Analyzing Character Representation in Media Content using Multimodal Foundation Model: Effectiveness and Trust
Evdoxia Taka, Debadyuti Bhattacharya, Joanne Garde-Hansen, Sanjay Sharma, Tanaya Guha

TL;DR
This paper introduces a multimodal AI tool that analyzes character demographics in media, presents visualizations for lay audiences, and evaluates public trust and usefulness through a user study.
Contribution
It proposes a novel AI-based character analysis tool using CLIP, combined with visualization designed for non-experts, and provides empirical insights into user trust and perception.
Findings
Participants understood the visualizations well.
The tool was deemed overall useful by users.
Trust in AI models was moderate to low.
Abstract
Recent advances in AI has made automated analysis of complex media content at scale possible while generating actionable insights regarding character representation along such dimensions as gender and age. Past works focused on quantifying representation from audio/video/text using AI models, but without having the audience in the loop. We ask, even if character distribution along demographic dimensions are available, how useful are those to the general public? Do they actually trust the numbers generated by AI models? Our work addresses these open questions by proposing a new AI-based character representation tool and performing a thorough user study. Our tool has two components: (i) An analytics extraction model based on the Contrastive Language Image Pretraining (CLIP) foundation model that analyzes visual screen data to quantify character representation across age and gender; (ii) A…
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Taxonomy
TopicsDiverse Approaches in Healthcare and Education Studies · Educational Systems and Policies
