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

By Iyad Rahwan. Ethics and Information Technology, 2017

458 citations recorded by the source corpus.

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.’

Subfields: Information Systems, Safety Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/69c1d27722de73178e1ec71ab1caf5b3074d8ce4

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Citations in the corpus

WeBuildAI (2019, background): “Rahwan [64] argues for “society-in-the-loop,” which stresses the importance of creating infrastructure and tools to involve societal opinions in the creation of AI.”
Artificial Intelligence and the Public Sector—Applications and Challenges (2018, background): “…in terms of explicability, transparency, fairness, and accountability with regard to AI systems and algorithms that govern humans ’ lives, taking on the difficult task of introducing mechanisms of governance that minimize risks and potential pitfalls (Gasser & Almeida, 2017; Rahwan, 2018).”
Aligning AI Optimization to Community Well-Being (2020, background): “Rahwan (2018) calls this society-in-the-loop control, defined as “ embedding the values of society, as a whole, in the algorithmic governance of societal outcomes ” (p. 3).”
Transparency in artificial intelligence (2020, result): “This is an argument in line with what Rahwan (2018) has emphasised in terms of a need to keep society “in-the-loop” in order to enable such balances.”
Artificial Intelligence Regulation: A Meta-Framework for Formulation and Governance (2020, unknown): “[34] Rahwan, I. “Society-in-the-loop: programming the algorithmic social contract” Ethics and Information Technology, 20, 2017,pp. 5-14.”
Trustworthy Artificial Intelligence: A Review (2022, methodology): “Another set of researchers [157] proposed a social contract method to monitor the working of algorithms based on agreements using society-in-the-loop.”
Auditing large language models: a three-layered approach (2023, background): “Moreover, it is difficult to track impacts stemming from indirect causal chains [224], [225].”
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, background): “27 academic articles [8, 9, 17, 20, 22, 28, 35, 48, 52, 58, 61, 65, 86, 90, 93, 98, 106, 110, 111, 120, 124, 135, 139–141, 143, 144] which were found to be relevant to the topic prior to the start of the systematic review were assessed for their keywords.”
The ethics of algorithms: key problems and solutions (2020, background): “This framework aims to maintain a well-functioning “algorithmic social contract”, defined as “a pact between various human stakeholders, mediated by machines” (Rahwan 2018, 1).”
The ethics of AI in health care: A mapping review. (2020, background): “, 2018) and ensuring social inclusion in decision-making (Balthazar et al., 2018; Friedman et al., 2017; Rahwan, 2018), these remain relatively untested.”