arXiv subjects

Sarah Bird

Publications and source records attributed to Sarah Bird.

11 recordsLinked to original sources

arXiv2023

A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications

We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services. Our framework for automatically measuring harms from LLMs builds on existing technical and sociotechnical expertise and leverages the capabilities of state-of-the-art LLMs, such as GPT-4. We use this framework to run through several case studies investigating how different LLMs may violate a range of RAI-related principles. The framework may be employed alongside domain-specific sociotechnical expertise to create measurements for new harm areas in the future. By implementing this framework, we aim to enable more advanced harm measurement efforts and further the responsible use of LLMs.

cs.CL↗

arXiv2021

The Stellar "Snake" I: Whole Structure and Properties

To complement our previous discovery of the young snake-like structure in the solar neighborhood and reveal the structure's full extent, we build two samples of stars within the Snake and its surrounding territory from {\tt Gaia EDR3}. With the friends-of-friends algorithm, we identify 2694 and 9615 Snake member candidates from the two samples. Thirteen open clusters are embedded in these member candidates. By combining the spectroscopic data from multiple surveys, we investigate the comprehensive properties of the candidates and find that they \thj{are very likely to} belong to one sizable structure, since most of the components are well bridged in their spatial distributions, and follow a single stellar population with an age of $30-40$\,Myr and solar metallicity. This sizable structure is best explained as hierarchically primordial, and probably formed from a filamentary giant molecular cloud with unique formation history in localized regions. To analyze the dynamics of the Snake, we divide the structure into five groups according to their tangential velocities; we find that the groups are expanding at a coherent rate ($\kappa_X\sim3.0\,\times10^{-2}\,\rm km\,s^{-1}\,pc^{-1}$) along the length of the structure ($X$-direction). \thj{The corresponding expansion age ($\tau\sim33$\,Myr) is highly consistent with the age of the Snake}. With over ten thousand member stars, the Snake is an ideal laboratory to study nearby coeval stellar formation, stellar physics, and environmental evolution over a large spatial extent.

astro-ph.GA↗

arXiv2021

Socio-Technological Challenges and Opportunities: Paths Forward

Advancements in digital technologies have a bootstrapping effect. The past fifty years of technological innovations from the computer architecture community have brought innovations and orders-of-magnitude efficiency improvements that engender use cases that were not previously possible -- stimulating novel application domains and increasing uses and deployments at an ever-faster pace. Consequently, computing technologies have fueled significant economic growth, creating education opportunities, enabling access to a wider and more diverse spectrum of information, and, at the same time, connecting people of differing needs in the world together. Technology must be offered that is inclusive of the world's physical, cultural, and economic diversity, and which is manufactured, used, and recycled with environmental sustainability at the forefront. For the next decades to come, we envision significant cross-disciplinary efforts to build a circular development cycle by placing pervasive connectivity, sustainability, and demographic inclusion at the design forefront in order to sustain and expand the benefits of a technologically rich society. We hope this work will inspire our computing community to take broader and more holistic approaches when developing technological solutions to serve people from different parts of the world.

cs.CY↗

arXiv2021

The Deeper, Wider, Faster Program: Exploring stellar flare activity with deep, fast cadenced DECam imaging via machine learning

We present our 500 pc distance-limited study of stellar fares using the Dark Energy Camera as part of the Deeper, Wider, Faster Program. The data was collected via continuous 20-second cadence g band imaging and we identify 19,914 sources with precise distances from Gaia DR2 within twelve, ~3 square-degree, fields over a range of Galactic latitudes. An average of ~74 minutes is spent on each field per visit. All light curves were accessed through a novel unsupervised machine learning technique designed for anomaly detection. We identify 96 flare events occurring across 80 stars, the majority of which are M dwarfs. Integrated are energies range from $\sim 10^{31}-10^{37}$ erg, with a proportional relationship existing between increased are energy with increased distance from the Galactic plane, representative of stellar age leading to declining yet more energetic are events. In agreement with previous studies we observe an increase in flaring fraction from M0 -> M6 spectral types. Furthermore, we find a decrease in the flaring fraction of stars as vertical distance from the galactic plane is increased, with a steep decline present around ~100 pc. We find that ~70% of identified flares occur on short timescales of ~8 minutes. Finally we present our associated are rates, finding a volumetric rate of $2.9 \pm 0.3 \times 10^{-6}$ flares pc$^{-3}$ hr$^{-1}$.

astro-ph.SR↗

arXiv2020

Tracking Measurement Obfuscations from SourceURL

Tracking scripts can use the sourceURL directive to mask their origin from developer tools and tools that use the same JS call stack and network stack information. Firefox and Chromium appear to be affected. Firefox 78 now includes a preference to disable this behavior. This short paper describes the effect when using the OpenWPM measurement platform along with details of discovery.

cs.CR↗

arXiv2020

Actions speak louder than words: Semi-supervised learning for browser fingerprinting detection

