant future exploration, since such a distinction is more of a scale[xxxv] than binary and could have significant impacts on the “right-to-try” approach to experimental therapies in the United States.[xxxvi] Namely, given the inherent limitations of conducting large cohort trials for deep brain stimulation interventions on patients with neuropsychiatric disorders, surgically innovative frameworks that blend abductive and inductive methodologies, like with sham stimulation phases, have traditionally been used.[xxxvii] Similarly, for adaptive brain-computer interface systems, if there are no large clinical trials and instead only publications that demonstrate that something similar worked for someone else, then, in addition to the evidence that the first treatment/dose worked for the patient in question, the balance of reasoning would be valid and arguably justify insurance coverage. As precision approaches to neurotechnology become more common, frameworks for evaluating efficacy will be crucial both for insurance coverage and for clinical decision making.
ACKNOWLEDGEMENT
This article was originally written as an assignment for Dr. Francis Shen’s “Bioethics & AI” course at Harvard’s Center for Bioethics. I would like to thank Dr. Shen for his comments as well as my colleagues in the Lázaro-Muñoz Lab for their feedback.
-
[i] Jonathan Kimmelman and Ian Tannock, “The Paradox of Precision Medicine,” Nature Reviews. Clinical Oncology 15, no. 6 (June 2018): 341–42, https://doi.org/10.1038/s41571-018-0016-0.
[ii] Henrik Vogt and Bjørn Hofmann, “How Precision Medicine Changes Medical Epistemology: A Formative Case from Norway,” Journal of Evaluation in Clinical Practice 28, no. 6 (December 2022): 1205–12, https://doi.org/10.1111/jep.13649.
[iii] David Barrett and Ahtisham Younas, “Induction, Deduction and Abduction,” Evidence-Based Nursing 27, no. 1 (January 1, 2024): 6–7, https://doi.org/10.1136/ebnurs-2023-103873.
[iv] Vogt and Hofmann, “How Precision Medicine Changes Medical Epistemology,” 1208.
[v] Wireko Andrew Awuah et al., “Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications,” World Neurosurgery, May 22, 2024, S1878-8750(24)00867-2, https://doi.org/10.1016/j.wneu.2024.05.104.
[vi] Mark Riedl, “A Very Gentle Introduction to Large Language Models without the Hype,” Medium (blog), May 25, 2023, https://mark-riedl.medium.com/a-very-gentle-introduction-to-large-language-models-without-the-hype-5f67941fa59e.
[vii] David E. Burdette and Barbara E. Swartz, “Chapter 4 - Responsive Neurostimulation,” in Neurostimulation for Epilepsy, ed. Vikram R. Rao (Academic Press, 2023), 97–132, https://doi.org/10.1016/B978-0-323-91702-5.00002-5.
[viii] Kimmelman and Tannock, 2018.
[ix] Kimmelman and Tannock, 2018.
[x] Simon Lohse, “Mapping Uncertainty in Precision Medicine: A Systematic Scoping Review,” Journal of Evaluation in Clinical Practice 29, no. 3 (April 2023): 554–64, https://doi.org/10.1111/jep.13789.
[xi] Kimmelman and Tannock, “The Paradox of Precision Medicine.”
[xii] Vogt and Hofmann, 1206.
[xiii] Vogt and Hofmann, 1206.
[xiv] Vogt and Hofmann, 1206.
[xv] Vogt and Hofmann, 1207.
[xvi] Vogt and Hofmann, 1207.
[xvii] Vogt and Hofmann, 1207.
[xviii] Vogt and Hofmann, 1210.
[xix] Mehar Sahu et al., “Chapter Three - Artificial Intelligence and Machine Learning in Precision Medicine: A Paradigm Shift in Big Data Analysis,” in Progress in Molecular Biology and Translational Science, ed. David B. Teplow, vol. 190, 1 vols., Precision Medicine (Academic Press, 2022), 57–100, https://doi.org/10.1016/bs.pmbts.2022.03.002.
[xx] Stefan Feuerriegel et al., “Causal Machine Learning for Predicting Treatment Outcomes,” Nature Medicine 30, no. 4 (April 2024): 958–68, https://doi.org/10.1038/s41591-024-02902-1.
