Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0226
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-15T14:30:50.038070Z
status: published
lens: trace
sector: science
headline: Advancing Decision-Making through AI-Human Collaboration: A Systematic Review and Conceptual Framework
dek: Abstract The interplay between humans and artificial intelligence (AI) in decision-making has become increasingly intricate and significant. Despite rapid advancements, the literature remains fragmented, with limited integrative frameworks to explain how AI-human dynamics and decision-making typologies shape outcomes. This study addresses this critical gap by conducting a systematic review and bibliometric analysis of 627 articles, culminating in a novel conceptual framework. The framework identifies two critica…
gain_title: A systematic review and bibliometric analysis of 627 articles produced a conceptual framework with two dimensions and four paradigms that advances theoretical understanding of hybrid decision-making and provides actionable insights for organizations in AI-driven environments.
problem_title: Existing literature on AI-human decision-making was fragmented and lacked integrative frameworks to explain how AI-human dynamics and decision typologies shape outcomes despite increasingly intricate human-AI interplay.
trace_subject: AI-human collaborative decision-making and how its dynamics and typologies shape decision outcomes
gain_reading: A systematic review and bibliometric analysis of 627 articles produced a conceptual framework with two dimensions and four paradigms that advances theoretical understanding of hybrid decision-making and provides actionable insights for organizations in AI-driven environments.
gain_evidence: advances the theoretical understanding of hybrid decision-making systems | provides actionable insights for organizations navigating complex and AI-driven environments | framework identifies two critical dimensions, AI-human dynamics and decision typologies, that shape decision outcomes
problem_reading: Existing literature on AI-human decision-making was fragmented and lacked integrative frameworks to explain how AI-human dynamics and decision typologies shape outcomes despite increasingly intricate human-AI interplay.
problem_evidence: literature remains fragmented, with limited integrative frameworks to explain how AI-human dynamics and decision-making typologies shape outcomes | The interplay between humans and artificial intelligence (AI) in decision-making has become increasingly intricate and significant
quick_read: On 2026-04-03, a peer-reviewed article reported a systematic review and bibliometric analysis of 627 articles on human-AI decision-making. The authors identified two critical dimensions, AI-human dynamics and decision typologies, and proposed a novel conceptual framework comprising four paradigms: adaptive intuitive, programmed algorithmic, interpretive analytical, and integrative hybrid decision-making.

The framework matters because it moves beyond fragmented prior literature to clarify mechanisms and trade-offs in hybrid decision systems, offering actionable insights for organizations navigating AI-driven environments. As a conceptual synthesis rather than an empirical trial of a specific deployment, its impact remains theoretical, laying a foundation for future research on adaptive decision systems amid accelerating technological change.
limitation: 
tag: Automated dual reading
key_points: Systematic review and bibliometric analysis covered 627 articles on AI-human decision-making. | Framework identifies two critical dimensions: AI-human dynamics and decision typologies that shape decision outcomes. | Proposes four distinct paradigms: adaptive intuitive decision, programmed algorithmic decision, interpretive analytical decision and integrative hybrid decision.
rundown: The authors conducted a systematic review and bibliometric analysis of 627 articles to address fragmentation in the field. From this corpus they distilled two critical dimensions that shape decision outcomes: AI-human dynamics and decision typologies.

They synthesized these dimensions into four distinct paradigms of AI-human collaborative decision-making: adaptive intuitive decision, programmed algorithmic decision, interpretive analytical decision and integrative hybrid decision. The work is positioned as advancing theoretical understanding of hybrid systems and elucidating mechanisms and trade-offs inherent in collaboration.
sources:
- peer_reviewed | Group Decision and Negotiation | https://doi.org/10.1007/s10726-026-09980-1 | 2026-04-03
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

8b6fca4a799222f43438aaefe8a57bcce1d9b879d7dfb90615430df83f6c5a17

previous

0000000000000000000000000000000000000000000000000000000000000000