# Designing for Human Rights in AI

arXiv: https://arxiv.org/abs/2005.04949 · HTML: https://mlsanity.com/paper/2005.04949/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 13 of 13)

- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [Seamful XAI: Operationalizing Seamful Design in Explainable AI](https://mlsanity.com/paper/2211.06753/) (2022)
- [Towards a multi-stakeholder value-based assessment framework for algorithmic systems](https://mlsanity.com/paper/2205.04525/) (2022)
- [Characterizing and modeling harms from interactions with design patterns in AI interfaces](https://mlsanity.com/paper/2404.11370/) (2024)
- ["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)
- [Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance](https://mlsanity.com/paper/2207.01482/) (2022)
- [Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice](https://mlsanity.com/paper/2506.09873/) (2025)
- [Negative Human Rights as a Basis for Long-term AI Safety and Regulation](https://mlsanity.com/paper/2208.14788/) (2022)
- [Atomist or Holist? A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics Dialogue](https://mlsanity.com/paper/2208.09174/) (2022)
- [Exploring LGBTQ+ Bias in Generative AI Answers across Different Country and Religious Contexts](https://mlsanity.com/paper/2407.03473/) (2024)
- [Hedging and Non-Affirmation: Quantifying LLM Alignment on Questions of Human Rights](https://mlsanity.com/paper/2502.19463/) (2025)
- [Don't "research fast and break things": On the ethics of Computational Social Science](https://mlsanity.com/paper/2206.06370/) (2022)
- [Kaleidoscope Gallery: Exploring Ethics and Generative AI Through Art](https://mlsanity.com/paper/2505.14758/) (2025)

## Abstract (arXiv)

In the age of big data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance pricing, and many more aspects of our lives. AI systems can help us make evidence-driven, efficient decisions, but can also confront us with unjustified, discriminatory decisions wrongly assumed to be accurate because they are made automatically and quantitatively. It is becoming evident that these technological developments are consequential to people's fundamental human rights. Despite increasing attention to these urgent challenges in recent years, technical solutions to these complex socio-ethical problems are often developed without empirical study of societal context and the critical input of societal stakeholders who are impacted by the technology. On the other hand, calls for more ethically- and socially-aware AI often fail to provide answers for how to proceed beyond stressing the importance of transparency, explainability, and fairness. Bridging these socio-technical gaps and the deep divide between abstract value language and design requirements is essential to facilitate nuanced, context-dependent design choices that will support moral and social values. In this paper, we bridge this divide through the framework of Design for Values, drawing on methodologies of Value Sensitive Design and Participatory Design to present a roadmap for proactively engaging societal stakeholders to translate fundamental human rights into context-dependent design requirements through a structured, inclusive, and transparent process.