Human factors considerations for the context-aware design of adaptive autonomous teammates.
Despite the gains in performance that AI can bring to human-AI teams, they also present them with new challenges, such as the decline in human ability to respond to AI failures as the AI becomes more autonomous. This challenge is particularly dangerous in human-AI teams, where the AI holds a unique...
| Publicado en: | Ergonomics Vol. 68; no. 4; pp. 571 - 588 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183842148&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183842148 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00140139 ERO jtl: Ergonomics issn: 00140139 maglogo: Y pubinfo: dt: Apr2025 vid: 68 iid: 4 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 183842148 178654416 183842148 183842148 10.1080/00140139.2024.2380341 183842148 ppf: 571 ppct: 17 formats: tig: atl: Human factors considerations for the context-aware design of adaptive autonomous teammates. aug: au: Hauptman, Allyson I. Mallick, Rohit Flathmann, Christopher McNeese, Nathan J. affil: School of Computing, Clemson University, Clemson, South Carolina sug: subj: Autonomy Artificial Intelligence Task Performance and Analysis Consciousness Communication User-Computer Interface Ergonomics Adaptation, Occupational Human Male Female Decision Making Descriptive Statistics Trust Emergency Nurses Information Management Male Female ab: Despite the gains in performance that AI can bring to human-AI teams, they also present them with new challenges, such as the decline in human ability to respond to AI failures as the AI becomes more autonomous. This challenge is particularly dangerous in human-AI teams, where the AI holds a unique role in the team's success. Thus, it is imperative that researchers find solutions for designing AI team-mates that consider their human team-mates' needs in their adaptation logic. This study explores adaptive autonomy as a solution to overcoming these challenges. We conducted twelve contextual inquiries with professionals in two teaming contexts in order to understand how human teammate perceptions can be used to determine optimal autonomy levels for AI team-mates. The results of this study will enable the human factors community to develop AI team-mates that can enhance their team's performance while avoiding the potentially devastating impacts of their failures. Practitioner summary: As AI becomes more autonomous, the human ability to detect and respond to their failures decreases as they become less a part of the AI's decision-making loop. This contextual inquiry study shows how human factors are affected by and should influence the design of adaptive AI team-mates in different teaming contexts. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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