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

TL;DR

This paper introduces Society-in-the-Loop (SITL), a framework for regulating AI systems by integrating human oversight with mechanisms for stakeholder value negotiation and compliance monitoring.

Contribution

It proposes a novel conceptual framework combining human-in-the-loop control with social contract mechanisms for AI governance.

Findings

SITL enables transparent and accountable AI regulation.

It facilitates stakeholder engagement in AI governance.

The framework supports ongoing monitoring and adjustment of AI systems.

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.'