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“Shoulda, Coulda, Woulda”: Conceptualizing the Differences in Trust Between Human-Human Teaming and Human-Machine Teaming
Intelligent decision support systems (IDSSs) are machine teammates designed to facilitate better human decision-making in high-consequence domains such as health care, power grid operations, and fraud detection. IDSSs identify patterns in datasets and provide intelligent decision-making recommendations to human teammates. However, previous research indicates that humans often trust IDSS recommendations less than the recommendations from their human teammates, even when the machine teammate is more accurate. To conceptualize why trust differs, we review the literature surrounding trust, error, and predictability. Then, we compile and compare participant trust ratings and decision-making in an abridged systematic review of previous studies manipulating teammate type, error rate, and error type. Finally, we conduct a content analysis of participants’ qualitative responses to trust queries from a survey on generative language models. Results suggest that humans may trust IDSS teammates less than other human teammates because of differences in (1) interaction complexity, (2) blame attribution, and (3) swift trust. We conclude that human factors practitioners should collaborate with data scientists and domain experts to build and maintain trust in IDSSs by anthropomorphizing algorithms, matching mental models, and considering individual differences.
A Data-driven Approach to Rethinking Open Source Software Organizations as a Team of Teams.
Abstract not provided.
ESGF project plan for ramping down development activities and transition the project and data to a new team
This document summarizes the current software development activities carried by the ESGF team with planned delivery dates before the transition of the project to a new team. The work we started on the publishing, search and retrieval, and the backend for indexing and data management that can be deployed using Kubernetes are planned to be delivered within the next three months; however, this assumes that the current team members will continue to work on the project. The activities are listed in Table 1 by priority and in case we lose team members, development of lower priority tasks can be paused and passed to the new team with its current status. Team members can be re-assigned based on their availability and transition work needed. Table 2 below describe activities necessary to help project transition to the new team. Deadlines for these activities are before October 1 st of 2022 or the completion of a successful transition. Support during the transition period assumes that we will have 3 key ESGF members and the ESGF PI supported at the 50% level or less for the duration of the transition. The level of support is an estimate, and it depends on several factors that cannot be decided at this time. The total cost of the proposed work and support activities for fiscal year 2021 is $1,270K with $230K carry over to fiscal year 2022. The cost for the support work listed in Table 2 is the sum of the two rows “HW support” and “project support, maintenance, and transition efforts”, colored blue, in Table1 which adds up to $720K. This cost is included in the $1,270K cost in table 1.
Characterizing Interaction Uncertainty in Human-Machine Teams
With the increasing use and adoption of artificial intelligence (AI), the reliability of modern data systems will be driven by a tighter teaming between human experts and intelligent machine teammates. As in the case of human-human teams, the success of human-machine teams will also rely on clear communication about mutual goals and actions. In this paper, we combine related literature from cognitive psychology, human-machine teaming, uncertainty in data analysis, and multi-agent systems to propose a new form of uncertainty: interaction uncertainty for characterizing bidirectional communication in human-machine teams. We map the causes and effects of interaction uncertainty and outline potential ways to mitigate uncertainty for mutual trust in a high-consequence real-world scenario.
Automating Software Productivity Planning: Lightweight Tools for Upgrading Team Practices
A lightweight iterative workflow, called Productivity and Sustainability Improvement Planning (PSIP) has been developed to assist software teams with employing good software engineering practices. Three PSIP tools automate practice summarization, self-assessment, and progress tracking to enhance team engagement around software process improvement: Reposcanner, RateYourProject (RYP) and Progress Tracking Cards (PTCs) respectively. Reposcanner is a data analysis tool. RYP is an interactive self-assessment tool that provides an easy-to-use entry point to the PSIP process. Once process improvements are identified, PTCs are created by teams to measure outcomes and progression toward their goals. We present an exemplifying case study with a software team where these tools have been put to use, along with a description of the tools, and future plans for further improvements.
