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Fallon, Corey K.

Publications and source records attributed to Fallon, Corey K..

18 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Transmission Operator Workflows for Real-Time Reliability Studies: A Review of Control Room Practices and Naturalistic Decision Making

This report provides an overview of real-time reliability study tools and their use by power system operators in the control room environment. After introducing some of the nuances of the control room environment and the differences in perspectives between power system engineers and operators, the roles and responsibilities of key entities involved in RTCA workflows are introduced. These are specifically the transmission system operator (TOP) and reliability coordinator (RC), which are required to run tools such as real-time contingency analysis (RTCA) as part of a real-time reliability assessment every 30 minutes, as dictated by a series of standards issued by the North American Electric Reliability Corporation (NERC). The process by which power systems operators operate the grid is discussed in terms of naturalistic decision making (NDM) and the recognition-primed decision-making (RPD) model. This cognitive model describe how experts working in high-risk, high-stress environments make safety-critical decisions under uncertainty and time pressure. For power system operators, the mental simulations involved in the traditional RPD model are supplemented by physics-based simulations using numerical tools, such as RTCA, to improve situational awareness and effectiveness of control actions. Next, a generic workflow is introduced to describe operator decision making for running RTCA tools and responding to system violations on a pre-contingent basis. The types of analysis performed and control actions chosen by power system operators are described in detail. The overall high-level workflow is then expanded in subsequent sections, with special attention given to high-voltage violations, low-voltage violations, and thermal overloads. Each type of violation is described in detail, with explanations of common causes, impacts on equipment and customers, and mitigation strategies. An additional workflow diagram is provided for each type of violation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

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.

uncertainty, data analytics, interaction, trust, h↗
DOE OSTI2023

HUMAN-ARTIFICIAL INTELLIGENCE TEAMING FOR THE U.S. NAVY: DEVELOPING A HOLISTIC RESEARCH ROADMAP

With the ever-increasing deluge of data and demand for warfighters to make decisions upon its analysis, U.S. defense strategy has prioritized the development of artificial intelligence (AI)/machine learning (ML) systems that can analyze multi-source data streams and suggest courses of action. However, there is a history of systems that have failed to be adopted by the warfighter due not only to unsolved technical challenges, but also a lack of usability or contributions to mission effectiveness, perceived or otherwise. To avoid this, research into human-AI teaming shows promise for developing AI systems that work with frontline operators. A recent National Academies of Sciences, Engineering, and Medicine report (NASEM, 2022) presented 57 research objectives in this area; however, the U.S. Navy requires a more-focused set of priorities, as it is impossible to tackle every priority. A workshop involving 23 human factors scientists, computer scientists, and active-duty sailors was organized at the Naval Information Warfare Center Pacific, resulting in a set of five research priorities spanning near-, mid-, and far-term time frames. This panel will summarize the results of this workshop, with a focus on the big questions both going into this workshop and coming out of it. The panel participants come from government, academia, and industry, providing perspective from the different kinds of organizations required to accomplish these research goals.

Wong, Jason↗
DOE OSTI2023

Developing Confidence in Machine Learning Results

As the field of deep learning has emerged in recent years, the amount of knowledge and expertise that data scientists are expected to absorb and maintain has correspondingly increased. One of the challenges experienced by data scientists working with deep learning models is developing confidence in the accuracy of their approach and the resulting findings. In this study, we conducted semi-structured interviews with data scientists at a National Laboratory to understand the processes that data scientists use when attempting to develop their models and the ways that they gain confidence that the results they obtained were accurate. These interviews were analysed to provide an overview of the techniques currently used when working with machine learning (ML) models and opportunities for collaboration with human factors researchers to develop new tools are identified.

