Combining Process Tracing and Policy Capturing Techniques for Judgment Analysis in an Anti-Submarine Warfare Simulation

2021 ◽  
Vol 65 (1) ◽  
pp. 1557-1561
Author(s):  
Katherine Labonté ◽  
Daniel Lafond ◽  
Bénédicte Chatelais ◽  
Aren Hunter ◽  
Folakemi Akpan ◽  
...  
Keyword(s):  
Decision Support Tool ◽  
Machine Learning Algorithms ◽  
Process Tracing ◽  
Policy Capturing ◽  
Support Tool ◽  
Human Decision ◽  
Model Training ◽  
Decision Policies ◽  
Available Information

The Cognitive Shadow is a prototype decision support tool that can notify users when they deviate from their usual judgment pattern. Expert decision policies are learned automatically online while performing one’s task using a combination of machine learning algorithms. This study investigated whether combining this system with the use of a process tracing technique could improve its ability to model human decision policies. Participants played the role of anti-submarine warfare commanders and rated the likelihood of detecting a submarine in different ocean areas based on their environmental characteristics. In the process tracing condition, participants were asked to reveal only the information deemed necessary, and only that information was sent to the system for model training. In the control condition, all the available information was sent to the system with each decision. Results showed that process tracing data improved the model’s ability to predict human decisions compared to the control condition.

scholarly journals Implementing machine learning algorithms for suicide prevention: Designing a Clinical Decision Support tool in partnership with Native American care providers (Preprint)

2020 ◽  
Author(s):  
Emily Haroz ◽  
Fiona Grubin ◽  
Novalene Goklish ◽  
Shardai Pioche ◽  
Mary Cwik ◽  
...  
Keyword(s):  
Native American ◽  
Risk Prediction ◽  
Suicide Risk ◽  
Clinical Care ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Case Managers ◽  
Machine Learning Algorithms ◽  
Support Tool ◽  
Appropriate Care

BACKGROUND Machine learning algorithms for suicide risk prediction have been developed with notable improvements in accuracy. Implementing these algorithms to enhance clinical care and reduce suicide has not been well studied. OBJECTIVE Our study aimed to design a Clinical Decision Support tool (CDS) and appropriate care pathways for a community-based suicide surveillance and case management systems operating on Native American reservations. METHODS Participants included Native American case managers and supervisors (N = 9) who work on suicide surveillance and case management programs on two Native American reservations. We used in-depth interviews to understand how case managers think about and respond to suicide risk. Results from interviews informed a draft CDS tool, which was then reviewed with supervisors and combined with appropriate care pathways. RESULTS Case managers reported acceptance of risk flags based on a predictive algorithm in their surveillance system tools, particularly if the information was available in a timely way and used in conjunction with their clinical judgement. Implementation of risk flags needed to be programmed on a dichotomous basis so the algorithm could produce output indicating high vs. low risk. To dichotomize the continuous predicted probabilities, we developed a cutoff point that favored specificity, with the understanding that case managers’ clinical judgment would help increase sensitivity. CONCLUSIONS Suicide risk prediction algorithms show promise, but implementation to guide clinical care has remained relatively elusive. Our study demonstrates the utility of working with partners to develop and guide operationalization of risk prediction algorithms to enhance clinical care in a community setting.

scholarly journals Neural Network-Based Aircraft Conflict Prediction in Final Approach Maneuvers

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1708
Author(s):  
Rafael Casado ◽  
Aurelio Bermúdez
Keyword(s):  
Neural Network ◽  
Traffic Management ◽  
Autonomous Navigation ◽  
Decision Support Tool ◽  
Air Traffic ◽  
Navigation Systems ◽  
Support Tool ◽  
Final Approach ◽  
Aircraft Trajectories ◽  
Available Information

Conflict detection and resolution is one of the main topics in air traffic management. Traditional approaches to this problem use all the available information to predict future aircraft trajectories. In this work, we propose the use of a neural network to determine whether a particular configuration of aircraft in the final approach phase will break the minimum separation requirements established by aviation rules. To achieve this, the network must be effectively trained with a large enough database, in which configurations are labeled as leading to conflict or not. We detail the way in which this training database has been obtained and the subsequent neural network design and training process. Results show that a simple network can provide a high accuracy, and therefore, we consider that it may be the basis of a useful decision support tool for both air traffic controllers and airborne autonomous navigation systems.

scholarly journals Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff

2019 ◽  
Vol 33 ◽  
pp. 2429-2437 ◽  
Author(s):  
Gagan Bansal ◽  
Besmira Nushi ◽  
Ece Kamar ◽  
Daniel S. Weld ◽  
Walter S. Lasecki ◽  
...  
Keyword(s):  
Team Performance ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Behavioral Changes ◽  
High Stakes ◽  
Human Decision ◽  
Classification Tasks ◽  
Prior Experiences ◽  
Training Objective