Beyond Visual Analytics: Human-Machine Teaming for AI-Driven Data Sensemaking
"Detect the expected, discover the unexpected" was the founding principle of the field of visual analytics. This mantra implies that human stakeholders, like a domain expert or data analyst, could leverage visual analytics techniques to seek answers to known unknowns and discover unknown unknowns in the course of the data sensemaking process. We argue that in the era of AI-driven automation, we need to recalibrate the roles of humans and machines (e.g., a machine learning model) as teammates. We posit that by realizing human-machine teams as a stakeholder unit, we can better achieve the best of both worlds: automation transparency and human reasoning efficacy. However, this also increases the burden on analysts and domain experts towards performing more cognitively demanding tasks than what they are used to. In this paper, we reflect on the complementary roles in a human-machine team through the lens of cognitive psychology and map them to existing and emerging research in the visual analytics community. We discuss open questions and challenges around the nature of human agency and analyze the shared responsibilities in human-machine teams.
SaS4D Home Team UI (SaS4D-HT-UI) v1.0
The SaS4D Home Team UI (python) is a software to view and interact with different layers of 3D geometries and generate usable MCNP-style input file. It is used by the remote Home Team in providing guidance and building models of environments they have never seen in order to investigate threat object discovered at the Working Point. The UI visualizes a colorized mesh, a semantic labelled mesh, and a semantic labelled probability mesh of the scanned environment as well as individual water-tight material-labeled objects. It allows for manipulation and re-processing of these objects. The UI also contains measurement tools to facilitate better MCNP input file generation in the manipulation workflow. The software is a key component in ensuring the Home Team has prompt awareness of the Working Point.
Winning Asset Management Improvement Team: Maintenance Planning and Scheduling in a Highly Regulated Environment
The Y-12 National Complex (Y-12) site has numerous aging facilities that are crucial to the Department of Energy and the national security strategy for the nation. Y-12’s commitment to safety and regulatory compliance is of the highest importance. The commitment to meet the national security mission also creates additional rigor and complexity to the everyday maintenance and planning process. Y-12 is a collection of many facilities, both old and new, nestled between two ridges in Oak Ridge, TN. Y-12 was made with the short-term focus of ending “The Great War” through the creation of the worlds’ first atomic bomb. Almost eighty (80) years have passed since the groundbreaking, with the mission of the site changing from decade to decade. While the mission has changed, the way Y-12 employees continuously meet the challenge has not. The site was created to react and overcome; Y-12 still takes pride in the ability to react and overcome. The difference is the site is no longer ignorant to the need for a better way to manage the aging facilities and infrastructure. Shear willpower and determination was once the way to reach the objectives, but as a wise man once said, “Work smarter, not harder.” The business case for change started within the senior leadership at Y-12. A team of managers sat down and dictated objectives to provide a clear scope for the maintenance planning and scheduling optimization team, to include our Eruditio integrated blended learning coaches. In addition to providing the direction, they also made themselves available for escalation of issues in the event the team ran in to road blocks.
TEAM Project Review, Year 2
This report summarizes our research activities within the TEAM project between December 2020 and December 2021, funded by the ASCR Advanced Research in Quantum Computing program. During the reporting period the LLNL-MSU team has made progress on several fronts. An overarching goal of the team is to provide a comprehensive suite of software tools that can be used for the Characterize-Optimize-Compute loop needed to implement and execute algorithms on quantum devices. We are concurrently developing lightweight solvers that can be used on desktop computers to find optimal control pulses and to characterize small quantum systems (consisting of a few transmons and cavities). However, desktop computers are insufficient for simulating and characterizing larger quantum systems. We have therefore also developed parallel, distributed memory, simulators and optimization solvers, both for open and closed quantum systems. These parallel solvers have, for example, been used to study quantum optimal control for pure-state preparation, utilizing 1000’s of cores on a modern high-performance computing (HPC) platform.
Bob Geyer: Coaching wrestling or clearing waste, he puts his team first
When Bob’s team started the project of clearing drums out of the PF-4 basement, there were 1,800 drums inside. Now, there are less than 500. It’s no coincidence that Bob Geyer leads a team that goes by the acronym of MVP — move, validate and prepare — more commonly recognized as “most valuable player.” Bob and the MVP team manage waste drums after they’ve been packaged. Their job is to constantly work a puzzle of drums and locations based on the contents of the drums, where they came from and where they’re headed. All the while, they’re ensuring there’s enough floor space in the Plutonium Facility for the waste materials that come out of daily production operations.
