Panel Discussion Cognitive Engineering: Will They Know Our Name When We Are 40?

Abstract

This panel discussion will examine the societal awareness of cognitive engineering today. Cognitive engineering celebrated its 30th anniversary in 2018 at the HFES annual meeting. Still, some would say that cognitive engineering is not as well-known as it should be, and that it is applied in an ad hoc manner in the many high-stakes, high-risk technology modernization efforts where it would be useful. As technology advances proliferate for sharp end of the spear decision makers, we are at risk of catastrophic results if CE remains in the shadows; these results are arguably emerging on a daily basis. Each panelist will describe, from their vantage point, CE’s state of the art today, thoughts on barriers to acceptance and application, and how they envision we act towards a future in 2028 in which cognitive engineers engage systematically in complex systems’ development.

Towards Joint Activity Design Heuristics: Essentials for Human-Machine Teaming

“…However, we tried to mitigate this risk by composing statements that would evoke rather than mask the underlying complexity. Ultimately, we believe this operationalization of concepts specifically for operational personnel is a necessary endeavor if the literature on joint cognitive systems is to have the large-scale impact that is desired (Dominguez et al, 2021).…”

“…A growing body of literature from cognitive systems engineering and other related fields is providing guidance beyond usability alone for designing and evaluating AI/ML technologies for human-machine teaming (NASEM, 2021); however, despite forty years of research, this literature has yet to have the large-scale impact desired beyond the human factors community (Dominguez et al, 2021). This shortcoming may in part stem from the wide variety of perspectives, each with their own terminology, from which relevant AI/ML guidelines can be derived, including: joint activity (Klein et al, 2005), teamwork (Feigh & Pritchett, 2014), interdependence (Johnson et al, 2014), situation awareness (Endsley, 2017), macrocognition (Klein et al, 2003;Rayo, 2017), distributed problem solving (Smith, 2018), joint cognitive systems (Woods & Hollnagel, 2006), human-AI interaction (Amershi et al, 2019), and resilience (Woods, 2019).…”

Adaptations to Trust Incidents with Artificial Intelligence

“…where the user may override the AI), and what information may be needed to support effective takeovers. Finally, identifying trust as a function of AI performance in such a representation would allow for better integration of cognitive engineers and AI developers, which has been noted as a challenge (Dominguez et al, 2021). Identifying AI performance (or other) parameters that may result in adding or removing tasks from the user's workflow allows for a direct comparison across the human and computational domains.…”

Where the Cognitive Engineering Rubber Meets the Systems Development Road

“…Recent work has highlighted numerous challenges to translating CE methods and findings into SD products (system requirements or performance specifications, system designs, test and evaluation plans, etc.). Dominguez et al (2021) described some generic challenges in communicating human factors principles to system developers (e.g. program managers, software engineers, and data scientists).…”