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

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.’

The Bitter Lesson: Rethinking How We Build AI Systems

The Race for AI Progress In 2019, Richard Sutton, wrote his groundbreaking essay titled ‘The Bitter Lesson’. Simply put, the essay concludes that systems which get better with higher compute beat the systems that do not. Or specifically in AI: raw computing power consistently wins over intricate human-designed solutions. I used to believe that clever orchestrations and sophisticated rules were the key to building better AI systems. That was a typical sofware dev mentality. You build a system, look for edgecases, cover them and you are good to go. Boy, was I wrong.

convaiinnovations/laya · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

WayFinder Philosophy | WayFinders Network

Wayfinding is a way of navigating through the uncertainty and complexity of the modern world

AI co-scientists are revolutionizing how research is done

Artificial-intelligence systems can generate hypotheses, design experiments and analyse data — but humans still need to decide what makes sense.

François Chollet on Twitter / X

Eventually, much of AI will converge towards intuition-guided symbolic world modeling, i.e. deep learning-guided program synthesis. It is inevitable. Symbolic modeling lets a system construct a compact, reusable, highly generalizable mental model of a problem space using minimal…— François Chollet (@fchollet) July 2, 2026

Bret Victor - The Future of Programming

I think this talk gives lots of food for thought. Are we too entrenched in our ways, to think in other/better ways?

Socialize the Plan

On AI and code review.

X : Do you think AI / vibe coding is not worthwhile? Me : Are you kidding? I was talking about conversational programming (what you call vibe coding) back in 2018. Of course, I think it's important.… | Simon Wardley | 44 comments

X : Do you think AI / vibe coding is not worthwhile? Me : Are you kidding? I was talking about conversational programming (what you call vibe coding) back in 2018. Of course, I think it's important. I think the approach we're using currently is flawed. X : Why? Me : Well, I could go on about the basics such as the medium is wrong (needs to be more image / whiteboard based rather than text) or how we're getting wrapped up in deterministic outputs (text based code) rather than realising the end outputs will eventually be non-deterministic models or we're still learning practices in this space especially with agents and how we rushing ahead into failure creating trust issues. There's a lot of messy stuff happening. But my real interest is with turning software engineering into an engineering discipline. X : Isn't it? Me : If I look at a map of decision making then testing (specifically TDD) is an engineering topic (see figure 1) but the development part is firmly a craft (see figure 2). If we want to make development an engineering subject then we need to learn from testing i.e. we should be building context specific tools (think a test suite) from lots of micro tools (think tests). See figure 3. X : And AI can help? Me : Oh, yes. In two parts - the construction of micro tools which helps us improve our ttA (time to answer) and in the construction of new hypothesis which helps us improve our ttQ (time to question). See figure 4. X : And is that happening? Me : Very slowly. Remember you have a multi billion dollar tool industry that doesn't want you creating tools and hence they're trying to flog you old tools with a skim of AI. X : But we need people selling pickaxes. Me : Sure. But they've been telling us that their pickaxes work everywhere ... making soup, you need a pickaxe with added AI. X : But coding is coding. Me : An electronic healthcare system is not the same as an online gambling site. The contexts are completely different. It's why the test suites are different and a test is nothing more than a micro tool. Why don't we compose our tool for that context out of micro tools ... just like testing? X : Because it's hard? Me: Pickaxe salespeople must love you. That's their marketing literature. Reminds me of the early days of Test Driven Development (TDD) where most people thought it was mad to build a test suite out of thousands of small tests. | 44 comments on LinkedIn

The future belongs to those who can refute AI, not just generate with AI

Why verification, not prompting, could shape the next decade of engineering

Algorithms may be making political polarization worse than we think

Marc Torrens and Carlos Carrasco-Farré analyze how algorithms influence opinions and heighten our emotional responses.

The Gap Through Which We Praise the Machine

My current theory of agentic programming: people are amazing at adapting the tools they're given and totally underestimate the extent to which they do it, and the amount of skill we build doing that is an incidental consequence of how badly the tools are designed.