Framework for human–XAI symbiosis: extended self from the dual-process theory perspective

Yulia Litvinova, Patrick Mikalef and Xin (Robert) Luo

Journal of Business Analytics, 2024, vol. 7, issue 4, 224-255

Abstract: The use of artificial intelligence (AI)-based decision support systems (DSSs) is expected to enable superior human–XAI performance. To enhance decision-making performance, explanations are integrated into such DSSs (XAI DSSs). However, recent studies demonstrate that XAI in DSS does not necessarily result in improved decision-making performance, which is attributed to cognitive biases or to inaccurate heuristics regarding such XAI. There are also noted discrepancies regarding how humans think they will use XAI DSS versus their actual behaviour. Currently, there is no established theoretical framework to explain the cognitive mechanisms that underpin human decision-making when supported by XAI. We propose a theoretical framework that is grounded in the notion of extended self and in the dual-process theory of cognition. Building on existing studies, we explain that human–XAI interaction can change how humans base their decision-making, enabling a form of extended self. This reframing can support efficient design and implementation of XAI in DSS.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/2573234X.2024.2396366 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjbaxx:v:7:y:2024:i:4:p:224-255

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjba20

DOI: 10.1080/2573234X.2024.2396366

Access Statistics for this article

Journal of Business Analytics is currently edited by Dursan Delen

More articles in Journal of Business Analytics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().