AI systems are being deployed to support human decision making in high-stakes domains such as healthcare and criminal justice. In many cases, the human and AI form a team, in which the human makes decisions after reviewing the AI’s inferences. A successful partnership requires that the human develops insights into the performance of the AI system, including its failures. We study the influence of updates to an AI system in this setting. While updates can increase the AI’s predictive performance, they may also lead to behavioral changes that are at odds with the user’s prior experiences and confidence in the AI’s inferences. We show that updates that increase AI performance may actually hurt team performance. We introduce the notion of the compatibility of an AI update with prior user experience and present methods for studying the role of compatibility in human-AI teams. Empirical results on three high-stakes classification tasks show that current machine learning algorithms do not produce compatible updates. We propose a re-training objective to improve the compatibility of an update by penalizing new errors. The objective offers full leverage of the performance/compatibility tradeoff across different datasets, enabling more compatible yet accurate updates.

scholarly journals Selection of the Best Proposal using FAHP: Case of Procurement of IT Master Plan’s Realization

2017 ◽  
Vol 7 (1) ◽  
pp. 353
Author(s):  
Amadou Diabagate ◽  
Abdellah Azmani ◽  
Mohamed El Harzli
Keyword(s):  
Computer Systems ◽  
Decision Support Tool ◽  
Master Plan ◽  
Ahp Method ◽  
Support Tool ◽  
Analysis And Evaluation ◽  
Master Plans ◽  
Published Paper ◽  
Selection Of

IT master plan, which allows planning and managing the development of the computer systems, derives its importance in the central role of the computer systems in the functioning of organizations. This article focuses on the use of FAHP method for analysis and evaluation of tenders during the awarding of contracts of IT master plan’s realization. For those purposes, a painstaking work was realized for making an inventory of criteria and sub-criteria involved in the evaluation of tenders and for specifying the degrees of preference for each pair of criteria and sub-criteria. To find a provider for the IT master plan’s realization, organizations are increasingly using tendering as the mode of awarding contracts. This paper is an improvement of a previous published paper in which AHP method was used. The goals of this work are to make available to members of tenders committee a decision support tool for evaluating tenders of IT master plan’s realization and endow the organizations with effective IT master plans in order to increase their information systems’ performance.

scholarly journals Profiling waitlisted incoming students for future delinquency with an ensemble of statistical machine learning algorithms

2017 ◽  
Author(s):  
Maureen Lyndel C Lauron ◽  
Jaderick P Pabico
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Decision Support Tool ◽  
Machine Learning Algorithms ◽  
Statistical Machine Learning ◽  
Training Set ◽  
Support Tool ◽  
Incoming Freshman ◽  
Validation Set ◽  
Incoming Students

Given a dataset \(\mathcal{R}=\{R_1, R_2, \dots, R_r\}\) of \(r\)~records of waitlisted incoming freshman students (WIFS), where for any \(i=1, 2, \dots, r\), \(R_i\) is a \((m+1)\)--tuple \((O_i, P_i^{(1)}, P_i^{(2)}, \dots, P_i^{(m)})\), \(O_i\) is any one in a set \(\mathcal{O}=\{O_1, O_2, \dots, O_o\}\) of \(o\)~classes, and \(P_i^{(1)}, P_i^{(2)}, \dots, P_i^{(m)}\) are \(m\)~potential predictors for~\(O_i\). Our purpose is to find a statistical machine learning algorithm (SMLA) \(\mathbb{A}\) such that \(V_i=\mathbb{A}(P_i^{(1)}, P_i^{(2)}, \dots, P_i^{(m)})\), where \(V_i\) is a predicted class by~\(\mathbb{A}\) that was developed using \(n\le m\) correct number of predictors for \(O\in\mathcal{O}\), and \(\mathbb{A}\)~is the best algorithm such that the metric \(v^{-1}\sum_{i=1}^v |O_i - V_i|\) is minimum across \(v<r\)~records in the validation set \(\mathcal{V}\subset\mathcal{R}\). Our problem is to find the subset \(\{P_i^{(1)}, P_i^{(2)}, \dots, P_i^{(n)}\}\) and to train \(\mathbb{A}\)~using \(t<r\) records from the training set \(\mathcal{T}\subset\mathcal{R}\), such that \(\mathcal{T}\cap\mathcal{V}=\emptyset\), so that \(\mathbb{A}\)~can predict whether a WIFS trying to enter an undergraduate program at UPLB will incur at least a ``delinquency'' once the student is accepted into the program. The \(\mathbb{A}\)~can be a useful decision-support tool for UPLB deans and college secretaries in deciding whether a WIFS will be accepted into the program or not.

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