arXiv subjects

Erin Howard

Publications and source records attributed to Erin Howard.

4 recordsLinked to original sources

arXiv2026

Unmitigated? How are the world's largest companies responding to AI risks?

AI is introducing new risks to large companies and amplifying existing risks, but whether companies are addressing the AI risks they are exposed to is largely untested. We investigated what outsiders can observe: whether companies publicly acknowledge AI risks. We coded public documents from 200 of the largest companies worldwide, collected in early 2026, against the 24 AI risks in the MIT AI Risk Repository, and compared acknowledgements with expert judgements of risk severity, sector vulnerability, and AI role responsibility from a companion Delphi study of 272 AI risk experts. Of the 100 largest companies, 32% did not publicly acknowledge any AI risks. Of the largest 1000 companies, we estimate that 62% [95% CI: 54%, 70%] did not publicly acknowledge any AI risks. Where companies did acknowledge risks, five accounted for over half of all acknowledgements: discrimination, weapons & cyberattacks, fraud & scams, loss of privacy, and devaluation of human creativity. Several risks that experts judged most severe were almost absent: almost no company acknowledged power centralization (concentration of power among those who control advanced AI), competitive dynamics (racing to deploy AI at the expense of safety), or dangerous capabilities. Companies in sectors experts judged more vulnerable to a risk were more likely to acknowledge that risk. But even companies in sectors judged 'extremely vulnerable' to a risk usually did not acknowledge it. Companies with roles experts judged most responsible for addressing a risk were no more likely to acknowledge it than other companies. General-purpose AI developers acknowledged AI misalignment (15%) far less often than discrimination (85%). These findings provide an empirical baseline for the design of AI risk disclosure requirements.

cs.CY↗

arXiv2026

An overview of stray light findings and interpretation during on-sky commissioning of LSSTCam

Wide-field telescopes are intrinsically difficult to shield from unwanted stray and scattered light, while the search to identify sources of contaminating light is frequently a challenging task. The Vera C.~Rubin Observatory, which achieved its first photon with the LSST Camera (LSSTCam) on April 15, 2025, will initiate a revolutionary era for the study of dark matter, dark energy, the transient sky, the Solar System, and the Milky Way. LSSTCam will provide near seeing-limited images of the sky in six bands ($u,g,r,i,z,y$) over a $3.^\circ 5$-diameter field of view, and over the course of a decade, it will execute the Legacy Survey of Space and Time (LSST). This work provides an overview of the dedicated stray and scattered light test campaign that has been undertaken since the start of Rubin commissioning. In particular, we highlight the processes used to characterize, model, and mitigate stray light present in LSSTCam images. The Rubin commissioning team created a series of testing and analysis tools to track stray light artifacts from their initial discovery through reproduction with timely observations, simulation using ray tracing to identify opto-mechanical origins, and finally devising corrective actions. The complex stray light features encountered by Rubin provide a wealth of experience for the future wide-field and extremely wide-field observatories. This work covers the many stages of a long journey that started with conceiving an innovative and challenging optical design, followed by the engineering and system engineering efforts to build it, to finally delivering an optimized and revolutionary cutting-edge facility.

astro-ph.IM↗

arXiv2022

Increase Investment in Accessible Physics Labs: A Call to Action for the Physics Education Community

The American Association of Physics Teachers (AAPT) Committee on Laboratories assembled a task force whose charge was to write an open letter to the physics education community calling for increased investment in accessible lab courses. Contributors to this paper include students, staff, and faculty with and without disabilities who expressed interest in the open letter. In this document, we recognize the need for making physics laboratories more accessible in all spaces (e.g., high school courses, graduate level courses, research labs). We focus on the experiences of students with disabilities in physics lab courses at the undergraduate level because that is the context for which the writing team had the most collective experience. The intended audiences for this document consist of undergraduate physics students, staff, and faculty, especially those who have direct stake in laboratory courses; physics departments; and member societies, including AAPT. We begin by presenting our motivation for the document and the importance of accessibility and diversity in education and the workforce. We start with the broader context of accessibility, narrowing our focus to physics education and the current state of affairs and availability of accessible resources. Accessibility is then discussed in the specific context of physics laboratory courses, focusing on how barriers are created and can be lowered. In exploring ideas and strategies for improving accessibility, we recognize that the development of multiple pathways for laboratory investigation creates opportunities to expand learning opportunities for more students in physics lab programs.

physics.ed-ph↗

arXiv2020

APOGEE Net: Improving the derived spectral parameters for young stars through deep learning

Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and spectral resolutions, but most often for main sequence or evolved stars, where reliable synthetic spectra provide labels and data for training. Spectral models of young stellar objects (YSOs) and low mass main sequence (MS) stars are less well-matched to their empirical counterparts, however, posing barriers to previous approaches to classify spectra of such stars. In this work we generate labels for YSOs and low mass MS stars through their photometry. We then use these labels to train a deep convolutional neural network to predict log g, Teff, and Fe/H for stars with APOGEE spectra in the DR14 dataset. This "APOGEE Net" has produced reliable predictions of log g for YSOs, with uncertainties of within 0.1 dex and a good agreement with the structure indicated by pre-main sequence evolutionary tracks, and correlate well with independently derived stellar radii. These values will be useful for studying pre-main sequence stellar populations to accurately diagnose membership and ages.

astro-ph.SR↗