# Who Owns The Robot?: Four Ethical and Socio-technical Questions about Wellbeing Robots in the Real World through Community Engagement

arXiv: https://arxiv.org/abs/2509.02624 · HTML: https://mlsanity.com/paper/2509.02624/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 37 of its 160 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 30 of 37)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022)
- [Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty](https://mlsanity.com/paper/2011.07586/) (2020)
- [Cultural Incongruencies in Artificial Intelligence](https://mlsanity.com/paper/2211.13069/) (2022)
- [Uncertainty-based Fairness Measures](https://mlsanity.com/paper/2312.11299/) (2023)
- [ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles](https://mlsanity.com/paper/1912.06166/) (2019)
- [Taking Principles Seriously: A Hybrid Approach to Value Alignment](https://mlsanity.com/paper/2012.11705/) (2020)
- [Black Feminist Musings on Algorithmic Oppression](https://mlsanity.com/paper/2101.09869/) (2021)
- [U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection](https://mlsanity.com/paper/2501.09687/) (2025)
- [Small but Fair! Fairness for Multimodal Human-Human and Robot-Human Mental Wellbeing Coaching](https://mlsanity.com/paper/2407.01562/) (2024)
- [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024)
- [Crowdsourcing Impacts: Exploring the Utility of Crowds for Anticipating Societal Impacts of Algorithmic Decision Making](https://mlsanity.com/paper/2207.09525/) (2022)
- [Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & China](https://mlsanity.com/paper/2408.04026/) (2024)
- [Machine Learning Fairness for Depression Detection using EEG Data](https://mlsanity.com/paper/2501.18192/) (2025)
- [Underneath the Numbers: Quantitative and Qualitative Gender Fairness in LLMs for Depression Prediction](https://mlsanity.com/paper/2406.08183/) (2024)
- [A Robotic Positive Psychology Coach to Improve College Students' Wellbeing](https://mlsanity.com/paper/2009.03829/) (2020)
- [Appropriateness of LLM-equipped Robotic Well-being Coach Language in the Workplace: A Qualitative Evaluation](https://mlsanity.com/paper/2401.14935/) (2024)
- [Opportunities in Mental Health Support for Informal Dementia Caregivers Suffering from Verbal Agitation](https://mlsanity.com/paper/2311.10912/) (2023)
- [Does Removing Stereotype Priming Remove Bias? A Pilot Human-Robot Interaction Study](https://mlsanity.com/paper/1807.00948/) (2018)
- [Robots Racialized in the Likeness of Marginalized Social Identities are Subject to Greater Dehumanization than those racialized as White](https://mlsanity.com/paper/1808.00320/) (2018)
- [Robots as Mental Well-being Coaches: Design and Ethical Recommendations](https://mlsanity.com/paper/2208.14874/) (2022)
- ["Oh, Sorry, I Think I Interrupted You'': Designing Repair Strategies for Robotic Longitudinal Well-being Coaching](https://mlsanity.com/paper/2401.03794/) (2024)
- [An extended framework for characterizing social robots](https://mlsanity.com/paper/1907.09873/) (2019)
- [Creating a Robot Coach for Mindfulness and Wellbeing: A Longitudinal Study](https://mlsanity.com/paper/2006.05289/) (2020)

## Abstract (arXiv)

Recent studies indicate that robotic coaches can play a crucial role in promoting wellbeing. However, the real-world deployment of wellbeing robots raises numerous ethical and socio-technical questions and concerns. To explore these questions, we undertake a community-centered investigation to examine three different communities' perspectives on using robotic wellbeing coaches in real-world environments. We frame our work as an anticipatory ethical investigation, which we undertake to better inform the development of robotic technologies with communities' opinions, with the ultimate goal of aligning robot development with public interest. We conducted workshops with three communities who are under-represented in robotics development: 1) members of the public at a science festival, 2) women computer scientists at a conference, and 3) humanities researchers interested in history and philosophy of science. In the workshops, we collected qualitative data using the Social Robot Co-Design Canvas on Ethics. We analysed the collected qualitative data with Thematic Analysis, informed by notes taken during workshops. Through our analysis, we identify four themes regarding key ethical and socio-technical questions about the real-world use of wellbeing robots. We group participants' insights and discussions around these broad thematic questions, discuss them in light of state-of-the-art literature, and highlight areas for future investigation. Finally, we provide the four questions as a broad framework that roboticists can and should use during robotic development and deployment, in order to reflect on the ethics and socio-technical dimensions of their robotic applications, and to engage in dialogue with communities of robot users. The four questions are: 1) Is the robot safe and how can we know that?, 2) Who is the robot built for and with?, 3) Who owns the robot and the data?, and 4) Why a robot?.