# Society-in-the-Loop: Programming the Algorithmic Social Contract

arXiv: https://arxiv.org/abs/1707.07232 · HTML: https://mlsanity.com/paper/1707.07232/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2017.

## Built on by (most cited first, 30 of 35)

- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Jury Learning: Integrating Dissenting Voices into Machine Learning Models](https://mlsanity.com/paper/2202.02950/) (2022)
- [Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature](https://mlsanity.com/paper/2103.12016/) (2021)
- [Building Socio-culturally Inclusive Stereotype Resources with Community Engagement](https://mlsanity.com/paper/2307.10514/) (2023)
- [The MineRL BASALT Competition on Learning from Human Feedback](https://mlsanity.com/paper/2107.01969/) (2021)
- [Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making](https://mlsanity.com/paper/2102.00625/) (2021)
- [A Pragmatic View of AI Personhood](https://mlsanity.com/paper/2510.26396/) (2025)
- [Putting AI Ethics into Practice: The Hourglass Model of Organizational AI Governance](https://mlsanity.com/paper/2206.00335/) (2022)
- [China and the U.S. produce more impactful AI research when collaborating together](https://mlsanity.com/paper/2304.11123/) (2023)
- [Human-AI Governance (HAIG): A Trust-Utility Approach](https://mlsanity.com/paper/2505.01651/) (2025)
- [Human-centered mechanism design with Democratic AI](https://mlsanity.com/paper/2201.11441/) (2022)
- [Artificial intelligence in government: Concepts, standards, and a unified framework](https://mlsanity.com/paper/2210.17218/) (2022)
- [The Ethics of AI Value Chains](https://mlsanity.com/paper/2307.16787/) (2023)
- [The Value-Sensitive Conversational Agent Co-Design Framework](https://mlsanity.com/paper/2310.11848/) (2023)
- [HCMD-zero: Learning Value Aligned Mechanisms from Data](https://mlsanity.com/paper/2202.10122/) (2022)
- [Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements](https://mlsanity.com/paper/2306.10043/) (2023)
- [Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making](https://mlsanity.com/paper/2502.14869/) (2025)
- [EconGym: A Scalable AI Testbed with Diverse Economic Tasks](https://mlsanity.com/paper/2506.12110/) (2025)
- [Artificial Intelligence as an Anti-Corruption Tool (AI-ACT) -- Potentials and Pitfalls for Top-down and Bottom-up Approaches](https://mlsanity.com/paper/2102.11567/) (2021)
- [Ethical AI-Powered Regression Test Selection](https://mlsanity.com/paper/2106.16050/) (2021)
- [A multidomain relational framework to guide institutional AI research and adoption](https://mlsanity.com/paper/2303.10106/) (2023)
- [Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle](https://mlsanity.com/paper/2404.18736/) (2024)
- [Challenging the Human-in-the-loop in Algorithmic Decision-making](https://mlsanity.com/paper/2405.10706/) (2024)
- [Governance by Design: Architecting Agentic AI for Organizational Learning and Scalable Autonomy](https://mlsanity.com/paper/2605.20210/) (2026)
- [Descriptive AI Ethics: Collecting and Understanding the Public Opinion](https://mlsanity.com/paper/2101.05957/) (2021)
- [Toward a Rational and Ethical Sociotechnical System of Autonomous Vehicles: A Novel Application of Multi-Criteria Decision Analysis](https://mlsanity.com/paper/2102.02928/) (2021)
- [Contextualizing Artificially Intelligent Morality: A Meta-Ethnography of Top-Down, Bottom-Up, and Hybrid Models for Theoretical and Applied Ethics in Artificial Intelligence](https://mlsanity.com/paper/2204.07612/) (2022)
- [Tackling problems, harvesting benefits -- A systematic review of the regulatory debate around AI](https://mlsanity.com/paper/2209.05468/) (2022)
- [Finding Common Ground for Incoherent Horn Expressions](https://mlsanity.com/paper/2209.06455/) (2022)
- [Exploring Public Opinion on Responsible AI Through The Lens of Cultural Consensus Theory](https://mlsanity.com/paper/2402.00029/) (2024)

## Abstract (arXiv)

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, `SITL = HITL + Social Contract.'