# DIANES: A DEI Audit Toolkit for News Sources

arXiv: https://arxiv.org/abs/2203.11383 · HTML: https://mlsanity.com/paper/2203.11383/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 1 of 1)

- [Predicting Race and Ethnicity From the Sequence of Characters in a Name](https://mlsanity.com/paper/1805.02109/) (2018)

## Built on by (most cited first, 2 of 2)

- [Multi-Layer Ranking with Large Language Models for News Source Recommendation](https://mlsanity.com/paper/2406.11745/) (2024)
- [Evaluation of AI Ethics Tools in Language Models: A Developers' Perspective Case Study](https://mlsanity.com/paper/2512.15791/) (2025)

## Abstract (arXiv)

Professional news media organizations have always touted the importance that they give to multiple perspectives. However, in practice the traditional approach to all-sides has favored people in the dominant culture. Hence it has come under ethical critique under the new norms of diversity, equity, and inclusion (DEI). When DEI is applied to journalism, it goes beyond conventional notions of impartiality and bias and instead democratizes the journalistic practice of sourcing -- who is quoted or interviewed, who is not, how often, from which demographic group, gender, and so forth. There is currently no real-time or on-demand tool in the hands of reporters to analyze the persons they quote. In this paper, we present DIANES, a DEI Audit Toolkit for News Sources. It consists of a natural language processing pipeline on the backend to extract quotes, speakers, titles, and organizations from news articles in real time. On the frontend, DIANES offers the WordPress plugins, a Web monitor, and a DEI annotation API service, to help news media monitor their own quoting patterns and push themselves towards DEI norms.