Baweja, Jessica A.↗
DOE OSTI2023

Opportunities for human factors in machine learning

Introduction The field of machine learning and its subfield of deep learning have grown rapidly in recent years. With the speed of advancement, it is nearly impossible for data scientists to maintain expert knowledge of cutting-edge techniques. This study applies human factors methods to the field of machine learning to address these difficulties. Methods Using semi-structured interviews with data scientists at a National Laboratory, we sought to understand the process used when working with machine learning models, the challenges encountered, and the ways that human factors might contribute to addressing those challenges. Results Results of the interviews were analyzed to create a generalization of the process of working with machine learning models. Issues encountered during each process step are described. Discussion Recommendations and areas for collaboration between data scientists and human factors experts are provided, with the goal of creating better tools, knowledge, and guidance for machine learning scientists.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Phishing in the Wild: An Ecologically Valid Study of the Phishing Tactics and Human Factors that Predict Susceptibility to a Phishing Attack

In this research, 153 employees at a National Laboratory received one of one of four different phishing emails. All of the emails were similar in content, but systematically varied according to the number and combination of phishing tactics in the message. Participants were unaware they would be receiving the email, which was sent during regular business hours. After receiving the emails, participants completed online questionnaires designed to measure possible predictors of phishing attack susceptibility. Finally, the significant predictors included how suspicious participants were of the email and their reported level of distress related to their work prior to completing the study.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An Analysis of Grid Operator Survey Responses: Inexperience, Workload and Fatigue in the Control Room

Although a wide array of tools and technologies have been developed over the last decade to support power grid operators, deployment of these tools has been less successful. One reason for unsuccessful deployment may be an inadequate understanding of the factors that contribute to operator error in the control room. An analysis of operators’ current vulnerabilities may provide the baseline understanding needed to inform new technology integration. In an attempt to learn more about these vulnerabilities and their perceived impact on human error we collected and analyzed survey data from 20 electric grid control room operators. We asked survey respondents to consider the various operator, technology and interaction vulnerabilities that may arise during work in the control room and record their attitudes and experiences toward each. Results suggest operator inexperience, high mental workload and fatigue are the most common vulnerabilities experienced during a shift. Survey results were analyzed to explore these vulnerabilities in greater depth.

Inexperience, Workload, Fatigue↗
DOE OSTI2022

A Real-Time Operations Manual for the IEEE 118 Bus Transmission Model

This document presents a sample operations manual for the IEEE 118 Bus Model, which is a synthetic test case developed in 1962 from a section of the transmission grid operated by American Electric Power (AEP). The model is one of the most used synthetic test cases for development of power system applications and is referenced by over 9000 papers. However, the model lacks any context for use with real-time energy management system (EMS) applications, including common considerations, such as generator ramp rates, reactive capabilities, operating limits, and other information typically used by power system operators for real-time decision making. This manual divides the IEEE 118 Bus Model into three operating areas, defines various operating limits, and sets recommended operating procedures for responding to a few types of emergency operating conditions. The manual can be used in support of a wide variety of human-in-the-loop evaluation methodologies for new advanced power applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Method for Generating Expert Derived Confidence Scores

We executed a pilot demonstration of a methodology for developing a new confidence metric to help operators calibrate their trust in ML event classifiers. This confidence metric was derived from domain expert judgment and was accompanied with a qualitative description describing the reason for each confidence rating. After learning the boundaries of an ML’s performance by studying a subset of events an SME rated his confidence in the ML’s ability to classify similar events and provided an explanation for his ratings. To demonstrate this methodology, we developed our expert driven confidence scores for the ML event classifier within the ESAMS. Next, we assessed the accuracy of the human expert confidence scores relative to the ML’s uncertainty quantification scores. This report includes a description of our methodology, summary of our findings and future directions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Operator Insights and Usability Evaluation of Machine Learning Assistance for Power Grid Contingency Analysis

Introducing machine learning (ML) assistance into any established process comes with adoption barriers, including entrenched procedures, technological and human readiness levels, human-machine trust, and work culture resistance to change. These barriers are even greater in critical operations such as operating a national or regional power grid, in which both regulatory frameworks and the importance of maintaining reliability levels causes additional resistance to the adoption of new computational support. Developers of future systems and job aides must consider not only technical aspects, but also whether new systems are usable by power system operators. This work presents the methodology and results of a study to evaluate the usability and readiness of a prototype recommender system for power grid contingency analysis. We explore operator cognitive load and evaluate operator performance when solving a collection of scenarios both with and without recommender assistance. We also examine operator trust in the system. We report insights gained on the readiness of the system using a collection of evaluation techniques.

