I recently had the privilege of interviewing Camille Canon, the founder of Apiary and an expert in decentralized governance and facilitation. Camille shared deep insights on the opportunities and limitations of using AI for online governance, drawing on her extensive hands-on experience helping organizations with shared ownership and decision-making.

"The reason I care about governance is that I think it's essential to solving much larger problems," Canon explains. For her, governance is deeply intertwined with the potential and pitfalls of internet-enabled collaboration. This interest led her to DAOs as an intriguing "petri dish" for experimentation.

The Complex Role of Facilitation

Camille emphasized that facilitation is not simply about efficient information sharing. As she explained, "Facilitation serves two purposes: information sharing and allowing individuals to feel heard/psychological relief. The latter may be difficult to replicate with a bot."

This psychological relief comes from feeling heard and understood. As Camille noted, "You can't actually get people to arrive at conclusions faster, or in good ways unless you create the space for that facilitation." She has seen first-hand how sharing unspoken concerns relieves stress and enables collaboration:

"One of the things I have repeatedly heard in any [facilitation process], including our Radworks project... I have individuals say 'I have been carrying around this weight within the organization, and because of power dynamics, and information flows, and uncertainty... I haven't had a place to share this.'"

It is unclear whether an AI system could provide the same human connection and trust needed for this vulnerability.

Canon warns against the trap of prioritizing technology over human needs: "People overlook the messiness of dealing with human beings." Successful governance, in her view, begins with creating safe spaces where individuals can confidently contribute: "I have repeatedly heard 'I have been carrying around this weight within the organization, and because of power dynamics, I haven't had a place to share this.' What happens is this very psychologically based relief of having been heard."

The ability of AI systems to replicate this level of trust and psychological safety as facilitators, she says, remains an open question.

The False Allure of Aggregation

Camille also cautioned against viewing governance as a purely rational process of aggregating individual inputs. As she put it, "Good facilitation navigates the relationship between individual and group perspectives. Aggregating individual inputs doesn't necessarily lead to group consensus."

In fact, Camille argued that paying attention to outliers and minority voices is critical for good decisions:

"It's kind of counterintuitive, but it's often the outliers of the group ... that is actually the point of most interest and something that moves the group forward with a decision."

Pushing for synthesis and consensus can gloss over the most important perspectives that need to be integrated. AI that aims to identify common ground may miss opportunities to look at problems in radically new ways.

Canon critiques the assumption that LLMs can single-handedly solve complex problems of group sensemaking. "Some people think individual contributions can be summarized into group thinking," she says, "That's a falsehood." She emphasizes the importance of skilled facilitation:

"With multiple layers of [AI-powered] decision-making that play into the middle, we ironically get further and further away from the actual truth of the group's thoughts and frustrations."

Rethinking Participation-Based Governance

When discussing participation-based governance, Camille voiced scepticism about its limits:

"I don't think that people actually like to participate in governance... there is not sufficient attention available for people to participate in the way that would be necessary for these systems to function well."

As an alternative, she proposed intervention-based governance where the focus is on allowing people to intervene when things go off track rather than requiring constant participation.

To make intervention work, information flows and decision rights must be consciously designed. Camille suggested an approach of "localizing power within a system so that people can act autonomously in small groups" while still providing "guardrails or systems" for organization-wide monitoring and alignment.

No matter the approach, Camille emphasized that "defining shared purpose and success criteria are also critical but challenging parts of governance." Without that alignment, decisions lose legitimacy regardless of the process.

Canon advocates for a groundbreaking shift in how we think about governance. "Governance doesn't have to be based on participation; governance can be based on intervention," she asserts. She questions the sustainability of systems reliant on constant participation, arguing that it can lead to "a delayed cost in the system." Instead, she champions governance designed for well-timed interventions by informed members: "How do we design systems where people are given the opportunity to intervene when things are going off, rather than having to consistently participate in the system?"

Thoughtfully Incorporating AI

Camille recognized the value AI could provide in quickly gathering diverse inputs compared to traditional surveys. This ideation still needs proper deliberation, but AI could help give a broader perspective on problems. As Camille summarized:

"Often in group conversations, we keep looking at the problem from one angle, the person and by leveraging AI and having more contributions we're able to like look more dynamically from multiple angles."

Used thoughtfully, AI may help make governance more inclusive and dynamic. But incorporating it poorly risks oversimplifying the inherent complexity of human collaboration. Balancing the capabilities of AI with deep wisdom about group dynamics will be critical as we experiment with participatory governance at scale.

For Canon, well-defined success criteria and transparent information flow are the cornerstones of effective technology-enabled governance. "How do you create a system in which there are guardrails... so that a red flag is raised when it's necessary?" With established metrics and decision-making processes, timely interventions become possible, creating a sense of transparency and legitimacy.

Canon warns against idealizing participation-driven models: "We need to admit that people... don't have sufficient attention available for these systems to function well." She believes the future lies in balancing human understanding, judgment, and timely interventions, enhanced but not replaced by machine intelligence.

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