Society-in-the-loop: programming the algorithmic social contract
Society-in-the-loop: programming the algorithmic social contract
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DOI：
10.1007/s10676-017-9430-8
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发表时间：
2018-03-01
影响因子：
3.6
通讯作者：
Rahwan, Iyad
中科院分区：
文献类型：
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作者：
Rahwan, Iyad
人工智能 (AI) 和机器学习的最新快速发展引发了许多有关自主机器的监管和治理机制的问题。许多评论家、学者和政策制定者现在呼吁确保管理我们生活的算法是透明、公平和负责任的。在这里，我提出了一个人工智能和算法系统监管的概念框架。我认为我们需要工具来编程、调试和维护算法社会契约，这是由机器调解的各种人类利益相关者之间的契约。为了实现这一目标，我们可以从建模与仿真以及交互式机器学习领域采用人机交互（HITL）的概念。我特别提出了一个称为社会循环（SITL）的议程，它将 HITL 控制范式与受人工智能系统影响的各个利益相关者的价值观协商机制以及监控协议的遵守情况相结合。简而言之，“SITL = HITL + 社会契约”。
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.'.