# How Different Groups Prioritize Ethical Values for Responsible AI

arXiv: https://arxiv.org/abs/2205.07722 · HTML: https://mlsanity.com/paper/2205.07722/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 75 references that are in the map.
- First posted on arXiv 16 May 2022.

## Builds on (most cited first, 14 of 14)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Differential Privacy Has Disparate Impact on Model Accuracy](https://mlsanity.com/paper/1905.12101/) (2019)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness](https://mlsanity.com/paper/1811.03654/) (2018)
- [Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction](https://mlsanity.com/paper/1802.09548/) (2018)
- [Overcoming Failures of Imagination in AI Infused System Development and Deployment](https://mlsanity.com/paper/2011.13416/) (2020)
- [Unpacking the Expressed Consequences of AI Research in Broader Impact Statements](https://mlsanity.com/paper/2105.04760/) (2021)
- [A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics](https://mlsanity.com/paper/1807.00553/) (2018)
- [Increasing Adversarial Uncertainty to Scale Private Similarity Testing](https://mlsanity.com/paper/2109.01727/) (2021)

## Built on by (most cited first, 30 of 49)

- [Co-Writing with Opinionated Language Models Affects Users' Views](https://mlsanity.com/paper/2302.00560/) (2023)
- [Design Principles for Generative AI Applications](https://mlsanity.com/paper/2401.14484/) (2024)
- [Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates](https://mlsanity.com/paper/2312.06861/) (2023)
- [`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values](https://mlsanity.com/paper/2307.10221/) (2023)
- [User Experience Design Professionals' Perceptions of Generative Artificial Intelligence](https://mlsanity.com/paper/2309.15237/) (2023)
- [NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs](https://mlsanity.com/paper/2312.11803/) (2023)
- [Scaling Laws Do Not Scale](https://mlsanity.com/paper/2307.03201/) (2023)
- [AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depression](https://mlsanity.com/paper/2504.18932/) (2025)
- [TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories](https://mlsanity.com/paper/2511.21322/) (2025)
- [Artificial Intelligence across Europe: A Study on Awareness, Attitude and Trust](https://mlsanity.com/paper/2308.09979/) (2023)
- [Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective](https://mlsanity.com/paper/2401.04374/) (2024)
- [Human-Centered Responsible Artificial Intelligence: Current & Future Trends](https://mlsanity.com/paper/2302.08157/) (2023)
- [The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements](https://mlsanity.com/paper/2310.06269/) (2023)
- ["I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AI](https://mlsanity.com/paper/2410.08526/) (2024)
- [Responsible AI Research Needs Impact Statements Too](https://mlsanity.com/paper/2311.11776/) (2023)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [Can we Debias Social Stereotypes in AI-Generated Images? Examining Text-to-Image Outputs and User Perceptions](https://mlsanity.com/paper/2505.20692/) (2025)
- [Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study](https://mlsanity.com/paper/2305.18307/) (2023)
- [An Empirical Exploration of Trust Dynamics in LLM Supply Chains](https://mlsanity.com/paper/2405.16310/) (2024)
- [Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance](https://mlsanity.com/paper/2408.01458/) (2024)
- [What Makes An Expert? Reviewing How ML Researchers Define "Expert"](https://mlsanity.com/paper/2411.00179/) (2024)
- [Explainable AI for Clinical Outcome Prediction: A Survey of Clinician Perceptions and Preferences](https://mlsanity.com/paper/2502.20478/) (2025)
- [FATE in AI: Towards Algorithmic Inclusivity and Accessibility](https://mlsanity.com/paper/2301.01590/) (2023)
- [The Value-Sensitive Conversational Agent Co-Design Framework](https://mlsanity.com/paper/2310.11848/) (2023)
- [Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI](https://mlsanity.com/paper/2312.08064/) (2023)
- [Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions](https://mlsanity.com/paper/2401.13324/) (2024)
- [Towards Responsible and Trustworthy Educational Data Mining: Comparing Symbolic, Sub-Symbolic, and Neural-Symbolic AI Methods](https://mlsanity.com/paper/2504.00615/) (2025)
- [Assessing AI Impact Assessments: A Classroom Study](https://mlsanity.com/paper/2311.11193/) (2023)
- [Developing and Evaluating a Design Method for Positive Artificial Intelligence](https://mlsanity.com/paper/2402.01499/) (2024)
- [The Hall of AI Fears and Hopes: Comparing the Views of AI Influencers and those of Members of the U.S. Public Through an Interactive Platform](https://mlsanity.com/paper/2504.06016/) (2025)

## Abstract (arXiv)

Private companies, public sector organizations, and academic groups have outlined ethical values they consider important for responsible artificial intelligence technologies. While their recommendations converge on a set of central values, little is known about the values a more representative public would find important for the AI technologies they interact with and might be affected by. We conducted a survey examining how individuals perceive and prioritize responsible AI values across three groups: a representative sample of the US population (N=743), a sample of crowdworkers (N=755), and a sample of AI practitioners (N=175). Our results empirically confirm a common concern: AI practitioners' value priorities differ from those of the general public. Compared to the US-representative sample, AI practitioners appear to consider responsible AI values as less important and emphasize a different set of values. In contrast, self-identified women and black respondents found responsible AI values more important than other groups. Surprisingly, more liberal-leaning participants, rather than participants reporting experiences with discrimination, were more likely to prioritize fairness than other groups. Our findings highlight the importance of paying attention to who gets to define responsible AI.