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This paper presents an experimental case study utilizing machine learning and generative AI to audit content diversity in a hyper- local news outlet, The Scope, based at a university and focused on underrepresented communities in Boston. Through computational text analysis, including entity extraction, topic labeling, and quote extraction and attribution, we evaluate the extent to which The Scope’s coverage aligns with its mission to amplify diverse voices. The results reveal coverage patterns, topical focus, and source demo- graphics, highlighting areas for improvement in editorial practices. This research underscores the potential for AI-driven tools to sup- port similar small newsrooms in enhancing content diversity and alignment with their community-focused missions. Future work en- visions developing a cost-effective auditing toolkit to aid hyperlocal publishers in assessing and improving their coverage.