ARTIFICIAL INTELLIGENCE, LLC: CORPORATE PERSONHOOD AS TORT REFORM

Our legal system has long tried to fit the square peg of artificial intelligence (AI) technologies into the round hole of the current tort regime, overlooking the inability of traditional liability schemes to address the nuances of how AI technology creates harms. The current tort regime deals out rough justice—using strict liability for some AI products and using the negligence rule for other AI services—both of which are insufficiently tailored to achieve public policy objectives. Under a strict liability regime where manufacturers are always held liable for the faults of their technology regardless of knowledge or precautionary measures, firms are incentivized to play it safe and stifle innovation. But even with this cautionary stance, the goals of strict liability cannot be met due to the unique nature of AI technology: its mistakes are merely “efficient errors”—they appropriately surpass the human baseline, they are game theory problems intended for a jury, they are necessary to train a robust system, or they are harmless but misclassified. Under a negligence liability regime where the onus falls entirely on consumers to prove the element of causation, victimized consumers must surmount the difficult hurdle of tracing the vectors of causation through the “black box” of algorithms. Unable to do so, many are left without sufficient recourse or compensation

Antitrust by Algorithm

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 implementation of antitrust by algorithm

Assessing Automated Administration

To fulfill their responsibilities, governments rely on administrators and employees who, simply because they are human, are prone to individual and group decision-making errors. These errors have at times produced both major tragedies and minor inefficiencies. One potential strategy for overcoming cognitive limitations and group fallibilities is to invest in artificial intelligence (AI) tools that allow for the automation of governmental tasks, thereby reducing reliance on human decision-making. Yet as much as AI tools show promise for improving public administration, automation itself can fail or can generate controversy. Public administrators face the question of when exactly they should use automation. This paper considers the justifications for governmental reliance on AI along with the legal concerns raised by such reliance. Comparing AI-driven automation with a status quo that relies on human decision-making, the paper provides public administrators with guidance for making decisions about AI use. After explaining why prevailing legal doctrines present no intrinsic obstacle to governmental use of AI, the paper presents considerations for administrators to use in choosing when and how to automate existing processes. It recommends that administrators ask whether their contemplated uses meet the preconditions for the deployment of AI tools and whether these tools are in fact likely to outperform the status quo. In moving forward, administrators should also consider the possibility that a contemplated AI use will generate public or legal controversy, and then plan accordingly. The promise and legality of automated administration ultimately depends on making responsible decisions about when and how to deploy this technology

Algorithm vs. Algorithm

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, cognitive biases, and racial prejudices, among other problems. On an organizational level, humans succumb to groupthink and free-riding, along with other collective dysfunctionalities. As a result, human decisions will in some cases prove far more problematic than their digital counterparts. Digital algorithms, such as machine learning, can improve governmental performance by facilitating outcomes that are more accurate, timely, and consistent. Still, when deciding whether to deploy digital algorithms to perform tasks currently completed by humans, public officials should proceed with care on a case-by-case basis. They should consider both whether a particular use would satisfy the basic preconditions for successful machine learning and whether it would in fact lead to demonstrable improvements over the status quo. The question about the future of public administration is not whether digital algorithms are perfect. Rather, it is a question about what will work better: human algorithms or digital ones

Regulation of Algorithmic Tools in the United States

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 for the right balance between ensuring public safety and transparency and promoting innovation and competitiveness on the global stage

The Spitzer c2d Survey of Nearby Dense Cores: III: Low Mass Star Formation in a Small Group, L1251B

We present a comprehensive study of a low-mass star-forming region,L1251B, at wavelengths from the near-infrared to the millimeter. L1251B, where only one protostar, IRAS 22376+7455, was known previously, is confirmed to be a small group of protostars based on observations with the Spitzer Space Telescope. The most luminous source of L1251B is located 5" north of the IRAS position. A near-infrared bipolar nebula, which is not associated with the brightest object and is located at the southeast corner of L1251B, has been detected in the IRAC bands. OVRO and SMA interferometric observations indicate that the brightest source and the bipolar nebula source in the IRAC bands are deeply embedded disk sources.Submillimeter continuum observations with single-dish telescopes and the SMA interferometric observations suggest two possible prestellar objects with very high column densities. Outside of the small group, many young stellar object candidates have been detected over a larger region of 12' x 12'. Extended emission to the east of L1251B has been detected at 850 micron; this "east core" may be a site for future star formation since no point source has been detected with IRAC or MIPS. This region is therefore a possible example of low-mass cluster formation, where a small group of pre- and protostellar objects (L1251B) is currently forming, alongside a large starless core (the east core).Comment: 35 pages, 15 figures, accepted for publication in ApJ, for the full resolution paper, visit "http://peggysue.as.utexas.edu/SIRTF/PAPERS/pap27.pub.pdf

Regulation of B-cell development and tolerance by different members of the miR-17∼1/492 family microRNAs

The molecular mechanisms that regulate B-cell development and tolerance remain incompletely understood. In this study, we identify a critical role for the miR-17∼1/492 microRNA cluster in regulating B-cell central tolerance and demonstrate that these miRNAs control early B-cell development in a cell-intrinsic manner. While the cluster member miR-19 suppresses the expression of Pten and plays a key role in regulating B-cell tolerance, miR-17 controls early B-cell development through other molecular pathways. These findings demonstrate differential control of two closely linked B-cell developmental stages by different members of a single microRNA cluster through distinct molecular pathwaysThis study is supported by the PEW Charitable Trusts, Cancer Research Institute, Lupus Research Institute and National Institute of Health (R01AI087634, R01AI089854, R56AI110403 and R56AI121155 to C.X.

Multi-level-assistance robotic platform for navigation in the urinary system: design and preliminary tests

This work was supported by the ATLAS project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 813782. This work was also partially supported by French State Funds managed by the “Agence Nationale de la Recherche (ANR)” through the “Investissements d’Avenir” (Investments for the Future) Program under Grant ANR-10-IAHU-02 (IHU-Strasbourg).Peer ReviewedPostprint (published version

Assessment of two hand hygiene regimens for intensive care unit personnel

http://deepblue.lib.umich.edu/bitstream/2027.42/55459/1/Larson EL, Assessment of two hand hygiene regimens for ICU personnel, 2001.pd

The Spitzer c2d Survey of Nearby Dense Cores: IV. Revealing the Embedded Cluster in B59

Infrared images of the dark cloud core B59 were obtained with the Spitzer Space Telescope as part of the "Cores to Disks" Legacy Science project. Photometry from 3.6-70 microns indicates at least 20 candidate low-mass young stars near the core, more than doubling the previously known population. Out of this group, 13 are located within about 0.1 pc in projection of the molecular gas peak, where a new embedded source is detected. Spectral energy distributions span the range from small excesses above photospheric levels to rising in the mid-infrared. One other embedded object, probably associated with the millimeter source B59-MMS1, with a bolometric luminosity L(bol) roughly 2 L(sun), has extended structure at 3.6 and 4.5 microns, possibly tracing the edges of an outflow cavity. The measured extinction through the central part of the core is A(V) greater than of order 45 mag. The B59 core is producing young stars with a high efficiency