Our teams design high-resolution MeV x-ray radiography systems for National security missions [Poster]
The NNSS has been at the forefront of custom high-fidelity x-ray/gamma radiographic imaging solutions that serve our national security for over five decades. Our radiography team utilizes expertise in physics modeling and analysis, along with optical, mechanical, and electrical design, in close collaboration with our customers, to develop imaging methods and capabilities that go beyond their needs. Conceptual designs are developed through R&D efforts to provide solutions for the specific problem at hand. Field systems are designed and built in-house, then qualified utilizing a range of facilities across the National Security Enterprise. Radiographic imaging systems are deployed by our team in the most challenging environments. The NNSS has a strong math and programming team that provides novel on-site image analysis methods that extract crucial information from data returned in field tests.
LLE Diagnostic Resource Team for Innovative Fusion Concepts (Final Report)
The LLE Diagnostic Resource Team for Innovative Fusion Concepts provided travelling neutron diagnostics, consultancy on neutron diagnostics, and consultancy on tritium handling to fusion companies. An existing neutron detector was recalibrated for low yields and two detectors were built based on designs proven on the Omega Laser Facility. The three detectors allow measurements over a wide range of yields (from 10 to 1E7 incident neutrons). The detectors were calibrated for DD neutrons on a dedicated OMEGA shot day. Neutron detectors from the company MIFTI were also calibrated on OMEGA. MIFTI is currently using their detectors and two of our detectors on staged Z-pinch experiments at UCSD. The Team acted as consultants for the development of a liquid scintillator array to detect neutrons from muon induced fusion by NK Labs. Segments of NK Labs’ detector were tested at the LLE using radioactive sources and a DD neutron generator. The Team also acted as consultants for the design of a tritium handling system for NK Labs. A new type of neutron spectrometer was designed and built, and has completed preliminary testing.
Data Steward Team Wall-to-Wall Inventory Status Update October - December 2023 [Slides]
7 Wall-to-wall inventories were completed between October 2 – December 11, 2023. The team viewed 12 technical areas. A total of 280 containers were verified against current WCATS data. We worked with WMCs team to schedule for all waste throughout WFO FOD. WMCs are doing an incredible job with keeping up with all their locations; The team only found one discrepancy that was resolved the same day, and The WMC also took the time to explain their process on how containers are labeled with closure days and why it was labeled as such.
Data Steward Team Wall-To-Wall Inventory Status Update July - September 2023 [Slides]
8 Wall-to-wall inventories were completed between July 3rd – September 20th, 2023. The scheduling was broken down by FODs, and the team completed UI and IF. The team viewed 12 sites across 7 technical areas. A total of 249 containers were verified against current WCATS data. 2 containers were unable to be verified due to lack of WMC response. We worked with WMCs and Environmental Stewardship teams this quarter to schedule the wall-to-wall inventories. Discrepancies that were found were fixed by the WMCs in a timely manner. Most of the discrepancies were Labeling issues which were resolved the day of the walk down or within a week.
Tough Errors Are no Match (TEAM): Optimizing the Quantum Compiler for Noise Resilience
This report summarizes research performed under the Tough Errors Are no Match (TEAM) project. The primary focus of TEAM has been to research and develop a compilation toolbox leveraging techniques from quantum characterization and control, probabilistic programming, and approximate computing. Our goal was to develop robust protocols that can be integrated into quantum compilers to optimize and enhance the robustness of noisy computation. Here, we provide a summary of TEAM work focused on characterization and control of quantum systems.
Fermilab 2025 Summer Internship Skills Learned from Working with Mu2e Tracker Team
As part of a DOE RENEW Grant, the undergraduate authors interned at Fermilab. They spent nine weeks over summer 2025 working on the tracker team for the Mu2e experiment. They gained practical experience in a multitude of new skills including electronics installation, testing, and repairs. Their work particularly increased productivity by helping repair the high voltage and calibration pre-amplifiers, over 20,000 of which are needed in the tracker. These components are very fragile and often break during the installation process. The experience also taught them several soft skills such as the importance of thoughtful data storage, record- keeping, and problem-solving skills. They learned the demands of a large, international collaboration and how to work in a team. The authors would like to acknowledge their advisor and PI of the grant, Dr. Christopher G. Fasano, and the Mu2e team lead by co- spokesperson Dr. Bob Bernstein and tracker L2 manager Dr. Brendan Kiburg.