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AI involvement level and workers' personal control in a bin-packing decision task

Source article: Navigating personal control: AI augmentation and its impact on decision-making

As Artificial Intelligence (AI) becomes increasingly embedded in organisational decision-making, it fundamentally reshapes workers' personal control over their tasks and outcomes. While, in contrast to AI automation, AI augmentation is typically promoted as a way to preserve human agency and personal control, empirical evidence regarding its psychological consequences remains fragmented. This study adopts Control Theory to examine how varying degrees of AI involvement, conceptualised as a continuous gradient rat…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-2142· cite & verify
Navigating personal control: AI augmentation and its impact on decision-making
"Containers" by Jim Bahn is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/.
Trace impact readingContested
P 64The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.

Both sides are scored from claims and sources, not community votes.

G 66The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.

In brief

Researchers ran an online within-subject experiment in which participants solved a bin-packing logistics optimisation problem while receiving different amounts of AI support. They treated AI involvement as a gradient instead of a choice between human-only and fully automated work, and measured participants' personal control at each level.

The result challenges the common assumption that keeping a human in the loop automatically preserves agency. Control fell once AI involvement became high, so system designers may need to watch for a threshold within augmentation. Evidence comes from a single task type and an online setting, so how well it carries over to real workplaces is not established in the text.

Main points

  1. Participants completed a bin-packing logistical optimisation task under several degrees of AI support, including a no-AI condition.
  2. Low and medium AI involvement left personal control comparable to the no-AI baseline, while high involvement reduced it significantly.
  3. Perceived responsibility mediated the effect: more AI intervention lowered felt responsibility, which then eroded personal control.
  4. Locus of Control moderated the effect, with internally oriented individuals more sensitive to control loss.
  5. The authors identify a threshold within augmentation beyond which effects resemble full automation.

The gain

Low and medium levels of AI support left participants' personal control comparable to working without AI, so limited augmentation did not measurably cost control in this task.

The problem

High AI involvement significantly reduced personal control by lowering felt responsibility, producing control loss comparable to full automation even when humans formally retained final decision authority.

The rundown

The study applies Control Theory to examine how AI involvement shapes personal control, noting that existing empirical evidence on the psychological consequences of augmentation is fragmented. It frames AI augmentation as typically promoted as a way to preserve human agency, in contrast to automation.

The mechanism the authors report runs through perceived responsibility: as the AI takes on more of the decision, users feel less responsible for outcomes, and this reduced responsibility lowers their sense of control. Personality matters too, since people with an internal locus of control react more strongly to losing control.

The authors say the findings have practical implications for designing human-centric AI systems and for putting human-in-the-loop principles into practice at work, because formal final decision authority alone did not prevent control loss at high AI involvement.

What this doesn’t fix

The evidence comes from an online within-subject experiment using a single bin-packing optimisation task, so it is a controlled setting rather than observation of real organisational decision-making.

Sources

  1. Peer-reviewedTechnovation2026-10-19

The debate