# A Human Rights-Based Approach to Responsible AI

arXiv: https://arxiv.org/abs/2210.02667 · HTML: https://mlsanity.com/paper/2210.02667/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 83 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Social Biases in NLP Models as Barriers for Persons with Disabilities](https://mlsanity.com/paper/2005.00813/) (2020)
- [Re-imagining Algorithmic Fairness in India and Beyond](https://mlsanity.com/paper/2101.09995/) (2021)
- [Perturbation Sensitivity Analysis to Detect Unintended Model Biases](https://mlsanity.com/paper/1910.04210/) (2019)
- [A Just and Comprehensive Strategy for Using NLP to Address Online Abuse](https://mlsanity.com/paper/1906.01738/) (2019)
- [Racial categories in machine learning](https://mlsanity.com/paper/1811.11668/) (2018)
- [Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics](https://mlsanity.com/paper/2005.07572/) (2020)
- [Towards decolonising computational sciences](https://mlsanity.com/paper/2009.14258/) (2020)
- [Case Study: Deontological Ethics in NLP](https://mlsanity.com/paper/2010.04658/) (2020)
- [Algorithmic Injustices: Towards a Relational Ethics](https://mlsanity.com/paper/1912.07376/) (2019)
- [Robot Rights? Let's Talk about Human Welfare Instead](https://mlsanity.com/paper/2001.05046/) (2020)
- [Measuring Diversity of Artificial Intelligence Conferences](https://mlsanity.com/paper/2001.07038/) (2020)

## Built on by (most cited first, 20 of 20)

- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties](https://mlsanity.com/paper/2309.00779/) (2023)
- [On the application of Large Language Models for language teaching and assessment technology](https://mlsanity.com/paper/2307.08393/) (2023)
- [Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates](https://mlsanity.com/paper/2312.06861/) (2023)
- [Value Imprint: A Technique for Auditing the Human Values Embedded in RLHF Datasets](https://mlsanity.com/paper/2411.11937/) (2024)
- [Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits](https://mlsanity.com/paper/2403.14791/) (2024)
- [Socially Responsible Data for Large Multilingual Language Models](https://mlsanity.com/paper/2409.05247/) (2024)
- [Antagonistic AI](https://mlsanity.com/paper/2402.07350/) (2024)
- [Sociotechnical Implications of Generative Artificial Intelligence for Information Access](https://mlsanity.com/paper/2405.11612/) (2024)
- [Cultural Re-contextualization of Fairness Research in Language Technologies in India](https://mlsanity.com/paper/2211.11206/) (2022)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [Machine Love](https://mlsanity.com/paper/2302.09248/) (2023)
- [SINCERE: Supervised Information Noise-Contrastive Estimation REvisited](https://mlsanity.com/paper/2309.14277/) (2023)
- [Autonomy by Design: Preserving Human Autonomy in AI Decision-Support](https://mlsanity.com/paper/2506.23952/) (2025)
- [Positive AI: Key Challenges in Designing Artificial Intelligence for Wellbeing](https://mlsanity.com/paper/2304.12241/) (2023)
- [Modeling the Sacred: Considerations when Using Religious Texts in Natural Language Processing](https://mlsanity.com/paper/2404.14740/) (2024)
- [The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems](https://mlsanity.com/paper/2409.16098/) (2024)
- [Hedging and Non-Affirmation: Quantifying LLM Alignment on Questions of Human Rights](https://mlsanity.com/paper/2502.19463/) (2025)
- [Beyond Abstract Compliance: Operationalising trust in AI as a moral relationship](https://mlsanity.com/paper/2601.22769/) (2026)
- [Toward a Theory of Value in AI Alignment](https://mlsanity.com/paper/2608.10327/) (2026)

## Abstract (arXiv)

Research on fairness, accountability, transparency and ethics of AI-based interventions in society has gained much-needed momentum in recent years. However it lacks an explicit alignment with a set of normative values and principles that guide this research and interventions. Rather, an implicit consensus is often assumed to hold for the values we impart into our models - something that is at odds with the pluralistic world we live in. In this paper, we put forth the doctrine of universal human rights as a set of globally salient and cross-culturally recognized set of values that can serve as a grounding framework for explicit value alignment in responsible AI - and discuss its efficacy as a framework for civil society partnership and participation. We argue that a human rights framework orients the research in this space away from the machines and the risks of their biases, and towards humans and the risks to their rights, essentially helping to center the conversation around who is harmed, what harms they face, and how those harms may be mitigated.