# Algorithmic Fairness in NLP: Persona-Infused LLMs for Human-Centric Hate Speech Detection

arXiv: https://arxiv.org/abs/2510.19331 · HTML: https://mlsanity.com/paper/2510.19331/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://mlsanity.com/paper/2203.09509/) (2022)
- [Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification](https://mlsanity.com/paper/1903.04561/) (2019)
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021)
- [Human and LLM Biases in Hate Speech Annotations: A Socio-Demographic Analysis of Annotators and Targets](https://mlsanity.com/paper/2410.07991/) (2024)
- [Large Language Models Show Human-like Social Desirability Biases in Survey Responses](https://mlsanity.com/paper/2405.06058/) (2024)
- [Hate Personified: Investigating the role of LLMs in content moderation](https://mlsanity.com/paper/2410.02657/) (2024)
- [How Hate Speech Varies by Target Identity: A Computational Analysis](https://mlsanity.com/paper/2210.10839/) (2022)
- [Efficient Toxic Content Detection by Bootstrapping and Distilling Large Language Models](https://mlsanity.com/paper/2312.08303/) (2023)
- [Personas with Attitudes: Controlling LLMs for Diverse Data Annotation](https://mlsanity.com/paper/2410.11745/) (2024)
- [Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation](https://mlsanity.com/paper/2412.07338/) (2024)
- ["Define Your Terms" : Enhancing Efficient Offensive Speech Classification with Definition](https://mlsanity.com/paper/2402.03221/) (2024)
- [Evaluating how LLM annotations represent diverse views on contentious topics](https://mlsanity.com/paper/2503.23243/) (2025)
- [The Impact of Persona-based Political Perspectives on Hateful Content Detection](https://mlsanity.com/paper/2502.00385/) (2025)
- [BERT-based Ensemble Approaches for Hate Speech Detection](https://mlsanity.com/paper/2209.06505/) (2022)
- [Leveraging a Multi-Agent LLM-Based System to Educate Teachers in Hate Incidents Management](https://mlsanity.com/paper/2506.23774/) (2025)
- [Towards Identity-Aware Cross-Modal Retrieval: a Dataset and a Baseline](https://mlsanity.com/paper/2412.21009/) (2024)

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

- [Improving Implicit Hate Speech Detection via a Community-Driven Multi-Agent Framework](https://mlsanity.com/paper/2601.09342/) (2026)
- [Who Decides What Is Harmful? Content Moderation Policy Through A Multi-Agent Personalised Inference Framework](https://mlsanity.com/paper/2605.01416/) (2026)

## Abstract (arXiv)

In this paper, we investigate how personalising Large Language Models (Persona-LLMs) with annotator personas affects their sensitivity to hate speech, particularly regarding biases linked to shared or differing identities between annotators and targets. To this end, we employ Google's Gemini and OpenAI's GPT-4.1-mini models and two persona-prompting methods: shallow persona prompting and a deeply contextualised persona development based on Retrieval-Augmented Generation (RAG) to incorporate richer persona profiles. We analyse the impact of using in-group and out-group annotator personas on the models' detection performance and fairness across diverse social groups. This work bridges psychological insights on group identity with advanced NLP techniques, demonstrating that incorporating socio-demographic attributes into LLMs can address bias in automated hate speech detection. Our results highlight both the potential and limitations of persona-based approaches in reducing bias, offering valuable insights for developing more equitable hate speech detection systems.