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.'

本研究提出了一个对人工智能和算法系统进行监管的概念性框架，其中结合人机交互学习与各利益相关者的价值观协商及监控来保证算法社会契约的编程、调试、运行和维护。