As online tracking continues to grow, existing anti-tracking and fingerprinting detection techniques that require significant manual input must be augmented. Heuristic approaches to fingerprinting detection are precise but must be carefully curated. Supervised machine learning techniques proposed for detecting tracking require manually generated label-sets. Seeking to overcome these challenges, we present a semi-supervised machine learning approach for detecting fingerprinting scripts. Our approach is based on the core insight that fingerprinting scripts have similar patterns of API access when generating their fingerprints, even though their access patterns may not match exactly. Using this insight, we group scripts by their JavaScript (JS) execution traces and apply a semi-supervised approach to detect new fingerprinting scripts. We detail our methodology and demonstrate its ability to identify the majority of scripts ($\geqslant$94.9%) identified by existing heuristic techniques. We also show that the approach expands beyond detecting known scripts by surfacing candidate scripts that are likely to include fingerprinting. Through an analysis of these candidate scripts we discovered fingerprinting scripts that were missed by heuristics and for which there are no heuristics. In particular, we identified over one hundred device-class fingerprinting scripts present on hundreds of domains. To the best of our knowledge, this is the first time device-class fingerprinting has been measured in the wild. These successes illustrate the power of a sparse vector representation and semi-supervised learning to complement and extend existing tracking detection techniques.

cs.CR↗

arXiv2019

MLSys: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two.

cs.LG↗

arXiv2017

Abstractions for AI-Based User Interfaces and Systems

Novel user interfaces based on artificial intelligence, such as natural-language agents, present new categories of engineering challenges. These systems need to cope with uncertainty and ambiguity, interface with machine learning algorithms, and compose information from multiple users to make decisions. We propose to treat these challenges as language-design problems. We describe three programming language abstractions for three core problems in intelligent system design. First, hypothetical worlds support nondeterministic search over spaces of alternative actions. Second, a feature type system abstracts the interaction between applications and learning algorithms. Finally, constructs for collaborative execution extend hypothetical worlds across multiple machines while controlling access to private data. We envision these features as first steps toward a complete language for implementing AI-based interfaces and applications.

cs.PL↗

arXiv2016

Making Contextual Decisions with Low Technical Debt

Applications and systems are constantly faced with decisions that require picking from a set of actions based on contextual information. Reinforcement-based learning algorithms such as contextual bandits can be very effective in these settings, but applying them in practice is fraught with technical debt, and no general system exists that supports them completely. We address this and create the first general system for contextual learning, called the Decision Service. Existing systems often suffer from technical debt that arises from issues like incorrect data collection and weak debuggability, issues we systematically address through our ML methodology and system abstractions. The Decision Service enables all aspects of contextual bandit learning using four system abstractions which connect together in a loop: explore (the decision space), log, learn, and deploy. Notably, our new explore and log abstractions ensure the system produces correct, unbiased data, which our learner uses for online learning and to enable real-time safeguards, all in a fully reproducible manner. The Decision Service has a simple user interface and works with a variety of applications: we present two live production deployments for content recommendation that achieved click-through improvements of 25-30%, another with 18% revenue lift in the landing page, and ongoing applications in tech support and machine failure handling. The service makes real-time decisions and learns continuously and scalably, while significantly lowering technical debt.

cs.LG↗

arXiv2010

The Inner Halo of M87: A First Direct View of the Red-Giant Population

An unusually deep (V,I) imaging dataset for the Virgo supergiant M87 with the Hubble Space Telescope ACS successfully resolves its brightest red-giant stars, reaching M_I(lim) = -2.5. After assessing the photometric completeness and biasses, we use this material to estimate the metallicity distribution for the inner halo of M87, finding that the distribution is very broad and likely to peak near [m/H] ~ -0.4 and perhaps higher. The shape of the MDF strongly resembles that of the inner halo for the nearby giant E galaxy NGC 5128. As a byproduct of our study, we also obtain a preliminary measurement of the distance to M87 with the TRGB (red-giant branch tip) method; the result is (m-M)_0 = 31.12 +- 0.14 (d = 16.7 +- 0.9 Mpc). Averaging this result with three other recent techniques give a weighted mean d(M87) = (16.4 +- 0.5) Mpc.

astro-ph.GA↗

arXiv2009

Five More Massive Binaries in the Cygnus OB2 Association

We present the orbital solutions for four OB spectroscopic binaries, MT145, GSC 03161-00815, 2MASS J20294666+4105083, and Schulte 73, and the partial orbital solution to the B spectroscopic binary, MT372, as part of an ongoing study to determine the distribution of orbital parameters for massive binaries in the Cygnus OB2 Association. MT145 is a new, single-lined, moderately eccentric (e=0.291+/-0.009) spectroscopic binary with period of 25.140+/-0.008 days. GSC 03161-00815 is a slightly eccentric (e=0.10+/-0.01), eclipsing, interacting and double-lined spectroscopic binary with a period of 4.674+/-0.004 days. 2MASS J20294666+4105083 is a moderately eccentric (e=0.273+/-0.002) double-lined spectroscopic binary with a period of 2.884+/-0.001 days. Schulte 73 is a slightly eccentric (e=0.169+/-0.009), double-lined spectroscopic binary with a period of 17.28+/-0.03 days and the first "twin" in our survey with a mass ratio of q=0.99+/-0.02. MT372 is a single-lined, eclipsing system with a period of 2.228 days and low eccentricity (e~0). Of the now 18 known OB binaries in Cyg OB2, 14 have periods and mass ratios. Emerging evidence also shows that the distribution of log(P) is flat and consistent with Oepik's Law.

astro-ph.GA↗