How Different Groups Prioritize Ethical Values for Responsible AI
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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.
Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). Builds on 14 of its 75 references that are in the map. First posted on arXiv 16 May 2022.
Builds on
14 of 75 references.
References · 14
- 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
- 2017On Fairness and Calibrationcited by 406
- 2017Algorithmic decision making and the cost of fairnesscited by 402
- 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
- 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
- 2021The Values Encoded in Machine Learning Researchcited by 172
- 2017Fairness in Machine Learning: Lessons from Political Philosophycited by 148
- 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
- 2018How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairnesscited by 48
- 2018Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Predictioncited by 35
- 2020Overcoming Failures of Imagination in AI Infused System Development and Deploymentcited by 22
- 2021Unpacking the Expressed Consequences of AI Research in Broader Impact Statementscited by 22
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Citing papers per year: 2023: 19; 2024: 10; 2025: 10; 2026: 10.
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- 2023Co-Writing with Opinionated Language Models Affects Users' Viewscited by 145
- 2024Design Principles for Generative AI Applicationscited by 57
- 2023Disentangling Perceptions of Offensiveness: Cultural and Moral Correlatescited by 26
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- 2025Explainable AI for Clinical Outcome Prediction: A Survey of Clinician Perceptions and Preferencescited by 4
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- 2025The Hall of AI Fears and Hopes: Comparing the Views of AI Influencers and those of Members of the U.S. Public Through an Interactive Platformcited by 2
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- 2023Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Makingcited by 1
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- 2025Multi-Agent LLMs as Ethics Advocates for AI-Based Systemscited by 1
- 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotationcited by 1
- 2026Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modellingcited by 1
- 2023A short review of the main concerns in A.I. development and application within the public sector supported by NLP and TMcited by 0
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- 2026Value-Sensitive Delegation in Everyday AI Agent Use: Evidence from OpenClaw18 Sep 2026
- 2026Assessing the Applicability of Existing Design Recommendations to AI Companion Design: A Multi-Method Study13 Sep 2026
- 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research11 Aug 2026
- 2026Bridging Values and Behavior: A Hierarchical Framework for Proactive Embodied Agents30 Apr 2026
- 2026Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modelling9 Apr 2026
- 2026Sima AIunty: Caste Audit in LLM-Driven Matchmaking31 Mar 2026
- 2026Student views in AI Ethics and Social Impact19 Mar 2026
- 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation11 Feb 2026
- 2026AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations30 Jan 2026
- 2025Problems With Large Language Models for Learner Modelling: Why LLMs Alone Fall Short for Responsible Tutoring in K--12 Education28 Dec 2025
- 2025TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories26 Nov 2025
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- 2025Are We Aligned? A Preliminary Investigation of the Alignment of Responsible AI Values between LLMs and Human Judgment6 Nov 2025
- 2025Multi-Agent LLMs as Ethics Advocates for AI-Based Systems11 Jul 2025
- 2025Can we Debias Social Stereotypes in AI-Generated Images? Examining Text-to-Image Outputs and User Perceptions27 May 2025
- 2025AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depression26 Apr 2025
- 2025The Hall of AI Fears and Hopes: Comparing the Views of AI Influencers and those of Members of the U.S. Public Through an Interactive Platform8 Apr 2025
- 2025Towards Responsible and Trustworthy Educational Data Mining: Comparing Symbolic, Sub-Symbolic, and Neural-Symbolic AI Methods1 Apr 2025
- 2025Explainable AI for Clinical Outcome Prediction: A Survey of Clinician Perceptions and Preferences27 Feb 2025
- 2024What Makes An Expert? Reviewing How ML Researchers Define "Expert"31 Oct 2024
- 2024"I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AI11 Oct 2024
