One year on: The momentum behind Jigsaw’s Sensemaking AI

Sensemaking AI | Jigsaw

Sensemaking is a suite of AI-powered tools designed to help public leaders understand public opinion quickly. By combining AI interviewing with automated reporting, we bridge the gap between individual voices and actionable policy insights.

More Than a Tool

What We Learned from a Year of Conversations About AI

Day Two

What I've learned in 48 hours of public sensemaking

Characterizing Agentic Flooding of Government Services

AI agents are making it easier for the public to interact with government, such as by helping them apply for benefits, understand complex policies, and make their opinions heard. Although...

Artificial intelligence in government: why people feel they lose control

The use of Artificial Intelligence (AI) in public administration is expanding rapidly. While AI promises greater efficiency and responsiveness, its integration into government and administration ra...

The social consequences of AI delegation

A substantial body of recent work has debated whether large language models (LLMs) can serve as substitutes for human participants in behavioural research. This debate, however, captures only one direction of a rapidly changing relationship. The more consequential question is not simply whether researchers should use LLMs as human surrogates, but whether - and under what conditions - humans are beginning to use LLMs as surrogates for their own deliberation. Across domains including health, law, finance, education, and personal guidance, increasing numbers of people consult generative AI systems before, alongside, or instead of human experts, peers, or independent judgment. Although evidence for actual delegation remains uneven, this uncertainty makes the phenomenon an urgent social-scientific object of study. We argue for a research programme that treats LLMs as consequential social actors in a functional sense: systems whose outputs shape human decisions, social norms, and collective dynamics.

3: Consulting the Record: AI Consistently Fails the Public - AI Now Institute

Amid the excitement over AI’s (speculative, hypothetical) potential, we have lost sight of the sobering reality of its present and recent past. AI is already intermediating critical social infrastructures, materially reshaping our institutions in ways that ratchet up inequality and concentrate power in the hands of the already powerful. It is consistently deployed in ways […]

Broad Listening · Democratic AI for sensemaking

Editorial AI that turns thousands of individual voices into a navigable atlas of topics, claims and quotes, grounded back to the participants who said them.

Our Approach to Artificial Intelligence

We are experimenting with using AI tools to extend our work as a small nonprofit, so that we can focus our time on reinforcing human connections, conversations, and communities that have eroded.

Democratizing AI Companies — The Collective Intelligence Project

As AI reshapes society, the companies behind these technologies face a critical challenge: how to build corporate governance structures that prioritize public benefit over profit maximization;.

Dario Amodei — The Urgency of Interpretability

In the decade that I have been working on AI, I’ve watched it grow from a tiny academic field to arguably the most important economic and geopolitical issue in the world.  In all that time, perhaps the most important lesson I’ve learned is this: the progress of the underlying technology is inexorable, driven by forces too powerful to stop, but the way in which it happens—the order in which things are built, the applications we choose, and the details of how it is rolled out to society—are eminently possible to change, and it’s possible to have great positive impact by doing so.  We can’t stop the bus, but we can steer it.  In the past I’ve written about the importance of deploying AI in a way that is positive for the world, and of ensuring that democracies build and wield the technology before autocracies do.  Over the last few months, I have become increasingly focused on an additional opportunity for steering the bus: the tantalizing possibility, opened up by some recent advances, that we could succeed at interpretability—that is, in understanding the inner workings of AI systems—before models reach an overwhelming level of power.

Building Political Superintelligence

Amidst fears of dystopia, a blueprint for how we use AI to reinvent the way we govern ourselves

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.’