[xxi] Sunderland Baker et al., “Ethical Considerations in Closed Loop Deep Brain Stimulation,” Deep Brain Stimulation 3 (October 1, 2023): 8–15, https://doi.org/10.1016/j.jdbs.2023.11.001.
[xxii] David Haslacher et al., “AI for Brain-Computer Interfaces,” 2024, 7, https://doi.org/10.1016/bs.dnb.2024.02.003.
[xxiii] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 103–4; “Premarket Approval (PMA),” https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P100026.
[xxiv] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 104.
[xxv] Burdette and Swartz, 126.
[xxvi] Sean L. Metzger et al., “A High-Performance Neuroprosthesis for Speech Decoding and Avatar Control,” Nature 620, no. 7976 (August 2023): 1037–46, https://doi.org/10.1038/s41586-023-06443-4.
[xxvii] Hao Fang and Yuxiao Yang, “Predictive Neuromodulation of Cingulo-Frontal Neural Dynamics in Major Depressive Disorder Using a Brain-Computer Interface System: A Simulation Study,” Frontiers in Computational Neuroscience 17 (March 6, 2023), https://doi.org/10.3389/fncom.2023.1119685; Mahsa Malekmohammadi et al., “Kinematic Adaptive Deep Brain Stimulation for Resting Tremor in Parkinson’s Disease,” Movement Disorders 31, no. 3 (2016): 426–28, https://doi.org/10.1002/mds.26482.
[xxviii] Young-Hoon Nho et al., “Responsive Deep Brain Stimulation Guided by Ventral Striatal Electrophysiology of Obsession Durably Ameliorates Compulsion,” Neuron 0, no. 0 (October 20, 2023), https://doi.org/10.1016/j.neuron.2023.09.034.
[xxix] Nho et al.
[xxx] Nho et al.; Erik Robinson, “Brain Implant at OHSU Successfully Controls Both Seizures and OCD,” OHSU News, accessed March 3, 2024, https://news.ohsu.edu/2023/10/25/brain-implant-at-ohsu-successfully-controls-both-seizures-and-ocd.
[xxxi] Awuah et al., “Bridging Minds and Machines”; Haslacher et al., “AI for Brain-Computer Interfaces.”
[xxxii] Awuah et al., “Bridging Minds and Machines.”
[xxxiii] Sara Green, Barbara Prainsack, and Maya Sabatello, “The Roots of (in)Equity in Precision Medicine: Gaps in the Discourse,” Personalized Medicine 21, no. 1 (January 2024): 5–9, https://doi.org/10.2217/pme-2023-0097.
[xxxiv] Green, Prainsack, and Sabatello, 7.
[xxxv] Robyn Bluhm and Kirstin Borgerson, “An Epistemic Argument for Research-Practice Integration in Medicine,” The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine 43, no. 4 (July 9, 2018): 469–84, https://doi.org/10.1093/jmp/jhy009.
[xxxvi] Vijay Mahant, “‘Right-to-Try’ Experimental Drugs: An Overview,” Journal of Translational Medicine 18 (June 23, 2020): 253, https://doi.org/10.1186/s12967-020-02427-4.
Public Perceptions Can Guide Regulation of Public Facial Recognition
Get PDFFacial recognition technology is changing how people pass through customs at airports, check in at schools, and move anonymously in public spaces. Yet despite these transformations, its use by the government is largely unregulated. This Article informs the policy and doctrinal debates about facial recognition by presenting a public attitudes perspective. These three novel empirical studies show the nuanced views that Americans hold about government use of facial recognition. The data reveal that people are generally comfortable with the government using facial recognition to investigate serious crimes, enhance the security of controlled spaces like airports and schools, and increase the efficiency of identity verification in some contexts. But people are often not comfortable with casual governmental facial recognition use in public spaces. This pattern of strong comfort for tailored uses persisted even when, in a second study, participants were primed with negative information about the accuracy of facial recognition. Here I explore the implications of these results for both current Fourth Amendment doctrine as well as future legislative reform, promoting a balanced approach that allows tailored use of facial recognition while regulating its purposes