- 2024Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance26 Jul 2024
- 2024An Empirical Exploration of Trust Dynamics in LLM Supply Chains25 May 2024
- 2024Developing and Evaluating a Design Method for Positive Artificial Intelligence2 Feb 2024
- 2024Design Principles for Generative AI Applications25 Jan 2024
- 2024Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions24 Jan 2024
- 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective9 Jan 2024
- 2024Exploring Public Opinion on Responsible AI Through The Lens of Cultural Consensus Theory6 Jan 2024
- 2024Fairness Certification for Natural Language Processing and Large Language Models2 Jan 2024
- 2023NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs19 Dec 2023
- 2023Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI13 Dec 2023
- 2023Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates11 Dec 2023
- 2023Responsible AI Research Needs Impact Statements Too20 Nov 2023
- 2023Assessing AI Impact Assessments: A Classroom Study19 Nov 2023
- 2023The Value-Sensitive Conversational Agent Co-Design Framework18 Oct 2023
- 2023The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements10 Oct 2023
- 2023Evaluating and Improving Value Judgments in AI: A Scenario-Based Study on Large Language Models' Depiction of Social Conventions4 Oct 2023
- 2023User Experience Design Professionals' Perceptions of Generative Artificial Intelligence26 Sep 2023
- 2023Artificial Intelligence across Europe: A Study on Awareness, Attitude and Trust19 Aug 2023
- 2023A short review of the main concerns in A.I. development and application within the public sector supported by NLP and TM25 Jul 2023
- 2023`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values14 Jul 2023
- 2023Scaling Laws Do Not Scale5 Jul 2023
- 2023Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making26 May 2023
- 2023Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study15 May 2023
- 2023Human-Centered Responsible Artificial Intelligence: Current & Future Trends16 Feb 2023
- 2023A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI10 Feb 2023
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- 2023FATE in AI: Towards Algorithmic Inclusivity and Accessibility3 Jan 2023
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- 2026Value-Sensitive Delegation in Everyday AI Agent Use: Evidence from OpenClawcited by 0
- 2026Value-Sensitive Delegation in Everyday AI Agent Use: Evidence from OpenClaw18 Sep 2026
- 2026Bridging Values and Behavior: A Hierarchical Framework for Proactive Embodied Agents30 Apr 2026
- 2025Multi-Agent LLMs as Ethics Advocates for AI-Based Systems11 Jul 2025
- 2023The Value-Sensitive Conversational Agent Co-Design Framework18 Oct 2023
- 2023Co-Writing with Opinionated Language Models Affects Users' Viewscited by 145
- 2024Design Principles for Generative AI Applicationscited by 57
- 2023Disentangling Perceptions of Offensiveness: Cultural and Moral Correlatescited by 26
- 2023`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Valuescited by 18
- 2023User Experience Design Professionals' Perceptions of Generative Artificial Intelligencecited by 16
- 2023NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMscited by 14
- 2023Scaling Laws Do Not Scalecited by 13
- 2025AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depressioncited by 13
- 2025TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Storiescited by 10
- 2023Artificial Intelligence across Europe: A Study on Awareness, Attitude and Trustcited by 9
- 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspectivecited by 9
- 2023Human-Centered Responsible Artificial Intelligence: Current & Future Trendscited by 8
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- 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research11 Aug 2026
- 2026Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modelling9 Apr 2026
- 2026Sima AIunty: Caste Audit in LLM-Driven Matchmaking31 Mar 2026
- 2026Student views in AI Ethics and Social Impact19 Mar 2026
- 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation11 Feb 2026
- 2026AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations30 Jan 2026
- 2025Problems With Large Language Models for Learner Modelling: Why LLMs Alone Fall Short for Responsible Tutoring in K--12 Education28 Dec 2025
- 2025TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories26 Nov 2025
- 2025Alternative Fairness and Accuracy Optimization in Criminal Justice6 Nov 2025
- 2025Are We Aligned? A Preliminary Investigation of the Alignment of Responsible AI Values between LLMs and Human Judgment6 Nov 2025
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