Human-Machine Teaming, Power Systems, usability ev↗
DOE OSTI2022

A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis

This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real-time power grid simulation. The goal of this framework is to rapidly collect and analyze a broad variety of human factors data in order to accelerate the development and evaluation loop for deploying machine learning applications. We describe our methodology and analysis, and we discuss insights gained from a pilot participant about the current usability state of an early technology readiness level (TRL) artificial neural network (ANN) recommender.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

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.

Sensemaking, human-machine teaming, agency, artifi↗
DOE OSTI2021

A Methodology for Assessing Risk to Inform Technology Integration

When new technology, such as artificial intelligence (AI), is introduced into an existing workflow it may impact risk by mitigating some vulnerabilities and threats in the workflow while introducing others. We present a versatile methodology for assessing the vulnerabilities and threats that impact overall risk in a workflow to inform technology integration. Our method involves both qualitative and quantitative assessment of risk and includes a formula for generating a risk score to guide technology integration. We describe our methodology and demonstrate its application to a specific workflow (the Derivative Classification review process). This work was funded by the Department of Energy (DOE) Automated Classification Tools for the Identification of Classified Information (ACTICI) program.

risk, technology integration↗
DOE OSTI2021

Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation

Trust calibration for a human–machine team is the process by which a human adjusts their expectations of the automation’s reliability and trustworthiness; adaptive support for trust calibration is needed to engender appropriate reliance on automation. Herein, we leverage an instance-based learning ACT-R cognitive model of decisions to obtain and rely on an automated assistant for visual search in a UAV interface. This cognitive model matches well with the human predictive power statistics measuring reliance decisions; we obtain from the model an internal estimate of automation reliability that mirrors human subjective ratings. The model is able to predict the effect of various potential disruptions, such as environmental changes or particular classes of adversarial intrusions on human trust in automation. Finally, we consider the use of model predictions to improve automation transparency that account for human cognitive biases in order to optimize the bidirectional interaction between human and machine through supporting trust calibration. The implications of our findings for the design of reliable and trustworthy automation are discussed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

A Context-Centered Research Approach to Phishing and Operational Technology in Industrial Control Systems

Advanced persistent threats (APT’s) that leverage phishing against operational technology (OT) are cyber-attacks that endanger critical infrastructure assets nationwide. Today phishing, a human focused exploit, constitutes 91% of successful attack vectors against Federal assets. This means Human Introduced Vulnerabilities (HICV’s) are the weakest cyber defense link. The success of these attacks also suggests HICV’s are not well understood nor mitigated. To characterize HICV’s and provide the necessary context in which they exist, this paper introduces a research approach derived from the mature science of Social Ecology. The desired end result of this research is an HICV focused risk assessment framework.

Advanced Persistent Threats↗
DOE OSTI2019

Opportunities and Challenges for Human-Machine Teaming in Cybersecurity Operations [Panel Discussion]

Many of the challenges associated with cybersecurity operations are also ripe opportunities for the application of human-machine teaming. Advances in cognitive science, artificial intelligence, and machine learning promise the technology to achieve the level of intelligence and sophistication necessary to create a true intelligent teammate. This panel gathers experts from across the community to discuss the challenges and opportunities for human-machine teaming in cybersecurity operations.

Cybersecurity, Human-Machine Teaming↗
DOE OSTI2018

Improving Automation Transparency: Addressing Some of Machine Learning’s Unique Challenges

Abstract. A variety of factors can affect one’s reliance on an automated aid. Some of these factors include one’s per-ception of the system’s trustworthiness, such as perceived reliability of the system or one’s ability to understand the system’s underlying reasoning. A mismatch between the operator’s perception and the true capabilities and charac-teristics of the system can lead to inappropriate reliance on the tool. This improper use of the system can manifest as either underutilization of the technology or complacency resulting from over-trusting the system. Increasing an au-tomated tool’s transparency is one approach that enables the operator to more appropriately rely on the technology. Transparent automated systems provide additional infor-mation that allows the user to see the system’s intent and understand its underlying processes and capabilities. Sev-eral researchers have developed frameworks to support the design of more transparent automation. However, these frameworks may not fully consider the particular challeng-es to transparency design introduced by automation that leverages machine learning. Like all automation, these systems can benefit from transparency. However, artificial intelligence poses new challenges that must be considered when designing for transparency. Unique considerations must be made in terms of the type, and amount or level of transparency information conveyed to the user.

Transparency, Machine Learning, Automation↗