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Ethics and Information Technology

, Volume 20, Issue 1, pp 5–14 | Cite as

Society-in-the-loop: programming the algorithmic social contract

  • Iyad Rahwan
Original Paper

Abstract

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.’

Keywords

Ethics Artificial intelligence Society Governance Regulation 

Notes

Acknowledgements

I am grateful for financial support from the Ethics & Governance of Artificial Intelligence Fund, as well as support from the Siegel Family Endowment. I am endebted to Joi Ito, Suelette Dreyfus, Cesar Hidalgo, Alex ‘Sandy’ Pentland, Tenzin Priyadarshi and Mark Staples for conversations and comments that helped shape this article. I’m grateful to Brett Scott for allowing me to appropriate the term ‘Techno-Leviathan’ which he originally presented in the context of Cryptocurrency (Scott 2014). I thank Deb Roy for introducing me to Walter Lippman’s ‘The Phantom Public’ and for constantly challenging my thinking. I thank Danny Hillis for pointing to the co-evolution of technology and societal values. I thank James Guszcza for suggesting the term ‘algorithm auditors’ and for other helpful comments.

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.The Media LabMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Institute for Data, Systems and SocietyMassachusetts Institute of TechnologyCambridgeUSA

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