# An Empirical Design Justice Approach to Identifying Ethical Considerations in the Intersection of Large Language Models and Social Robotics

arXiv: https://arxiv.org/abs/2406.06400 · HTML: https://mlsanity.com/paper/2406.06400/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 74 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Builds on (most cited first, 17 of 17)

- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Gender bias and stereotypes in Large Language Models](https://mlsanity.com/paper/2308.14921/) (2023)
- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Unveiling Security, Privacy, and Ethical Concerns of ChatGPT](https://mlsanity.com/paper/2307.14192/) (2023)
- [Understanding Large-Language Model (LLM)-powered Human-Robot Interaction](https://mlsanity.com/paper/2401.03217/) (2024)
- [WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Models](https://mlsanity.com/paper/2306.15087/) (2023)
- [The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support](https://mlsanity.com/paper/2401.14362/) (2024)
- [VITA: A Multi-modal LLM-based System for Longitudinal, Autonomous, and Adaptive Robotic Mental Well-being Coaching](https://mlsanity.com/paper/2312.09740/) (2023)
- [Embodiment in Socially Interactive Robots](https://mlsanity.com/paper/1912.00312/) (2019)
- [LLM-Powered Socially Assistive Robot-Delivered Cognitive Behavioral Therapy Exercises: an Exploratory Study with University Students](https://mlsanity.com/paper/2402.17937/) (2024)
- [Robot's Gendering Trouble: A Scoping Review of Gendering Humanoid Robots and its Effects on HRI](https://mlsanity.com/paper/2207.01130/) (2022)
- [How Can Large Language Models Enable Better Socially Assistive Human-Robot Interaction: A Brief Survey](https://mlsanity.com/paper/2404.00938/) (2024)
- [Contextual Code Switching for Machine Translation using Language Models](https://mlsanity.com/paper/2312.13179/) (2023)

## Built on by (most cited first, 1 of 1)

- [Stakeholder Perspectives on Whether and How Social Robots Can Support Mediation and Advocacy for Higher Education Students with Disabilities](https://mlsanity.com/paper/2503.16499/) (2025)

## Abstract (arXiv)

The integration of Large Language Models (LLMs) in social robotics presents a unique set of ethical challenges and social impacts. This research is set out to identify ethical considerations that arise in the design and development of these two technologies in combination. Using LLMs for social robotics may provide benefits, such as enabling natural language open-domain dialogues. However, the intersection of these two technologies also gives rise to ethical concerns related to misinformation, non-verbal cues, emotional disruption, and biases. The robot's physical social embodiment adds complexity, as ethical hazards associated with LLM-based Social AI, such as hallucinations and misinformation, can be exacerbated due to the effects of physical embodiment on social perception and communication. To address these challenges, this study employs an empirical design justice-based methodology, focusing on identifying socio-technical ethical considerations through a qualitative co-design and interaction study. The purpose of the study is to identify ethical considerations relevant to the process of co-design of, and interaction with a humanoid social robot as the interface of a LLM, and to evaluate how a design justice methodology can be used in the context of designing LLMs-based social robotics. The findings reveal a mapping of ethical considerations arising in four conceptual dimensions: interaction, co-design, terms of service and relationship and evaluates how a design justice approach can be used empirically in the intersection of LLMs and social robotics.