Human Factors Contributions to Knowledge Elicitation

Hoffman, Robert R.

doi:10.1518/001872008x288475

Cited by 17 publications

(11 citation statements)

References 58 publications

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“…A possible mitigation could be to further increase safety assessment competence at operational level. The present study suggests that this is easier to do by establishing knowledge about the Resilience Engineering principles than with traditional safety assessment vocabulary, because the resilience principles help describe operators' everyday work (as describing tacit knowledge of expert operators is critical but difficult [21,41,40,58]). The method developed here seems to support that observations are collected, sorted, analyzed and taken care of, as close as possible to the operational environment, in order to be proactive to detect variation in operations before, during and after implementation of new design.…”

Section: Discussionmentioning

confidence: 99%

Towards understanding work-as-done in air traffic management safety assessment and design

Woltjer

Pinska-Chauvin

Laursen³

et al. 2015

Reliability Engineering & System Safety

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Section: Discussionmentioning

confidence: 99%

Towards understanding work-as-done in air traffic management safety assessment and design

Woltjer

Pinska-Chauvin

Laursen³

et al. 2015

Reliability Engineering & System Safety

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“…CTA is a process of understanding cognition while performing complex tasks. It provides a mechanism for eliciting and representing general and specific knowledge [54-56]. Team CTA is an extension of CTA that considers a team as a single cognitive entity (eg, more than a collection of individuals) [57,58].…”

Section: Methodsmentioning

confidence: 99%

Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design

Roosan¹,

Law²,

Karim³

et al. 2019

JMIR Res Protoc

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BackgroundAccording to the September 2015 Institute of Medicine report, Improving Diagnosis in Health Care, each of us is likely to experience one diagnostic error in our lifetime, often with devastating consequences. Traditionally, diagnostic decision making has been the sole responsibility of an individual clinician. However, diagnosis involves an interaction among interprofessional team members with different training, skills, cultures, knowledge, and backgrounds. Moreover, diagnostic error is prevalent in the interruption-prone environment, such as the emergency department, where the loss of information may hinder a correct diagnosis.ObjectiveThe overall purpose of this protocol is to improve team-based diagnostic decision making by focusing on data analytics and informatics tools that improve collective information management.MethodsTo achieve this goal, we will identify the factors contributing to failures in team-based diagnostic decision making (aim 1), understand the barriers of using current health information technology tools for team collaboration (aim 2), and develop and evaluate a collaborative decision-making prototype that can improve team-based diagnostic decision making (aim 3).ResultsBetween 2019 to 2020, we are collecting data for this study. The results are anticipated to be published between 2020 and 2021.ConclusionsThe results from this study can shed light on improving diagnostic decision making by incorporating diagnostics rationale from team members. We believe a positive direction to move forward in solving diagnostic errors is by incorporating all team members, and using informatics.International Registered Report Identifier (IRRID)DERR1-10.2196/16047

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“…Taking this areas forward will likely require building upon and rethinking traditional knowledge elicitation techniques. Effective knowledge elicitation, as part of the hot topic of knowledge acquisition in heady days of expert systems, was thought to be a foundational building block of AI [15,34]. With the inductive, data-driven turn, we may need to rediscover it as something which constrains and augments patterns learned from data, less around tough or rare cases [35] as much as around contentious, value-laden ones.…”

Section: 'Always a Person Involved': Augmenting Outputsmentioning

confidence: 99%

Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making

Veale

Kleek

Binns

2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

303

223

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Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions-like taxation, justice, and child protection-are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and 'discrimination-aware' machine learning-absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the 'street-level bureaucrats' on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications.

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Human Factors Contributions to Knowledge Elicitation

Abstract: I discuss some open issues for further research and methodological investigation.

Cited by 17 publications

References 58 publications

Towards understanding work-as-done in air traffic management safety assessment and design

Towards understanding work-as-done in air traffic management safety assessment and design

Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design

Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making

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