Human-AI Collaboration Mechanism Study on AIGC Assisted Image Production for Special Coverage

TL;DR

This study investigates controllable AIGC image production for journalism, addressing challenges of semantic fidelity, cultural specificity, and trust, through experiments and a human-in-the-loop pipeline emphasizing transparency and content verification.

Contribution

It introduces a human-AI collaboration framework for AIGC in journalism, combining modular pipelines with semantic scoring and content filtering to enhance controllability and trustworthiness.

Findings

Disparities in semantic alignment across platforms due to training bias.

A modular pipeline improves editorial fidelity and content verification.

Proposed evaluation metrics include CIS, CEA, and U-PA.

Abstract

Artificial Intelligence Generated Content (AIGC) assisting image production triggers controversy in journalism while attracting attention from media agencies. Key issues involve misinformation, authenticity, semantic fidelity, and interpretability. Most AIGC tools are opaque "black boxes," hindering the dual demands of content accuracy and semantic alignment and creating ethical, sociotechnical, and trust dilemmas. This paper explores pathways for controllable image production in journalism's special coverage and conducts two experiments with projects from China's media agency: (1) Experiment 1 tests cross-platform adaptability via standardized prompts across three scenes, revealing disparities in semantic alignment, cultural specificity, and visual realism driven by training-corpus bias and platform-level filtering. (2) Experiment 2 builds a human-in-the-loop modular pipeline combining high-precision segmentation (SAM, GroundingDINO), semantic alignment (BrushNet), and style regulating (Style-LoRA, Prompt-to-Prompt), ensuring editorial fidelity through CLIP-based semantic scoring, NSFW/OCR/YOLO filtering, and verifiable content credentials. Traceable deployment preserves semantic representation. Consequently, we propose a human-AI collaboration mechanism for AIGC assisted image production in special coverage and recommend evaluating Character Identity Stability (CIS), Cultural Expression Accuracy (CEA), and User-Public Appropriateness (U-PA).