TY - GEN
T1 - Design-engineers’ selection of agency
T2 - ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2022
AU - Ballestas, Caseysimone
AU - Lanoy, Jusuël
AU - Janssens, Jules
AU - Kim, Euiyoung
PY - 2022
Y1 - 2022
N2 - The computing paradigm where sensor and actuator technology work in tandem to track and act on events in real Euclidean space, known as ambient intelligence (AmI), is likely to become increasingly common due to the rapid maturation of computing technology. Installing AmI in the built environment creates ambient intelligent environments (AmIE), which strive to make the places we inhabit (invisibly) sensitive and responsive to our presence, needs, wants, and preferences. Given that built environments and the goings-on therein are complicated in an of them selves, implementing AmI for (increasingly) complicated tasks in (increasingly) complicated scenarios, increases the difficulty of managing the outcomes in AmIEs. Our previous research indicates that industry practitioners attribute the agency of AmI artifacts as responsible for these outcomes; especially when harm perpetuation is (one of) the outcome(s), which we codified as the Agency/Intelligence Axis [1]. Due to the nascence of AmI, research on best practices for the design-engineering of AmI is still emerging. This research seeks to add to this literature by evaluating our formerly identified Agency/Intelligence Axis in the context of AmIE through a case study of VyZee, a retail company working on transitioning their retail stores to “smart” stores. Our findings highlight that while VyZee seems largely unaware of any relationship between agency and perpetuating un-anticipated/-desired outcomes, they do implement an array of levels of AmI agency in their retail stores, and their justifications for their choices are presented in the discussion. Finally, coding the data revealed more nuance then previously documented in the Agency/Intelligence Axis, and a new Ambient Intelligent Agent Model, which suggests that AmI agents have six properties, is proposed.
AB - The computing paradigm where sensor and actuator technology work in tandem to track and act on events in real Euclidean space, known as ambient intelligence (AmI), is likely to become increasingly common due to the rapid maturation of computing technology. Installing AmI in the built environment creates ambient intelligent environments (AmIE), which strive to make the places we inhabit (invisibly) sensitive and responsive to our presence, needs, wants, and preferences. Given that built environments and the goings-on therein are complicated in an of them selves, implementing AmI for (increasingly) complicated tasks in (increasingly) complicated scenarios, increases the difficulty of managing the outcomes in AmIEs. Our previous research indicates that industry practitioners attribute the agency of AmI artifacts as responsible for these outcomes; especially when harm perpetuation is (one of) the outcome(s), which we codified as the Agency/Intelligence Axis [1]. Due to the nascence of AmI, research on best practices for the design-engineering of AmI is still emerging. This research seeks to add to this literature by evaluating our formerly identified Agency/Intelligence Axis in the context of AmIE through a case study of VyZee, a retail company working on transitioning their retail stores to “smart” stores. Our findings highlight that while VyZee seems largely unaware of any relationship between agency and perpetuating un-anticipated/-desired outcomes, they do implement an array of levels of AmI agency in their retail stores, and their justifications for their choices are presented in the discussion. Finally, coding the data revealed more nuance then previously documented in the Agency/Intelligence Axis, and a new Ambient Intelligent Agent Model, which suggests that AmI agents have six properties, is proposed.
UR - http://www.scopus.com/inward/record.url?scp=85142491656&partnerID=8YFLogxK
U2 - 10.1115/DETC2022-91063
DO - 10.1115/DETC2022-91063
M3 - Conference contribution
AN - SCOPUS:85142491656
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 34th International Conference on Design Theory and Methodology (DTM)
PB - The American Society of Mechanical Engineers (ASME)
Y2 - 14 August 2022 through 17 August 2022
ER -